Publications – Mentis Laboratory

Conference

FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception

Francesco Pessia, Sazzad Sayyed and Francesco Restuccia

Proc. of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)

ABSTRACT BIBTEX HTML


@inproceedings{pessia2025flexrfml,
  title={{FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception}},
  author={Francesco Pessia and Sazzad Sayyed and Francesco Restuccia},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)},
  address={Houston, Texas},
  month={October},
  pages={XXX--XXX},
  year={2025},
  doi={XXX}
}

2025

Conference

AdvO-RAN: Adversarial Deep Reinforcement Learning in AI-Driven Open Radio Access Networks

T. Hassan, F. Meneghello and F. Restuccia

Proc. of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)

ABSTRACT BIBTEX HTML


@inproceedings{hassan2025advoran,
  title={AdvO-RAN: Adversarial Deep Reinforcement Learning in AI-Driven Open Radio Access Networks},
  author={Hassan, T. and Meneghello, F. and Restuccia, F.},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)},
  address={Houston, Texas},
  month={oct},
  pages={XXX--XXX},
  year={2025},
  doi={XXX},
  note={Acceptance rate: 23\%}
}

2025

Conference

SHRINK: Reducing MIMO Feedback Overhead in Wi-Fi with Dynamic Data-Driven Channel Sounding

KM Rumman, F. Meneghello, K. F. Haque, F. Gringoli and F. Restuccia

Proc. of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)

ABSTRACT BIBTEX HTML


@inproceedings{rumman2025shrink,
  title={SHRINK: Reducing MIMO Feedback Overhead in Wi-Fi with Dynamic Data-Driven Channel Sounding},
  author={Rumman, K. M. and Meneghello, Francesca and Haque, Khandaker Foysal and Gringoli, Francesco and Restuccia, Francesco},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc)},
  address={Houston, Texas},
  month={October},
  pages={XXX--XXX},
  year={2025},
  doi={XXX},
  note={Acceptance rate: 23\%}
}

2025

Conference

Breaking the Barrier to Downlink Packet Capture in OFDMA Wi-Fi Networks

S. Fontana, F. Meneghello, J. Link, F. Restuccia, M. Hollick, F. Gringoli,

Proc. of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (ACM WINTECH)

BIBTEX


@inproceedings{fontana2025breaking,
  title={Breaking the Barrier to Downlink Packet Capture in OFDMA Wi-Fi Networks},
  author={Fontana, S. and Meneghello, F. and Link, J. and Restuccia, F. and Hollick, M. and Gringoli, F.},
  booktitle={Proceedings of the 19th ACM Workshop on Wireless Network Testbeds, Experimental evaluation \& Characterization},
  pages={XXX--XXX},
  year={2025},
  note={To appear; Hong Kong, China, November 4--8, 2025; DOI: TBD}
}

2025

Conference

FEARL: AI-Assisted Energy-Aware Real-Time Receiver Adaptation to Dynamic Environments

Mohammad Abdi, Diptashree Das, and Minghan Liu, Marvin Onabajo and Francesco Restuccia

IEEE International Conference on Communications (ICC)

BIBTEX


@inproceedings{abdi2025fearl,
  title={FEARL: AI-Assisted Energy-Aware Real-Time Receiver Adaptation to Dynamic Environments},
  author={Abdi, Mohammad and Das, Diptashree and Liu, Minghan and Onabajo, Marvin and Restuccia, Francesco},
  year={2025},
  organization={IEEE International Conference on Communications (ICC)}
}

2025

Journal

6G Horizons: Toward Intelligent and Sustainable Connectivity

Anwer Al-Dulaimi, Sumei Sun, Francesco Restuccia, Tommy Svensson, Hao Guo

IEEE Vehicular Technology Magazine

BIBTEX HTML


@article{al20256g,
  title={6G Horizons: Toward Intelligent and Sustainable Connectivity [From the Guest Editors]},
  author={Al-Dulaimi, Anwer and Sun, Sumei and Restuccia, Francesco and Svensson, Tommy and Guo, Hao},
  journal={IEEE Vehicular Technology Magazine},
  volume={20},
  number={1},
  pages={40--42},
  year={2025},
  publisher={IEEE}
}

2025

Conference

Radio Fingerprinting of Wi-Fi Devices Through MIMO Compressed Channel Feedback

Francesca Meneghello, Khandaker Foysal Haque and Francesco Restuccia

IEEE Conference on Computer Communications Workshops (INFOCOM WORKSHOPS)

ABSTRACT BIBTEX HTML


@INPROCEEDINGS{11152893,
  author={Meneghello, Francesca and Haque, Khandaker Foysal and Restuccia, Francesco},
  booktitle={IEEE INFOCOM 2025 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, 
  title={Radio Fingerprinting of Wi-Fi Devices Through MIMO Compressed Channel Feedback}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Neural networks;Channel estimation;Fingerprint recognition;Physical layer;Hardware;Reproducibility of results;MIMO;Object recognition;Network interfaces;Wireless fidelity;Radio fingerprinting;Wi-Fi;MIMO;compressed beamforming feedback;channel sounding},
  doi={10.1109/INFOCOMWKSHPS65812.2025.11152893}}

2025

Conference

Digitally Tunable CMOS Mixer Design for Adaptive RF Front-Ends

Diptashree Das, Minghan Liu, Mohammad Abdi, Francesco Restuccia, Marvin Onabajo

IEEE International Symposium on Circuits and Systems (ISCAS) 2025

ABSTRACT BIBTEX HTML


@inproceedings{das2025digitally,
  title={Digitally Tunable CMOS Mixer Design for Adaptive RF Front-Ends},
  author={Das, Diptashree and Liu, Minghan and Abdi, Mohammad and Restuccia, Francesco and Onabajo, Marvin},
  booktitle={2025 IEEE International Symposium on Circuits and Systems (ISCAS)},
  pages={1--5},
  year={2025},
  organization={IEEE}
}

2025

Journal

A Low-Power Differential Temperature Sensor with Chopped Cascode Transistors and Switched-Capacitor Integration

Junyi Yang, Thomas Gourousis, Mengting Yan, Ruyi Ding ,Ankit Mittal, Milin Zhang, Francesco Restuccia, Aatmesh Shrivastava, Yunsi Fei, and Marvin Onabajo

Electronics

ABSTRACT BIBTEX HTML


@article{yang2025low,
  title={A Low-Power Differential Temperature Sensor with Chopped Cascode Transistors and Switched-Capacitor Integration},
  author={Yang, Junyi and Gourousis, Thomas and Yan, Mengting and Ding, Ruyi and Mittal, Ankit and Zhang, Milin and Restuccia, Francesco and Shrivastava, Aatmesh and Fei, Yunsi and Onabajo, Marvin},
  journal={Electronics},
  volume={14},
  number={12},
  pages={2381},
  year={2025},
  publisher={MDPI}
}

2025

Journal

RF Anti-Jamming via Multi-Level Howells-Applebaum Null-Forming: 32-Channels, 5.8 GHz/100 MHz/Beam on Xilinx Sx475T FPGA

Umesha Kumarasiri , Sivakumar Sivasankar , Hasitha Weerasooriya, Hiruni Silva, Chamira Edussooriya , Viduneth Ariyarathna , Francesco Restuccia and Arjuna Madanayake

IEEE Journal of Radio Frequency Identification

ABSTRACT BIBTEX HTML


@article{kumarasiri2025rf,
  title={RF Anti-Jamming via Multi-Level Howells-Applebaum Null-Forming: 32-Channels, 5.8 GHz/100 MHz/Beam on Xilinx Sx475T FPGA},
  author={Kumarasiri, Umesha and Sivasankar, Sivakumar and Weerasooriya, Hasitha and Silva, Hiruni and Edussooriya, Chamira and Ariyarathna, Viduneth and Restuccia, Francesco and Madanayake, Arjuna},
  journal={IEEE Journal of Radio Frequency Identification},
  year={2025},
  publisher={IEEE}
}

2025

Journal

Semantic Edge Computing and Semantic Communications in 6g Networks: A Unifying Survey and Research Challenges

Milin Zhanga, Mohammad Abdia, Venkat R. Dasari and Francesco Restuccia

Computer Networks

ABSTRACT BIBTEX HTML


@article{ZHANG2025111531,
title = {Semantic Edge Computing and Semantic Communications in 6G networks: A unifying survey and research challenges},
journal = {Computer Networks},
volume = {270},
pages = {111531},
year = {2025},
issn = {1389-1286},
doi = {https://doi.org/10.1016/j.comnet.2025.111531},
url = {https://www.sciencedirect.com/science/article/pii/S1389128625004980},
author = {Milin Zhang and Mohammad Abdi and Venkat R. Dasari and Francesco Restuccia},
keywords = {Semantic Edge Computing, Semantic Communication, Split computing, Collaborative intelligence}}

2025

Journal

BASTION: A Framework for Secure Third-Party IP Integration in NoC-based SoC Platforms

Francesco Restuccia, Zhenghua Ma, Joseph Zuckerman, Andres Meza, Biruk Seyoum, Luca Carloni, and Ryan Kastner

IACR Transactions on Cryptographic Hardware and Embedded Systems

ABSTRACT BIBTEX HTML

Modern System-on-Chip (SoC) architectures are a complex mix of processors, accelerators, memories, and I/O controllers interconnected by on-chip communication networks. Given the complexity of the computation and the requirements mandated in modern applications, several of these IPs are often outsourced as third-party modules. The integration of third-party modules, however, has been demonstrated to raise severe system-level security concerns – undiscovered vulnerabilities, incorrect firmware configurations, malicious code, and hardware trojans undetected in such IPs can produce leaks of confidential information and compromise the integrity of critical components. These challenges are further intensified when the communication infrastructure lacks robust mechanisms to supervise and monitor the interactions of third-party IPs with the rest of the system. Thus, runtime monitoring and supervising of third-party IPs is a crucial aspect for the system-level security of the entire SoC – the computing modules integrated in the SoC and their communication must behave securely. This paper presents Bastion, an open-source framework designed to support the secure integration of third-party IP modules into SoC architectures based on network-on-chip (NoC) communications, with a focus on providing robust security guarantees for NoC-based open-source hardware platforms. Unlike most previous works, which either focus on design or verification, we address the challenge of securely integrating third-party IPs in NoC-based platforms through a holistic design and verification framework based on three pillars: (i) a high-performance security socket that can be seamlessly integrated into NoC tiles; (ii) secure configuration and management of the security sockets via a Hardware Root of Trust; and (iii) an ad-hoc property-based security verification framework to ensure secure system operation. Bastion is integrated on the popular open-source ESP framework and validated through simulations and FPGA emulation of realistic SoCs. By explicitly targeting open-source platforms and releasing the entire project as open-source, we aim to democratize access to robustly secure application-specific SoC platforms for critical applications and foster further advancements in this domain.


@article{restuccia2025bastion,
  title={BASTION: A Framework for Secure Third-Party IP Integration in NoC-based SoC Platforms},
  author={Restuccia, Francesco and Ma, Zhenghua and Zuckerman, Joseph and Meza, Andres and Seyoum, Biruk and Carloni, Luca and Kastner, Ryan},
  journal={IACR Transactions on Cryptographic Hardware and Embedded Systems},
  volume={2025},
  number={4},
  pages={317--340},
  year={2025}
}

2025

Conference

FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception

Francesco Pessia, AQM Sazzad Sayyed, and Francesco Restuccia

Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

BIBTEX


@inproceedings{pessia2025flexrfml,
  title={FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception},
  author={Pessia, Francesco and Sayyed, Sazzad and Restuccia, Francesco},
  year={2025},
  organization={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)}
}

2025

Conference

ENCORE: A Neural Collapse Perspective on Out-of-Distribution Detection in Deep Neural Networks

AQM Sazzad Sayyed, Nathaniel D. Bastian and Francesco Restuccia

Proceedings of Winter Conference on Applications of Computer Vision (WACV)

ABSTRACT BIBTEX HTML


@inproceedings{sayyed2026encore,
  title={{ENCORE: A Neural Collapse Perspective on Out-of-Distribution Detection in Deep Neural Networks}},
  author={Sayyed, AQM Sazzad and Bastian, Nathaniel D. and Restuccia, Francesco},
  booktitle={Proceedings of Winter Conference on Applications of Computer Vision (WACV)}
  year={2026}
}

2025

Journal

Finding a Needle in a (Spectrum) Haystack: Multi-Band Multi-Device Radio Fingerprinting

Ildi Alla, Milin Zhang, Jonathan Ashdown, Valeria Loscri, Francesco Restuccia

Preprint

ABSTRACT BIBTEX HTML


@misc{alla2025finding,
  title={Finding a Needle in a (Spectrum) Haystack: Multi-Band Multi-Device Radio Fingerprinting},
  author={Alla, Ildi and Zhang, Milin and Ashdown, Jonathan and Loscri, Valeria and Restuccia, Francesco},
  year={2025}
}

2025

Conference

Resilient Wireless Communications with Selective Deep Neural Network Classification

A. Q. M. Sazzad Sayyed, Jonathan Ashdown, Michael De Lucia, Ananthram Swami, Nathaniel D. Bastian and Francesco Restuccia

Proc. of IEEE Military Communications Conference (MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{sayyed2025selective,
  title={Resilient Wireless Communications with Selective Deep Neural Network Classification},
  author={A. Q. M. Sazzad Sayyed, Jonathan Ashdown, Michael De Lucia, Ananthram Swami,  Nathaniel D. Bastian and Francesco Restuccia},
  booktitle={MILCOM 2025-2025 IEEE Military Communications Conference (MILCOM)},
 year={2025}
}

2025

Conference

NI-Diff: Zero-Day and Adversarial Network Intrusion Detection with Diffusion Models

Milin Zhang, Michael De Lucia, Ananthram Swami, Jonathan Ashdown, Nathaniel D. Bastian and Francesco Restuccia

Proc. of IEEE Military Communications Conference (MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{zhang202nidiff,
  title={NI-Diff: Zero-Day and Adversarial Network Intrusion Detection with Diffusion Models},
  author={Milin Zhang, Michael De Lucia, Ananthram Swami, Jonathan Ashdown, Nathaniel D. Bastian and Francesco},
  booktitle={MILCOM 2025-2025 IEEE Military Communications Conference (MILCOM)},
}

2025

Conference

Method for Multi-Antenna Networking Through Multi-path Feedback Extraction and Reporting

Francesco Restuccia, Arman Elyasi, Francesca Meneghello

Technical Report

ABSTRACT BIBTEX HTML


@inproceedings{elyasi2025multipath,
  title={{Method for Multi-Antenna Networking Through Multi-path Feedback Extraction and Reporting}},
  author={Arman Elyasi and Francesca Meneghello and Francesco Restuccia},
  booktitle={Technical Report},
  year={2025}
}

2025

Conference

SINF: Semantic Inference in Neural Networks with Semantic Subgraphs

AQM Sazzad Sayyed, Francesco Restuccia

ICML 2025 Workshop on Machine Learning for Wireless Communication and Networks (ML4Wireless)

ABSTRACT BIBTEX HTML


@inproceedings{sayyed2025sinf,
  title={SINF: Semantic Inference in Neural Networks with Semantic Subgraphs},
  author={Sayyed, AQM Sazzad and Restuccia, Francesco},
  booktitle={ICML 2025 Workshop on Machine Learning for Wireless Communication and Networks (ML4Wireless)}
}

2025

Conference

Semantic Subgraph Extraction Attacks To Convolutional Neural Networks

Gabriele Restuccia, Ilenia Tinnirello, Francesco Restuccia

ICML 2025 Workshop on Machine Learning for Wireless Communication and Networks (ML4Wireless)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2025semantic,
  title={Semantic Subgraph Extraction Attacks To Convolutional Neural Networks},
  author={Restuccia, Gabriele and Tinnirello, Ilenia and Restuccia, Francesco},
  booktitle={ICML 2025 Workshop on Machine Learning for Wireless Communication and Networks (ML4Wireless)}
}

2025

Journal

Out-of-Distribution Detection in Computer Vision: A Comprehensive Survey and Research Challenges

AMQ Sazzad Sayyed, Shahriar Rifat, Milin Zhang, Ananthram Swami, Michael De Lucia, Nathaniel D. Bastian, Francesco Restuccia

ACM Computer Surveys (Under Review)

ABSTRACT BIBTEX HTML


@article{sayyed2025ood,
  author = {AMQ Sazzad Sayyed and Shahriar Rifat and Milin Zhang and Ananthram Swami and Michael De Lucia and Nathaniel D. Bastian and Francesco Restuccia},
  title = {{Out-of-Distribution Detection in Computer Vision: A Comprehensive Survey and Research Challenges}},
  year = {2025}
}

2025

Journal

DECOR: Multi-Modal Decentralized Cluster-based Energy Efficient Covert Routing in HetNets

K. F. Haque , J. H. Kong , T. J. Moore , K. Chan , F. Restuccia , and F. T. Dagefu

IEEE (Under Review)

ABSTRACT BIBTEX HTML


@article{haque_decor2025,
  author = {Haque, Khandaker Foysal and Kong, Justin and Moore, Terrence J and Chen, Kevin and Restuccia, Francesco and Dagefu, Fikadu T},
  title = {DECOR: Multi-Modal Decentralized Cluster-based Energy Efficient Covert Routing in HetNets},
  journal = {mentis.info},
  year = {2025}
}

2025

Journal

How to Poison an xApp: Dissecting Backdoor Attacks to Deep Reinforcement Learning in Open Radio Access Networks

Andrea Lacava, Stefano Maxenti, Leonardo Bonati, Salvatore D’Oro, Alina Oprea, Tommaso Melodia, Francesco Restuccia

Computer Networks (Under Review)

ABSTRACT BIBTEX HTML


@article{lacava2025how,
  author = {Andrea Lacava and Stefano Maxenti and Leonardo Bonati and Salvatore D’Oro and Alina Oprea and Tommaso Melodia and Francesco Restuccia},
  title = {{How to Poison an xApp: Dissecting Backdoor Attacks to Deep Reinforcement Learning in Open Radio Access Networks}},
  year = {2025}
}

2025

Journal

SCOPE: Cooperative Integrated Communications and Sensing for Material Classification at Sub-Terahertz Frequencies

K. F. Haque, X. Cantos-Roman, F. Meneghello, J. M. Jornet and F. Restuccia

IEEE Wireless Communications Letters

ABSTRACT BIBTEX HTML


@article{haque2025scope,
  author    = {Haque, Khandaker Foysal and Cantos-Roman, Xavier and Meneghello, Francesca and Jornet, Josep Miquel and Restuccia, Francesco},
  title     = {SCOPE: Cooperative Integrated Communications and Sensing for Material Classification at Sub-Terahertz Frequencies},
  journal   = {IEEE Wireless Communications Letters},
  year      = {2025},
  doi       = {10.1109/LWC.2025.3576010},
  publisher = {IEEE}
}

2025

Conference

MAGIC: Meta-Learning Adaptive Gesture Recognition with mmWave MIMO CSI

K. F. Haque, K. M. Rumman, A. Elyasi, F. Meneghello, F. Restuccia

Proc. of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM)

ABSTRACT BIBTEX HTML


@article{haque2025magic,
  title={{MAGIC: Meta-Learning Adaptive Gesture Recognition with mmWave MIMO CSI}},
  author={Haque, Khandaker Foysal and Rumman, K M  and Elyasi, Arman and Meneghello, Francesca and Restuccia, Francesco},
  journal={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM)},
  year={2025}
}

2025

Conference

Metacognitive Artificial Intelligence in Vision Foundation Models: Research Challenges

S. Rifat, A.Q.M. S. Sayyed, N. D. Bastian, and F. Restuccia

Workshop on Metacognitive Prediction of AI Behavior at SDM 2025

ABSTRACT BIBTEX HTML


@article{rifat2025metacognitive,
	author = {Shahriar Rifat and AMQ Sazzad Sayyed and Nathaniel D. Bastian and Francesco Restuccia},
	journal = {Workshop on Metacognitive Prediction of AI Behavior (METACOG)},
	title = {{Metacognitive Artificial Intelligence in Vision Foundation Models: Research Challenges}},
	year = 2025
}

2025

Conference

SOAR: Semantic Multi-User MIMO Communicationsfor Reliable Wireless Edge Computing

S. L.G. Contreras, K. F. Haque, F. Restuccia, and M. Levorato

Proc. of International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

ABSTRACT BIBTEX HTML


@article{haque2025magic,
  title={{SOAR: Semantic Multi-User MIMO Communicationsfor Reliable Wireless Edge Computing}},
  author={Contreras, Sharon L.G. and Haque, Khandaker Foysal and Restuccia, Francesco and Levorato, Marco },
  journal={2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)},
  year={2025}
}

2025

Conference

DEER: Simultaneous Multi-Modal Decentralized Energy Efficient Covert Routing

K. F. Haque, J. Kong, T. J. Moore, F. Restuccia, F. T. Dagefu

Proc. of IEEE Wireless Communications and Networking Conference (WCNC)

ABSTRACT BIBTEX HTML


@inproceedings{haque2025deer,
  title={DEER: Simultaneous Multi-Modal Decentralized Energy Efficient Covert Routing},
  author={Haque, Khandaker Foysal and Kong, Justin and Moore, Terrence J and Restuccia, Francesco and Dagefu, Fikadu T},
  booktitle={2025 IEEE Wireless Communications and Networking Conference (WCNC)},
  pages={1--6},
  year={2025},
  organization={IEEE}
}

2025

Journal

O-RAN xApps: Survey and Research Challenges

Arman Elyasi, Andrew Ashdown, K M Rumman, Francesco Restuccia

ABSTRACT BIBTEX HTML


@article{Elyasi_xApps,
  author = {Arman Elyasi and Andrew Ashdown and K M Rumman and Francesco Restuccia},
  title = {O-RAN xApps: Survey and Research Challenges},
  journal = {Computer Networks},
  year = {2025}
}

2025

Conference

DARDA: Domain-Aware Real-Time Dynamic Neural Network Adaptation

S. Rifat , J. Ashdown and F. Restuccia

Proc. of IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ABSTRACT BIBTEX HTML


@article{rifat2025darda,
  title={DARDA: Domain-Aware Real-Time Dynamic Neural Network Adaptation},
  author={Rifat, Shahriar and Ashdown, Jonathan and Restuccia, Francesco},
  journal={Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2025}
}

2025

Journal

BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback

K. F. Haque, M. Zhang, F. Meneghello, and F. Restuccia

Computer Networks

ABSTRACT BIBTEX HTML


@article{haque2025beamsense,
  title={Beamsense: Rethinking wireless sensing with mu-mimo wi-fi beamforming feedback},
  author={Haque, Khandaker Foysal and Zhang, Milin and Meneghello, Francesca and Restuccia, Francesco},
  journal={Computer Networks},
  volume={258},
  pages={111020},
  year={2025},
  publisher={Elsevier}
}

2025

Conference

On the Adversarial Vulnerability of Label-Free Test-Time Adaptation

S. Rifat, J. Ashdown, M. J. De Lucia, A. Swami, F. Restuccia

Proc. of International Conference on Learning Representations (ICLR)

ABSTRACT BIBTEX HTML


@article{rifat2025on,
	author = {Shahriar Rifat and Jonathan Ashdown and Michael J. De Lucia and Ananthram Swami and Francesco Restuccia },
	journal = {Proceedings of International Conference on Learning Representations (ICLR)},
	title = {{On the Adversarial Vulnerability of Label-Free Test-Time Adaptation}},
	year = 2025
}

2025

Conference

PhyDNNs: Bringing Deep Neural Networks to the Physical Layer

M. Abdi and K. F. Haque and F. Meneghello and J. Ashdown and F. Restuccia

Proc. of IEEE International Conference on Computer Communications (IEEE INFOCOM)

ABSTRACT BIBTEX HTML


@article{abdi2025phydnns,
	author = {Mohammad Abdi and Khandaker Foysal Haque and Francesca Meneghello and Jonathan Ashdown and Francesco Restuccia},
	journal = {Proceedings of IEEE International Conference on Computer Communications (INFOCOM)},
	title = {{PhyDNNs: Bringing Deep Neural Networks to the Physical Layer}},
	year = 2025
}

2025

Conference

How to BREAK MU-MIMO Precoding in IEEE 802.11 Wi-Fi Networks

F. Meneghello, F. Gringoli, M. Cominelli, M. Rossi and F. Restuccia

Proc. of IEEE International Conference on Computer Communications (IEEE INFOCOM) — Best Paper Award

ABSTRACT BIBTEX HTML


@article{meneghello2025how,
	author = {Meneghello, Francesca and Gringoli, Francesco and Cominelli, Marco and Rossi, Michele and Restuccia, Francesco},
	journal = {Proceedings of IEEE International Conference on Computer Communications (INFOCOM)},
	title = {{How to BREAK MU-MIMO Precoding in IEEE 802.11 Wi-Fi Networks}},
	year = 2025
}

2025

Conference

m3MIMO: An 8×8mmWave Multi-User MIMO Testbed for Wireless Research

K. F. Haque , F. Meneghello, K. M. Rumman and F. Restuccia

Proc. of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (ACM WINTECH)

ABSTRACT BIBTEX HTML


@inproceedings{haque2024m3mimo,
  title={m3MIMO: An 8$\times$ 8 mmWave Multi-User MIMO Testbed for Wireless Research},
  author={Haque, Khandaker Foysal and Meneghello, Francesca and Rumman, KM and Restuccia, Francesco},
  booktitle={Proceedings of the 30th Annual International Conference on Mobile Computing and Networking},
  pages={1922--1929},
  year={2024}
}

2024

Conference

HyperAdv: Dynamic Defense Against Adversarial Radio Frequency Machine Learning Systems

M. Zhang , M. De Lucia, A. Swami, J. Ashdown, K. Turck and F. Restuccia

Proc. of IEEE Military Communications Conference (IEEE MILCOM)

2024

Conference

Blind Spectrum Sensing for Dynamic Spectrum Awareness in Next G Open Radio Access Networks

A. Ashdown , J. Kelly, J. Ashdown, I. Kurzrock, C. Turck, and F. Restuccia

Proc. of IEEE Military Communications Conference (IEEE MILCOM)

2024

Conference

ADA: Adversarial Dynamic Test Time Adaptation in Radio Frequency Machine Learning Systems

S. Rifat, M. De Lucia, A. Swami, J. Ashdown, K. Turck, F. Restuccia

Proc. of IEEE Military Communications Conference (IEEE MILCOM)

2024

Conference

Integrated Sensing and Communication for Efficient Edge Computing

K. F. Haque , F. Meneghello and F. Restuccia

Proc. of International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

ABSTRACT BIBTEX HTML


@INPROCEEDINGS{10770523,

  author={Haque, Khandaker Foysal and Meneghello, Francesca and Restuccia, Francesco},

  booktitle={2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)}, 

  title={Integrated Sensing and Communication for Efficient Edge Computing}, 

  year={2024},

  volume={},

  number={},

  pages={611-614},

  keywords={Wireless communication;Location awareness;Performance evaluation;Wireless sensor networks;Computer vision;Image edge detection;Virtual reality;Reproducibility of results;Servers;Edge computing},

  doi={10.1109/WiMob61911.2024.10770523}}

2024

Conference

Resilience of Entropy Model in Distributed Neural Networks

M. Zhang, M. Abdi, S. Rifat, F. Restuccia

Proc. of European Conference on Computer Vision (ECCV)

ABSTRACT BIBTEX HTML


@article{zhang2024resilience,
  title={Resilience of Entropy Model in Distributed Neural Networks},
  author={Zhang, Milin and Abdi, Mohammad and Rifat, Shahriar and Restuccia, Francesco},
  journal={arXiv preprint arXiv:2403.00942},
  year={2024}
}

2024

Journal

Evaluating the Impact of Channel Feedback Quantization and Grouping in IEEE 802.11 MIMO Wi-Fi Networks

F. Meneghello , K. F. Haque and F. Restuccia

IEEE Wireless Communications Letters

ABSTRACT BIBTEX HTML


@article{meneghello2024evaluating,
  title={Evaluating the Impact of Channel Feedback Quantization and Grouping in IEEE 802.11 MIMO Wi-Fi Networks},
  author={Meneghello, Francesca and Haque, Khandaker Foysal and Restuccia, Francesco},
  journal={IEEE Wireless Communications Letters},
  year={2024},
  publisher={IEEE}
}

2024

Conference

FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated Learning in LoRa Networks

F. Busacca, S. Mangione, G. Neglia, I. Tinnirello, S. Palazzo, F. Restuccia

IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)

ABSTRACT BIBTEX HTML


@inproceedings{busacca2024fedlora,
  title={FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated Learning in LoRa Networks},
  author={Busacca, Fabio and Mangione, Stefano and Neglia, Giovanni and Tinnirello, Ilenia and Palazzo, Sergio and Restuccia, Francesco},
  booktitle={2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS)},
  pages={295--303},
  year={2024},
  organization={IEEE}
}

2024

Journal

WHACK: Adversarial Beamforming in MU-MIMO Through Compressed Feedback Poisoning

F. Meneghello, F. Restuccia, M. Rossi

IEEE Transactions on Wireless Communications

ABSTRACT BIBTEX HTML


@article{meneghello2024whack,
  title={WHACK: Adversarial Beamforming in MU-MIMO Through Compressed Feedback Poisoning},
  author={Meneghello, Francesca and Restuccia, Francesco and Rossi, Michele},
  journal={IEEE Transactions on Wireless Communications},
  year={2024},
  publisher={IEEE}
}

2024

Conference

A Digitally Reconfigurable Low-Noise Amplifier with Robust Input Impedance for Machine Learning-Based Receiver Optimizations

M. Liu, D. Das, M. Abdi, F. Restuccia, M. Onabajo

IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)

ABSTRACT BIBTEX HTML


@inproceedings{liu2024digitally,
  title={A Digitally Reconfigurable Low-Noise Amplifier with Robust Input Impedance for Machine Learning-Based Receiver Optimizations},
  author={Liu, Minghan and Das, Diptashree and Abdi, Mohammad and Restuccia, Francesco and Onabajo, Marvin},
  booktitle={2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS)},
  pages={392--396},
  year={2024},
  organization={IEEE}
}

2024

Conference

OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge

C. Puligheddu, N. Varshney, T. Hassan, J. Ashdown, F. Restuccia, C. Fabiana Chiasserini

IEEE 44th International Conference on Distributed Computing Systems (ICDCS)

ABSTRACT BIBTEX HTML


@inproceedings{puligheddu2024offloadnn,
  title={OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge},
  author={Puligheddu, Corrado and Varshney, Nancy and Hassan, Tanzil and Ashdown, Jonathan and Restuccia, Francesco and Chiasserini, Carla Fabiana},
  booktitle={2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)},
  pages={624--634},
  year={2024},
  organization={IEEE}
}

2024

Conference

Poster: Transport-Aware Resource Block Allocation in 5G Slicing

A. Pinto, TB Hassan, F. Restuccia, F. Esposito

IEEE 10th International Conference on Network Softwarization (NetSoft)

ABSTRACT BIBTEX HTML


@inproceedings{pinto2024poster,
  title={Poster: Transport-Aware Resource Block Allocation in 5G Slicing},
  author={Pinto, Andrea and Hassan, Tanzil Bin and Restuccia, Francesco and Esposito, Flavio},
  booktitle={2024 IEEE 10th International Conference on Network Softwarization (NetSoft)},
  pages={322--323},
  year={2024},
  organization={IEEE}
}

2024

Patent

Private 5G Cellular Connectivity as a Service Through Full-Stack Wireless Steganographyation

T. Melodia, L. Bonati, S. D’Oro and F. Restuccia

U.S. Patent No.: 17/316,773

HTML

2024

Patent

Methods for Multi-Access Edge Computing Network Slicing in 5G Networks

S. D’Oro, T. Melodia, F. Restuccia and L. Bonati

U.S. Patent No.: 17/365,070

HTML

2024

Conference

Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal Stitching

D. Uvaydov, M. Zhang, CP. Robinson, S. D’Oro, T. Melodia, F. Restuccia

Proc. of IEEE International Conference on Computer Communications (INFOCOM).

ABSTRACT BIBTEX HTML


@article{uvaydov2024stitching,
  title={Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal},
  author={Uvaydov, Daniel and Zhang, Milin and Robinson, Clifton Paul and D'Oro, Salvatore and Melodia, Tommaso and Restuccia, Francesco},
  journal={arXiv preprint arXiv:2402.03465},
  year={2024}
}

2024

Conference

RF-SoC Platforms for RF-AI Spectrum Perception

H. Weerasooriya, G. Rathnasekara, F. Restuccia, A. Madanayake

International Applied Computational Electromagnetics Society Symposium (ACES)

ABSTRACT BIBTEX HTML


@inproceedings{weerasooriya2024rf,
  title={RF-SoC Platforms for RF-AI Spectrum Perception},
  author={Weerasooriya, Hasitha and Rathnasekara, Gayani and Restuccia, Francesco and Madanayake, Arjuna},
  booktitle={2024 International Applied Computational Electromagnetics Society Symposium (ACES)},
  pages={1--2},
  year={2024},
  organization={IEEE}
}

2024

Journal

Toward Wireless System and Circuit Co-Design for the Internet of Self-Adaptive Things

D. Das, M. Abdi, M. Liu, M. Onabajo, F. Restuccia

IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

ABSTRACT BIBTEX HTML


@inproceedings{das2024toward,
  title={Toward Wireless System and Circuit Co-Design for the Internet of Self-Adaptive Things},
  author={Das, Diptashree and Abdi, Mohammad and Liu, Minghan and Onabajo, Marvin and Restuccia, Francesco},
  booktitle={2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
  pages={540--547},
  year={2024},
  organization={IEEE}
}

2024

Conference

Det-RAN: Data-driven cross-layer real-time attack detection in 5G open RANs

A. Scalingi, S. D’Oro, F. Restuccia, T. Melodia, D. Giustiniano

Proc. of IEEE International Conference on Computer Communications (INFOCOM).

ABSTRACT BIBTEX HTML


@inproceedings{scalingi2024det,
  title={Det-RAN: Data-driven cross-layer real-time attack detection in 5G open RANs},
  author={Scalingi, Alessio and D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso and Giustiniano, Domenico},
  booktitle={IEEE INFOCOM 2024-IEEE Conference on Computer Communications},
  pages={41--50},
  year={2024},
  organization={IEEE}
}

2024

Patent

Embedded networked deep learning for implanted medical devices

D. Uvaydov, R. Guida, F. Restuccia, T. Melodia

U.S. Patent No.: 17/176,229

HTML

2024

Journal

Sub-6 GHz Energy Detection-Based Fast On-Chip Analog Spectrum Sensing with Learning-Driven Signal Classification

A. Mittal, M. Zhang, T. Gourousis, Z. Zhang, Y. Fei, M. Onabajo, F. Restuccia, A. Shrivastava

IEEE Internet of Things Journal

ABSTRACT BIBTEX HTML


@article{mittal2024sub,
  title={Sub-6 GHz Energy Detection-Based Fast On-Chip Analog Spectrum Sensing with Learning-Driven Signal Classification},
  author={Mittal, Ankit and Zhang, Milin and Gourousis, Thomas and Zhang, Ziyue and Fei, Yunsi and Onabajo, Marvin and Restuccia, Francesco and Shrivastava, Aatmesh},
  journal={IEEE Internet of Things Journal},
  year={2024},
  publisher={IEEE}
}

2024

Conference

BFA-Sense: Learning Beamforming Feedback Angles for Wi-Fi Sensing

K. Foysal Haque, F. Meneghello, and F. Restuccia

IEEE International Conference on Pervasive Computing and Communications Workshops

ABSTRACT BIBTEX HTML


@inproceedings{haque2024bfa,
  title={BFA-Sense: Learning Beamforming Feedback Angles for Wi-Fi Sensing},
  author={Haque, Khandaker Foysal and Meneghello, Francesca and Restuccia, Francesco},
  booktitle={2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
  pages={575--580},
  year={2024},
  organization={IEEE}
}

2024

Patent

Methods for the enforcement of network slicing policies in virtualized cellular networks

DORO. Salvatore, F. Restuccia, T. Melodia

U.S. Patent No.: 17/424,522

HTML

2024

Patent

Deep learning-based polymorphic platform

T. Melodia, F. Restuccia

U.S. Patent No.: 17/604,476

HTML

2024

Conference

AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

T. Benz, A. Ottaviano, R. Balas, A. Garofalo, F. Restuccia, A. Biondi, L. Benini

2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)

ABSTRACT BIBTEX HTML


@inproceedings{benz2024axi,
  title={AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs},
  author={Benz, Thomas and Ottaviano, Alessandro and Balas, Robert and Garofalo, Angelo and Restuccia, Francesco and Biondi, Alessandro and Benini, Luca},
  booktitle={2024 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
  pages={1--6},
  year={2024},
  organization={IEEE}
}

2024

Journal

SDR-LoRa, an open-source, full-fledged implementation of LoRa on Software-Defined Radios: Design and potential exploitation

F. Busacca, S. Mangione, S. Palazzo, F. Restuccia, and I. Tinnirello

Computer Networks (COMNET)

ABSTRACT BIBTEX


@article{busacca2024sdr,
  title={SDR-LoRa, an open-source, full-fledged implementation of LoRa on Software-Defined-Radios: Design and potential exploitation},
  author={Busacca, Fabio and Mangione, Stefano and Palazzo, Sergio and Restuccia, Francesco and Tinnirello, Ilenia},
  journal={Computer Networks},
  volume={241},
  pages={110194},
  year={2024},
  publisher={Elsevier}
}

2024

Journal

Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

K. Wu, J. Pegoraro, F. Meneghello, JA. Zhang, JO. Lacruz, J. Widmer, F. Restuccia, M. Rossi, X. Huang, D. Zhang, G. Caire, Y. Guo

IEEE Signal Processing Magazine (IEEE SPM)

ABSTRACT BIBTEX HTML


@article{wu2024sensing,
  title={Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective},
  author={Wu, Kai and Pegoraro, Jacopo and Meneghello, Francesca and Zhang, J Andrew and Lacruz, Jesus O and Widmer, Joerg and Restuccia, Francesco and Rossi, Michele and Huang, Xiaojing and Zhang, Daqing and others},
  journal={arXiv preprint arXiv:2402.09048},
  year={2024}
}

2024

Conference

Federated Learning with Packet Losses

A. Rodio, G. Neglia, F. Busacca, S. Mangione, S. Palazzo, I. Tinnirello

Proc. of the International Symposium On Wireless Personal Multimedia Communications (WPMC)

ABSTRACT BIBTEX HTML


@inproceedings{rodio2023federated,
  title={Federated Learning with Packet Losses},
  author={Rodio, Angelo and Neglia, Giovanni and Busacca, Fabio and Mangione, Stefano and Palazzo, Sergio and Restuccia, Francesco and Tinnirello, Ilenia},
  booktitle={2023 26th International Symposium on Wireless Personal Multimedia Communications (WPMC)},
  pages={1--6},
  year={2023},
  organization={IEEE}
}

2023

Conference

Zero-Shot Dynamic Neural Network Adaptation in Tactical Wireless Systems

Shahriar Rifat, J. Ashdown, K. Turck, and F. Restuccia

Proc. of IEEE Military Communications Conference (IEEE MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{rifat2023zero,
  title={Zero-Shot Dynamic Neural Network Adaptation in Tactical Wireless Systems},
  author={Rifat, Shahriar and Ashdown, Jonathan and Turck, Kurt and Restuccia, Francesco},
  booktitle={MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM)},
  pages={418--423},
  year={2023},
  organization={IEEE}
}

2023

Conference

Channel-Adaptive Dynamic Neural Networks for Low-Complexity Distributed Signal Analysis

M. Abdi, J. Ashdown, K. Turck, and F. Restuccia

Proc. of IEEE Military Communications Conference (IEEE MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{abdi2023channel,
  title={Channel-Adaptive Dynamic Neural Networks for Low-Complexity Distributed Signal Awareness},
  author={Abdi, Mohammad and Ashdown, Jonathan and Turck, Kurt and Restuccia, Francesco},
  booktitle={MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM)},
  pages={27--32},
  year={2023},
  organization={IEEE}
}

2023

Conference

Hercules: An Emulation-Based Framework for Transport Layer Measurements over 5G Wireless Networks

A. Pinto, A. Ashdown, T. B. Hassan, H. Cheng, F. Esposito, L. Bonati, S. D’Oro, T. Melodia, F. Restuccia

Proceedings of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (ACM WINTECH)

ABSTRACT BIBTEX HTML


@inproceedings{pinto2023hercules,
  title={{Hercules: An Emulation-Based Framework for Transport Layer Measurements over 5G Wireless Networks}},
  author={Pinto, Andrea and Ashdown, Andrew and Bin Hassan, Tanzil and Cheng, Hai and Esposito, Flavio and Bonati, Leonardo and D'Oro, Salvatore and Melodia, Tommaso and Restuccia, Francesco},
  booktitle={Proceedings of the ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization},
  pages={72--79},
  year={2023}
}

2023

Conference

Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices

K. Foysal Haque, F. Meneghello, and F. Restuccia

Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (ACM WINTECH)

ABSTRACT BIBTEX HTML


@inproceedings{haque2023wi,
  title={Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices},
  author={Haque, Khandaker Foysal and Meneghello, Francesca and Restuccia, Francesco},
  booktitle={Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation \& Characterization},
  pages={104--111},
  year={2023}
}

2023

Conference

Identification of Stealthy Hardware Trojans through On-Chip Temperature Sensing and an Autoencoder-Based Machine Learning Algorithm

T. Gourousis, Z. Zhang, M. Yan, M. Zhang, A. Mittal, A. Shrivastava, F. Restuccia, Y. Fei, M. Onabajo

Proc. of IEEE International Midwest Symposium on Circuits and Systems (IEEE MWSCAS)

ABSTRACT BIBTEX HTML


@inproceedings{gourousis2023identification,
  title={Identification of Stealthy Hardware Trojans through On-Chip Temperature Sensing and an Autoencoder-Based Machine Learning Algorithm},
  author={Gourousis, Thomas and Zhang, Ziyue and Yan, Mengting and Zhang, Millin and Mittal, Ankit and Shrivastava, Aatmesh and Restuccia, Francesco and Fei, Yunsi and Onabajo, Marvin},
  booktitle={2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)},
  pages={30--34},
  year={2023},
  organization={IEEE}
}

2023

Patent

Neural Network for Adversarial Deep Learning in Wireless Systems

F. Restuccia, S. D’Oro and T. Melodia

U.S. Patent No.: 17/128,437

HTML

2023

Conference

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing

N. Bahadori, Y. Matsubara, M. Levorato and F. Restuccia

Proc. of the IEEE International Conference on Distributed Computing Systems (IEEE ICDCS)

ABSTRACT BIBTEX HTML

Modern IEEE 802.11 (Wi-Fi) networks extensively rely on multiple-input multiple-output (MIMO) to significantly improve throughput. To correctly beamform MIMO transmissions, the access point needs to frequently acquire a beamforming matrix (BM) from each connected station. However, the size of the matrix grows with the number of antennas and subcarriers, resulting in an increasing amount of airtime overhead and computational load at the station. Conventional approaches come with either excessive computational load or loss of beamforming precision. For this reason, we propose SplitBeam, a new framework where we train a split deep neural network (DNN) to directly output the BM given the channel state information (CSI) matrix as input. The DNN is designed with an additional “bottleneck” layer to “split” the original DNN into a head model and a tail model, respectively executed by the station and the access point. The head model generates a compressed representation of the BM, which is then used by the AP to produce the BM using the tail model. We formulate and solve a bottleneck optimization problem (BOP) to keep computation, airtime overhead, and bit error rate (BER) below application requirements. We perform extensive experimental CSI collection with off-the-shelf Wi-Fi devices in two distinct environments and compare the performance of SplitBeam with the standard IEEE 802.11 algorithm for BM feedback and the state-of-the-art DNN-based approach LB-SciFi. Our experimental results show that SplitBeam reduces the beamforming feedback size and computational complexity by respectively up to 81 % and 84 % while maintaining BER within about 10 −3 of existing approaches. We also implement the SplitBeam DNNs on FPGA hardware to estimate the end-to-end BM reporting delay, and show that the latter is less than 10 milliseconds in the most complex scenario, which is the target channel sounding frequency in realistic multi-user MIMO scenarios. To allow full reproducibility, we will release our code and datasets to the community.


@inproceedings{baadori2023splitbeam,
  title={{SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing}},
  author={Bahadori, Niloofar and Matsubara, Yoshitomo and Levorato, Marco and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS)},
  year={2023},
}

2023

Journal

Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks

F. Meneghello, C. Chen, C. Cordeiro, and F. Restuccia

IEEE Communications Magazine

ABSTRACT BIBTEX HTML


@article{meneghello2023toward,
  title={{Toward Integrated Sensing and Communications in IEEE 802.11 bf Wi-Fi Networks}},
  author={Meneghello, Francesca and Chen, Cheng and Cordeiro, Carlos and Restuccia, Francesco},
  journal={IEEE Communications Magazine},
  volume={61},
  number={7},
  pages={128--133},
  year={2023},
  publisher={IEEE}
}

2023

Conference

Shot Boundary Detection and Convolutional Neural Network for Video Classification

F. Candela, A. Giordano, M. Campolo, F. Restuccia and F. C. Morabito

Proceedings of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023)

ABSTRACT BIBTEX HTML


@inproceedings{candela2023shot,
  title={{Shot Boundary Detection and Convolutional Neural Network for Video Classification}},
  author={Candela, Federico and Giordano, Angelo and Campolo, Maurizio and Restuccia, Francesco and Morabito, Francesco Carlo},
  booktitle={Proceedings of International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

2023

Conference

SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

K. Foysal Haque, M. Zhang and F. Restuccia

Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

ABSTRACT BIBTEX HTML


@article{haque2023simwisense,
  title={{SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals}},
  author={Haque, Khandaker Foysal and Zhang, Milin and Restuccia, Francesco},
  journal={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM)},
  year={2023}
}

2023

Conference

Edge-V: Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands

F. Raviglione, C. Casetti and F. Restuccia

Proceedings of IEEE Vehicular Technology Conference (IEEE VTC2023-Spring)

ABSTRACT BIBTEX HTML


@inproceedings{raviglione2023edge,
  title={{Edge-V: Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands}},
  author={Raviglione, Francesco and Casetti, Claudio and Restuccia, Francesco},
  booktitle={Proceedings of IEEE Vehicular Technology Conference (IEEE VTC2023-Spring)},
  pages={1--5},
  year={2023},
  organization={IEEE}
}

2023

Conference

Mobile RF Scenario Design for Massive-Scale Wireless Channel Emulators

R. Rusca, F. Raviglione, C. Casetti, P. Giaccone, F. Restuccia

Proceedings of Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

ABSTRACT BIBTEX HTML


@inproceedings{rusca2023mobile,
  title={{Mobile RF Scenario Design for Massive-Scale Wireless Channel Emulators}},
  author={Rusca, Riccardo and Raviglione, Francesco and Casetti, Claudio and Giaccone, Paolo and Restuccia, Francesco},
  booktitle={Proceedings of Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)},
  pages={675--680},
  year={2023},
  organization={IEEE}
}

2023

Conference

A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems

A. Coletta, F. Giorgi, G. Maselli, M. Prata, D. Silvestri, J. Ashdown, and F. Restuccia

Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM)

ABSTRACT BIBTEX HTML


@article{coletta20232,
  title={{A}$^{2}${-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems}},
  author={Coletta, Andrea and Giorgi, Flavio and Maselli, Gaia and Prata, Matteo and Silvestri, Domenicomichele and Ashdown, Jonathan and Restuccia, Francesco},
  journal={Proceedings of IEEE International Conference on Computer Communications (IEEE INFOCOM)},
  year={2023}
}

2023

Conference

SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems

C. Puligheddu, J. Ashdown, C. F. Chiasserini, and F. Restuccia

Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM)

ABSTRACT BIBTEX HTML


@inproceedings{puligheddu2023sem,
  title={{SEM-O-RAN: Semantic and Flexible O-RAN Slicing for NextG Edge-Assisted Mobile Systems}},
  author={Puligheddu, Corrado and Ashdown, Jonathan and Chiasserini, Carla Fabiana and Restuccia, Francesco},
  booktitle={Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM)},
  pages={1--10},
  year={2023},
  organization={IEEE}
}

2023

Patent

Real-Time Cognitive Wireless Networking Through Deep Learning in Transmission and Reception Communication Paths

F. Restuccia and T. Melodia

U.S. Patent No.: 11,610,111

HTML

2023

Patent

Device and Method for Reliable Classification of Wireless Signals

S. D’Oro, F. Restuccia, and T. Melodia

U.S. Patent No.: 11,611,457

HTML

2023

Journal

Deep Learning at the Physical Layer for Adaptive Terahertz Communications

J. Hall, J.M. Jornet, N. Thawdar, T. Melodia and F. Restuccia

IEEE Transactions on Terahertz Science and Technology

ABSTRACT BIBTEX HTML


@article{hall2023deep,
  title={{Deep Learning at the Physical Layer for Adaptive Terahertz Communications}},
  author={Hall, Jacob and Jornet, Josep Miquel and Thawdar, Ngwe and Melodia, Tommaso and Restuccia, Francesco},
  journal={IEEE Transactions on Terahertz Science and Technology},
  volume={13},
  number={2},
  pages={102--112},
  year={2023},
  publisher={IEEE}
}

2023

Conference

Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations

M. Cominelli, F. Gringoli and F. Restuccia

Proceedings of IEEE Conference on Pervasive Computing and Communications (IEEE PerCom)

ABSTRACT BIBTEX HTML


@inproceedings{cominelli2023exposing,
  title={{Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations}},
  author={Cominelli, Marco and Gringoli, Francesco and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom)},
  pages={81--90},
  year={2023},
  organization={IEEE}
}

2023

Patent

Coordination-Free mmWave Beam Management With Deep Waveform Learning

M. Polese, F. Restuccia and T. Melodia

U.S. Patent No.: 11,588,539

HTML

2023

Patent

Underwater Ultrasonic Communication System and Method

F. Restuccia, E. Demirors, and T. Melodia

U.S. Patent No.: 11,575,987

HTML

2023

Journal

Wi-Fi Sensing Based on IEEE 802.11bf

C. Chen, H. Song, Q. Li, F. Meneghello, F. Restuccia and C. Cordeiro

IEEE Communications Magazine

ABSTRACT BIBTEX HTML


@article{chen2022wi,
  title={{Wi-Fi sensing based on IEEE 802.11 bf}},
  author={Chen, Cheng and Song, Hao and Li, Qinghua and Meneghello, Francesca and Restuccia, Francesco and Cordeiro, Carlos},
  journal={IEEE Communications Magazine},
  volume={61},
  number={1},
  pages={121--127},
  year={2022},
  publisher={IEEE}
}

2023

Journal

IEEE 802.11 Artificial Intelligence Machine Learning (AIML) Technical Interest Group

F. Restuccia

IEEE Communications Standards Magazine

ABSTRACT BIBTEX HTML


@ARTICLE{10034506,
  author={Francesco Restuccia},
  journal={IEEE Communications Standards Magazine}, 
  title={{IEEE 802.11 Artificial Intelligence Machine Learning (AIML) Technical Interest Group (in Standards News)}}, 
  year={2022},
  volume={6},
  number={4},
  pages={4-5},
  doi={10.1109/MCOMSTD.2022.10034506}}

2022

Journal

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Y. Matsubara, M. Levorato and F. Restuccia

ACM Computing Surveys

ABSTRACT BIBTEX HTML


@article{matsubara2022split,
  title={{Split Computing And Early Exiting For Deep Learning Applications: Survey And Research Challenges}},
  author={Matsubara, Yoshitomo and Levorato, Marco and Restuccia, Francesco},
  journal={ACM Computing Surveys},
  volume={55},
  number={5},
  pages={1--30},
  year={2022},
  publisher={ACM New York, NY}
}

2022

Conference

3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems

F. Restuccia, E. Blasch, A. Ashdown, J. Ashdown, and K. Turck

Proceedings of IEEE Military Communications Conference (IEEE MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia20223d,
  title={{3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems}},
  author={Restuccia, Francesco and Blasch, Erik and Ashdown, Andrew and Ashdown, Jonathan and Turck, Kurt},
  booktitle={Proceedings of IEEE Military Communications Conference (IEEE MILCOM)},
  pages={19--24},
  year={2022},
  organization={IEEE}
}

2022

Conference

SDR-LoRa: Dissecting and Implementing LoRa on Software-Defined Radios to Advance Experimental IoT Research

F. Busacca, S. Mangione, I. Tinnirello, S. Palazzo, and F. Restuccia

Proceedings of ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization (ACM WINTECH)

ABSTRACT BIBTEX HTML


@inproceedings{busacca2022sdr,
  title={{SDR-LoRa: Dissecting and Implementing LoRa on Software-Defined Radios to Advance Experimental IoT Research}},
  author={Busacca, Fabio and Mangione, Stefano and Tinnirello, Ilenia and Palazzo, Sergio and Restuccia, Francesco},
  booktitle={Proceedings of the ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization},
  pages={24--31},
  year={2022}
}

2022

Journal

Generalized Wireless Adversarial Deep Learning

F. Restuccia, S. D’Oro, A. Al-Shawabka, B. Costa Rendon, K. Chowdhury, S. Ioannidis and T. Melodia

Computer Networks

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2020generalized,
  title={{Generalized Wireless Adversarial Deep Learning}},
  author={Restuccia, Francesco and D'Oro, Salvatore and Al-Shawabka, Amani and Rendon, Bruno Costa and Chowdhury, Kaushik and Ioannidis, Stratis and Melodia, Tommaso},
  booktitle={Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning},
  pages={49--54},
  year={2020}
}

2022

Conference

Terahertz Communications Can Work in Rain and Snow: Impact of Adverse Weather Conditions on Channels at 140 GHz

J. Hall, M. Polese, V. Petrov, D. Bodet, F. Restuccia, T. Melodia, and J. Jornet

Proceedings of the ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems (mmNets)

ABSTRACT BIBTEX HTML


@inproceedings{sen2022terahertz,
  title={{Terahertz Communications Can Work in Rain and Snow: Impact of Adverse Weather Conditions on Channels at 140 GHz}},
  author={Sen, Priyangshu and Hall, Jacob and Polese, Michele and Petrov, Vitaly and Bodet, Duschia and Restuccia, Francesco and Melodia, Tommaso and Jornet, Josep M},
  booktitle={Proceedings of the ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems},
  pages={13--18},
  year={2022}
}

2022

Conference

DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning

F. Meneghello, M. Rossi and F. Restuccia

Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS)

ABSTRACT BIBTEX HTML


@inproceedings{meneghello2022deepcsi,
  title={{DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning}},
  author={Meneghello, Francesca and Rossi, Michele and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS)},
  pages={1062--1072},
  year={2022},
  organization={IEEE}
}

2022

Conference

ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning

N. Bahadori, J. Ashdown and F. Restuccia

Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (IEEE WoWMoM) — Best Paper Award

ABSTRACT BIBTEX HTML

Thanks to the ubiquitousness of Wi-Fi access points and devices, Wi-Fi sensing enables transformative applications in remote health care, home/office security, and surveillance, just to name a few. Existing work has explored the usage of machine learning on channel state information (CSI) computed from Wi-Fi packets to classify events of interest. However, most of these algorithms require a significant amount of data collection, as well as extensive computational power for additional CSI feature extraction. Moreover, the majority of these models suffer from poor accuracy when tested in a new/untrained environment. In this paper, we propose ReWiS, a novel framework for robust and environment-independent Wi-Fi sensing. The key innovation of ReWiS is to leverage few-shot learning (FSL) as the inference engine, which (i) reduces the need for extensive data collection and application-specific feature extraction; (ii) can rapidly generalize to new environments by leveraging only a few new samples. Moreover, ReWiS leverages multi-antenna, multi-receiver diversity, as well as fine-grained frequency resolution, to improve the overall robustness of the algorithms. Finally, we propose a technique based on singular value decomposition (SVD) to make the FSL input constant irrespective of the number of receive antennas. We prototype the ReWiS using off-the-shelf Wi-Fi equipment and showcase its performance by considering a compelling use case of human activity recognition. Thus, we perform an extensive data collection campaign in three different propagation environments with two human subjects. We evaluate the impact of each diversity component on the performance and compare ReWiS with an existing convolutional neural network (CNN)-based approach. Experimental results show that ReWiS improves the performance by about 40% with respect to existing single-antenna low-resolution approaches. Moreover, when compared to a CNN-based approach, ReWiS shows a 35% more accuracy and less than 10% drop in accuracy when tested in different environments, while the CNN drops by more than 45%. To allow reproducibility of our results and to address the current dearth of Wi-Fi sensing datasets, we pledge to release our 60 GB dataset and the entire code repository to the community.


@inproceedings{bahadori2022rewis,
  title={{ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning}},
  author={Bahadori, Niloofar and Ashdown, Jonathan and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  pages={50--59},
  year={2022},
  organization={IEEE}
}

2022

Conference

SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection

D. Callegaro, M. Levorato, and F. Restuccia

Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM}

ABSTRACT BIBTEX HTML

Mobile devices such as drones and autonomous vehicles increasingly rely on object detection (OD) through deep neural networks (DNNs) to perform critical tasks such as navigation, target-tracking and surveillance, just to name a few. Due to their high complexity, the execution of these DNNs requires excessive time and energy. Low-complexity object tracking (OT) is thus used along with OD, where the latter is periodically applied to generate "fresh" references for tracking. However, the frames processed with OD incur large delays, which does not comply with real-time applications requirements. Offloading OD to edge servers can mitigate this issue, but existing work focuses on the optimization of the offloading process in systems where the wireless channel has a very large capacity. Herein, we consider systems with constrained and erratic channel capacity, and establish parallel OT (at the mobile device) and OD (at the edge server) processes that are resilient to large OD latency. We propose Katch-Up, a novel tracking mechanism that improves the system resilience to excessive OD delay. We show that this technique greatly improves the quality of the reference available to tracking, and boosts performance up to 33%. However, while Katch-Up significantly improves performance, it also increases the computing load of the mobile device. Hence, we design SmartDet, a low-complexity controller based on deep reinforcement learning (DRL) that learns to achieve the right trade-off between resource utilization and OD performance. SmartDet takes as input highly-heterogeneous context-related information related to the current video content and the current network conditions to optimize frequency and type of OD offloading, as well as Katch-Up utilization. We extensively evaluate SmartDet on a real-world testbed composed by a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link, to collect several network-related traces, as well as energy measurements. We consider a state-of-the-art video dataset (ILSVRC 2015 - VID) and state-of-the-art OD models (EfficientDet 0, 2 and 4). Experimental results show that SmartDet achieves an optimal balance between tracking performance – mean Average Recall (mAR) and resource usage. With respect to a baseline with full Katch-Up usage and maximum channel usage, we still increase mAR by 4% while using 50% less of the channel and 30% power resources associated with Katch-Up. With respect to a fixed strategy using minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the frames.


@inproceedings{callegaro2022smartdet,
  title={{SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection}},
  author={Callegaro, Davide and Levorato, Marco and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  pages={357--366},
  year={2022},
  organization={IEEE}
}

2022

Conference

BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing

Y. Matsubara, D. Callegaro, S. Singh, M. Levorato, and F. Restuccia

Proceedings of the IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM)

ABSTRACT BIBTEX HTML


@inproceedings{matsubara2022bottlefit,
  title={{BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing}},
  author={Matsubara, Yoshitomo and Callegaro, Davide and Singh, Sameer and Levorato, Marco and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  pages={337--346},
  year={2022},
  organization={IEEE}
}

2022

Conference

ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control

L. Baldesi, F. Restuccia and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM) — Best Paper Award

ABSTRACT BIBTEX HTML

Today’s radio access networks (RANs) are monolithic entities which often operate statically on a given set of parameters for the entirety of their operations. To implement realistic and effective spectrum sharing policies, RANs will need to seamlessly and intelligently change their operational parameters. In stark contrast with existing paradigms, the new O-RAN architectures for 5G-and-beyond networks (NextG) separate the logic that controls the RAN from its hardware substrate, allowing unprecedented real-time fine-grained control of RAN components. In this context, we propose the Channel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliant framework that allows (i) sensing the spectrum to infer the presence of interference and (ii) reacting in real time by switching the distributed unit (DU) and radio unit (RU) operational parameters according to a specified spectrum access policy. ChARM is based on neural networks operating directly on unprocessed I/Q waveforms to determine the current spectrum context. ChARM does not require any modification to the existing 3GPP standards. It is designed to operate within the O-RAN specifications, and can be used in conjunction with other spectrum sharing mechanisms (e.g., LTE-U, LTE-LAA or MulteFire). We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi-Fi in unlicensed bands, where a controller operating over a RAN Intelligent Controller (RIC) senses the spectrum and switches cell frequency to avoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with. To collect standard-compliant Wi-Fi data, we extended the Colosseum testbed using system-on-chip (SoC) boards running a modified version of the OpenWiFi architecture. Experimental results show that ChARM achieves accuracy of up to 96% on Colosseum and 85% on an over-the-air testbed, demonstrating the capacity of ChARM to exploit the considered spectrum channels.


@inproceedings{baldesi2022charm,
  title={{ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control}},
  author={Baldesi, Luca and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={240--249},
  year={2022},
  organization={IEEE}
}

2022

Journal

Dynamic Spectrum Sharing Between Active and Passive Users Above 100 GHz

M. Polese, V. Ariyarathna, P. Sen, J. V. Siles, F. Restuccia, T. Melodia, J. M. Jornet

Nature Communications Engineering

ABSTRACT BIBTEX HTML


@article{polese2022dynamic,
  title={{Dynamic Spectrum Sharing Between Active And Passive Users Above 100 GHz}},
  author={Polese, Michele and Ariyarathna, Viduneth and Sen, Priyangshu and Siles, Jose V and Restuccia, Francesco and Melodia, Tommaso and Jornet, Josep M},
  journal={Communications Engineering},
  volume={1},
  number={1},
  pages={6},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

2022

Conference

AiEEG: Personalized Seizure Prediction Through Partially-Reconfigurable Deep Neural Networks

D. Uvaydov, R. Guida, P. Johari, F. Restuccia and T. Melodia

Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom)

ABSTRACT BIBTEX HTML


@inproceedings{uvaydov2022aieeg,
  title={{AiEEG: Personalized Seizure Prediction Through Partially-Reconfigurable Deep Neural Networks}},
  author={Uvaydov, Daniel and Guida, Raffaele and Johari, Pedram and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom)},
  pages={77--88},
  year={2022},
  organization={IEEE}
}

2022

Conference

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

L. Bonati, P. Johari, M. Polese, S. D’Oro, S. Mohanti, M. Tehrani-Moayyed, D. Villa, S. Shrivastava, C. Tassie, K. Yoder, A. Bagga, P. Patel, V. Petkov, M. Seltser, F. Restuccia, A. Gosain, K.R. Chowdhury, S. Basagni, T. Melodia

Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

ABSTRACT BIBTEX HTML


@inproceedings{bonati2021colosseum,
	author = {Bonati, Leonardo and Johari, Pedram and Polese, Michele and D'Oro, Salvatore and Mohanti, Subhramoy and Tehrani-Moayyed, Miead and Villa, Davide and Shrivastava, Shweta and Tassie, Chinenye and Yoder, Kurt and Bagga, Ajeet and Patel, Paresh and Petkov, Ventz and Seltser, Michael and Restuccia, Francesco and Gosain, Abhimanyu and Chowdhury, Kaushik R. and Basagni, Stefano and Melodia, Tommaso},
	booktitle = {Proceedings of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
	organization = {IEEE},
	pages = {105--113},
	title = {{Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation}},
	year = {2021}}

2021

Journal

DeepFIR: Channel-Robust Physical-Layer Deep Learning Through Adaptive Wave-form Filtering

F. Restuccia, S. D’Oro, A. Al-Shawabka, B. Costa Rendon, S. Ioannidis and T. Melodia

IEEE Transactions on Wireless Communications

ABSTRACT BIBTEX HTML


@article{restuccia2021deepfir,
  title={{DeepFIR: Channel-Robust Physical-Layer Deep Learning Through Adaptive Waveform Filtering}},
  author={Restuccia, Francesco and D’Oro, Salvatore and Al-Shawabka, Amani and Rendon, Bruno Costa and Ioannidis, Stratis and Melodia, Tommaso},
  journal={IEEE Transactions on Wireless Communications},
  volume={20},
  number={12},
  pages={8054--8066},
  year={2021},
  publisher={IEEE}
}

2021

Patent

Real-Time Channel-Resilient Optimization of Radio Fingerprinting

F. Restuccia, S. D’Oro and T. Melodia

U.S. Patent No.: 11,184,783

HTML

2021

Journal

Coordinated 5G Network Slicing: How Constructive Interference Can Boost Network Throughput

S. D’Oro, L. Bonati, F. Restuccia, and T. Melodia

IEEE/ACM Transactions on Networking

ABSTRACT BIBTEX HTML


@article{d2021coordinated,
  title={{Coordinated 5G Network Slicing: How Constructive Interference Can Boost Network Throughput},
  author={D’Oro, Salvatore and Bonati, Leonardo and Restuccia, Francesco and Melodia, Tommaso},
  journal={IEEE/ACM Transactions on Networking},
  volume={29},
  number={4},
  pages={1881--1894},
  year={2021},
  publisher={IEEE}
}

2021

Conference

SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing

D. Callegaro, M. Levorato, and F. Restuccia

Proceedings of IEEE International Conference on Sensing, Communication and Networking (SECON)

ABSTRACT BIBTEX HTML

Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channel quality, network load, and edge server load can interrupt the task flow's execution, which necessarily leads to severe disruption of the system's key operations. Existing work has mostly tackled the problem with reactive approaches, which cannot guarantee task-level reliability. Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading. By conducting a preliminary experimental evaluation, we show that there is no dominant feature that can predict the edge-MAS system reliability, which calls for an ensemble and selection of weaker features. To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework. We first mathematically formulate a Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple edge servers for redundancy, and needs to select which server(s) to transmit its computing tasks in order to maximize the probability of task execution while minimizing channel and edge resource utilization. We then create a predictor based on Deep Reinforcement Learning (DRL), which produces the optimum task assignment based on application-, network- and telemetry-based features. We prototype SeReMAS on a testbed composed by a Tarot650 quadcopter drone, mounting a PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi interfaces. We extensively evaluate SeReMAS by considering an application where one drone offloads high-resolution images for real-time analysis to three edge servers on the ground. Experimental results show that SeReMAS improves the task execution probability by 17% with respect to existing reactive-based approaches. To allow full reproducibility of results, we share the dataset and code with the research community.


@inproceedings{callegaro2021seremas,
  title={{SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing}},
  author={Callegaro, Davide and Levorato, Marco and Restuccia, Francesco},
  booktitle={Proceedings of IEEE International Conference on Sensing, Communication, and Networking (SECON)},
  pages={1--9},
  year={2021},
  organization={IEEE}
}

2021

Conference

DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation

A. Al-Shawabka, P. Pietraski, S.B. Pattar, F. Restuccia and T. Melodia

Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

ABSTRACT BIBTEX HTML

The Long Range (LoRa) protocol for low-power wide-area networks (LPWANs) is a strong candidate to enable the massive roll-out of the Internet of Things (IoT) because of its low cost, impressive sensitivity (-137dBm), and massive scalability potential. As tens of thousands of tiny LoRa devices are deployed over large geographic areas, a key component to the success of LoRa will be the development of reliable and robust authentication mechanisms. To this end, Radio Frequency Fingerprinting (RFFP) through deep learning (DL) has been heralded as an effective zero-power supplement or alternative to energy-hungry cryptography. Existing work on LoRa RFFP has mostly focused on small-scale testbeds and low-dimensional learning techniques; however, many challenges remain. Key among them are authentication techniques robust to a wide variety of channel variations over time and supporting a vast population of devices. In this work, we advance the state of the art by presenting (i) the first massive experimental evaluation of DL RFFP and (ii) new data augmentation techniques for LoRa designed to counter the degradation introduced by the wireless channel. Specifically, we collected and publicly shared more than 1TB of waveform data from 100 bit-similar devices (with identical manufacturing processes) over different deployment scenarios (outdoor vs. indoor) and spanning several days. We train and test diverse DL models (convolutional and recurrent neural networks) using either preamble or payload data slices. We compare three different representations of the received signal: (i) IQ, (ii) amplitude-phase, and (iii) spectrogram. Finally, we propose a novel data augmentation technique called DeepLoRa to enhance the LoRa RFFP performance. Results show that (i) training the CNN models with IQ representation is not always the best combo in fingerprinting LoRa radios; training CNNs and RNN-LSTMs with amplitude-phase and spectrogram representations may increase the fingerprinting performance in small and medium-scale testbeds; (ii) using only payload data in the fingerprinting process outperforms preamble only data, and (iii) DeepLoRa data augmentation technique improves the classification accuracy from 19% to 36% in the RFFP challenging case of training on data collected on a different day than the testing data. Moreover, DeepLoRa raises the accuracy from 82% to 91% when training and testing 100 devices with data collected on the same day.


@inproceedings{al2021deeplora,
  title={{DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation}},
  author={Al-Shawabka, Amani and Pietraski, Philip and Pattar, Sudhir B and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  pages={251--260},
  year={2021}
}

2021

Conference

DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks

M. Polese, F. Restuccia, and T. Melodia

Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

ABSTRACT BIBTEX HTML

Highly directional millimeter wave (mmWave) radios need to perform beam management to establish and maintain reliable links. To achieve this objective, existing solutions mostly rely on explicit coordination between the transmitter (TX) and the receiver (RX), which significantly reduces the airtime available for communication and further complicates the network protocol design. This paper advances the state of the art by presenting DeepBeam, a framework for beam management that does not require pilot sequences from the TX, nor any beam sweeping or synchronization from the RX. This is achieved by inferring (i) the Angle of Arrival (AoA) of the beam and (ii) the actual beam being used by the transmitter through waveform-level deep learning on ongoing transmissions between the TX to other receivers. In this way, the RX can associate Signal-to-Noise-Ratio (SNR) levels to beams without explicit coordination with the TX. This is possible because different beam patterns introduce different "impairments" to the waveform, which can be subsequently learned by a convolutional neural network (CNN). To demonstrate the generality of DeepBeam, we conduct an extensive experimental data collection campaign where we collect more than 4 TB of mmWave waveforms with (i) 4 phased array antennas at 60.48 GHz, (ii) 2 codebooks containing 24 one-dimensional beams and 12 two-dimensional beams; (iii) 3 receiver gains; (iv) 3 different AoAs; (v) multiple TX and RX locations. Moreover, we collect waveform data with two custom-designed mmWave software-defined radios with fully-digital beamforming architectures at 58 GHz. We also implement our learning models in FPGA to evaluate latency performance. Results show that DeepBeam (i) achieves accuracy of up to 96%, 84% and 77% with a 5-beam, 12-beam and 24-beam codebook, respectively; (ii) reduces latency by up to 7x with respect to the 5G NR initial beam sweep in a default configuration and with a 12-beam codebook. The waveform dataset and the full DeepBeam code repository are publicly available.


@inproceedings{polese2021deepbeam,
  title={{DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks}},
  author={Polese, Michele and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  pages={61--70},
  year={2021}
}

2021

Conference

The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting

M. Piva, G. Maselli, and F. Restuccia

Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

ABSTRACT BIBTEX HTML

Millions of RFID tags are pervasively used all around the globe to inexpensively identify a wide variety of everyday-use objects. One of the key issues of RFID is that tags cannot use energy-hungry cryptography, and thus can be easily cloned. For this reason, radio fingerprinting (RFP) is a compelling approach that leverages the unique imperfections in the tag's wireless circuitry to achieve large-scale RFID clone detection. Recent work, however, has unveiled that time-varying channel conditions can significantly decrease the accuracy of the RFP process. Prior art in RFID identification does not consider this critical aspect, and instead focuses on custom-tailored feature extraction techniques and data collection with static channel conditions. For this reason, we propose the first large-scale investigation into RFP of RFID tags with dynamic channel conditions. Specifically, we perform a massive data collection campaign on a testbed composed by 200 off-the-shelf identical RFID tags and a software-defined radio (SDR) tag reader. We collect data with different tag-reader distances in an over-the-air configuration. To emulate implanted RFID tags, we also collect data with two different kinds of porcine meat inserted between the tag and the reader. We use this rich dataset to train and test several convolutional neural network (CNN)-based classifiers in a variety of channel conditions. Our investigation reveals that training and testing on different channel conditions drastically degrades the classifier's accuracy. For this reason, we propose a novel training framework based on federated machine learning (FML) and data augmentation (DAG) to boost the accuracy. Extensive experimental results indicate that (i) our FML approach improves accuracy by up to 48%; (ii) our DAG approach improves the FML performance by up to 19% and the single-dataset performance by 31%. To the best of our knowledge, this is the first paper experimentally demonstrating the efficacy of FML and DAG on a large device population. To allow full replicability, we are sharing with the research community our fully-labeled 200-GB RFID waveform dataset, as well as the entirety of our code and trained models, concurrently with our submission.


@inproceedings{piva2021tags,
  title={{The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting}},
  author={Piva, Mauro and Maselli, Gaia and Restuccia, Francesco},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  pages={41--50},
  year={2021}
}

2021

Conference

A Blockchain Definition to Clarify Its Role for the Internet of Things

L. Ghiro, F. Restuccia, S. D’Oro, S. Basagni, T. Melodia, L. Maccari, and R. Lo Cigno

Mediterranean Communication and Computer Networking Conference (MedComNet)

ABSTRACT BIBTEX HTML


@inproceedings{ghiro2021blockchain,
  title={{A Blockchain Definition to Clarify Its Role for the Internet of Things}},
  author={Ghiro, Lorenzo and Restuccia, Francesco and D'Oro, Salvatore and Basagni, Stefano and Melodia, Tommaso and Maccari, Leonardo and Cigno, Renato Lo},
  booktitle={Mediterranean Communication and Computer Networking Conference (MedComNet)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}

2021

Conference

Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification

S. D’Oro, F. Restuccia and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML

Due to the sheer scale of the Internet of Things (IoT) and 5G, the wireless spectrum is becoming severely congested. For this reason, wireless devices will need to continuously adapt to current spectrum conditions by changing their communication parameters in real-time. Therefore, wireless signal classification (WSC) will become a compelling necessity to decode fast-changing signals from dynamic transmitters. Thanks to its capability of classifying complex phenomena without explicit mathematical modeling, deep learning (DL) has been demonstrated to be a key enabler of WSC. Although DL can achieve a very high accuracy under certain conditions, recent research has unveiled that the wireless channel can disrupt the features learned by the DL model during training, thus drastically reducing the classification performance in real-world live settings. Since retraining classifiers is cumbersome after deployment, existing work has leveraged the usage of carefully-tailored Finite Impulse Response (FIR) filters that, when applied at the transmitter’s side, can restore the features that are lost because of the the channel actions, i.e., waveform synthesis. However, these approaches compute FIRs using offline optimization strategies, which limits their efficacy in highly-dynamic channel settings. In this paper, we improve the state of the art by proposing Chares, a Deep Reinforcement Learning (DRL)-based framework for channel-resilient adaptive waveform synthesis. Chares adapts to new and unseen channel conditions by optimally computing through DRL the FIRs in real time. Chares is a DRL agent whose architecture is based upon the Twin Delayed Deep Deterministic Policy Gradients (TD3), which requires minimal feedback from the receiver and explores a continuous action space for best performance. Chares has been extensively evaluated on two well-known datasets with an extensive number of channels. We have also evaluated the real-time latency of Chares with an implementation on field-programmable gate array (FPGA). Results show that Chares increases the accuracy up to 4.1x when no waveform synthesis is performed, by 1.9x with respect to existing work, and can compute new actions within 41µs.


@inproceedings{d2021can,
  title={{Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification}},
  author={D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={1--10},
  year={2021},
  organization={IEEE}
}

2021

Conference

SteaLTE: Private 5G Cellular Connectivity as a Service with Full-stack Wireless Steganography

L. Bonati, S. D’Oro, F. Restuccia, S. Basagni and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML


@inproceedings{bonati2021stealte,
  title={{SteaLTE: Private 5G Cellular Connectivity as a Service with Full-stack Wireless Steganography}},
  author={Bonati, Leonardo and D’Oro, Salvatore and Restuccia, Francesco and Basagni, Stefano and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={1--10},
  year={2021},
  organization={IEEE}
}

2021

Conference

DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning

D. Uvaydov, S. D’Oro, F. Restuccia and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML

Spectrum sharing will be a key technology to tackle spectrum scarcity in the sub-6 GHz bands. To fairly access the shared bandwidth, wireless users will necessarily need to quickly sense large portions of spectrum and opportunistically access unutilized bands. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over large bandwidths to detect tiny spectrum holes and to guarantee strict real-time digital signal processing (DSP) constraints; (ii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. To the best of our knowledge, the literature lacks spectrum sensing techniques able to accomplish both requirements. In this paper, we propose DeepSense, a software/hardware framework for real-time wideband spectrum sensing that relies on real-time deep learning tightly integrated into the transceiver’s baseband processing logic to detect and exploit unutilized spectrum bands. DeepSense uses a convolutional neural network (CNN) implemented in the wireless platform’s hardware fabric to analyze a small portion of the unprocessed baseband waveform to automatically extract the maximum amount of information with the least amount of I/Q samples. We extensively validate the accuracy, latency and generality performance of DeepSense with (i) a 400 GB dataset containing hundreds of thousands of WiFi transmissions collected "in the wild" with different Signal-to-Noise-Ratio (SNR) conditions and over different days; (ii) a dataset of transmissions collected using our own software-defined radio testbed; and (iii) a synthetic dataset of LTE transmissions under controlled SNR conditions. We also measure the real-time latency of the CNNs trained on the three datasets with an FPGA implementation, and compare our approach with a fixed energy threshold mechanism. Results show that our learning-based approach can deliver a precision and recall of 98% and 97% respectively and a latency as low as 0.61ms. For reproducibility and benchmarking purposes, we pledge to share the code and the datasets used in this paper to the community.


@inproceedings{uvaydov2021deepsense,
  title={{DeepSense: Fast Wideband Spectrum Sensing Through Real-Time In-the-Loop Deep Learning}},
  author={Uvaydov, Daniel and D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={1--10},
  year={2021},
  organization={IEEE}
}

2021

Journal

Arena: A 64-antenna SDR-based Ceiling Grid Testing Platform for Sub-6 GHz 5G-and-Beyond Radio Spectrum Research

L. Bertizzolo, L. Bonati, E. Demirors, A. Al-Shawabka, S. D’Oro, F. Restuccia, and T. Melodia

Computer Networks

ABSTRACT BIBTEX HTML


@article{bertizzolo2020arena,
  title={{Arena: A 64-antenna SDR-based Ceiling Grid Testing Platform for Sub-6 GHz 5G-and-Beyond Radio Spectrum Research}},
  author={Bertizzolo, Lorenzo and Bonati, Leonardo and Demirors, Emrecan and Al-Shawabka, Amani and D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso},
  journal={Computer Networks},
  volume={181},
  pages={107436},
  year={2020},
  publisher={Elsevier}
}

2020

Conference

HyBloSE: Hybrid Blockchain for Secure-by-Design Smart Environments

G. Maselli, M. Piva and F. Restuccia

Proceedings of ACM Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock)

ABSTRACT BIBTEX HTML


@inproceedings{maselli2020hyblose,
  title={{HyBloSE: Hybrid Blockchain for Secure-by-Design Smart Environments}},
  author={Maselli, Gaia and Piva, Mauro and Restuccia, Francesco},
  booktitle={Proceedings of ACM Workshop on Cryptocurrencies and Blockchains for Distributed Systems (CryBlock)},
  pages={23--28},
  year={2020}
}

2020

Journal

Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond

F. Restuccia and T. Melodia

IEEE Communications Magazine

ABSTRACT BIBTEX HTML


@article{restuccia2020deep,
  title={{Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond}},
  author={Restuccia, Francesco and Melodia, Tommaso},
  journal={IEEE Communications Magazine},
  volume={58},
  number={10},
  pages={58--64},
  year={2020},
  publisher={IEEE}
}

2020

Journal

The Implantable Internet of Medical Things: Toward Lifelong Remote Monitoring and Treatment of Chronic Diseases

R. Guida, N. Dave, F. Restuccia, E. Demirors, and T. Melodia

ACM GetMobile

ABSTRACT BIBTEX HTML


@article{guida2021implantable,
  title={{The Implantable Internet Of Medical Things: Toward Lifelong Remote Monitoring And Treatment Of Chronic Diseases}},
  author={Guida, Raffaele and Dave, Neil and Restuccia, Francesco and Demirors, Emrecan and Melodia, Tommaso},
  journal={GetMobile: Mobile Computing and Communications},
  volume={24},
  number={3},
  pages={20--25},
  year={2021},
  publisher={ACM New York, NY, USA}
}

2020

Conference

Generalized Wireless Adversarial Deep Learning

F. Restuccia, S. D’Oro, A. Al-Shawabka, B. Costa Rendon, K. Chowdhury, S. Ioannidis and T. Melodia

Proceedings of ACM Workshop on Wireless Security and Machine Learning (WiseML)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2020generalized,
  title={{Generalized Wireless Adversarial Deep Learning}},
  author={Restuccia, Francesco and D'Oro, Salvatore and Al-Shawabka, Amani and Rendon, Bruno Costa and Chowdhury, Kaushik and Ioannidis, Stratis and Melodia, Tommaso},
  booktitle={Proceedings of the ACM Workshop on Wireless Security and Machine Learning (WiseML)},
  pages={49--54},
  year={2020}
}

2020

Conference

Exposing the Fingerprint: Dissecting the Impact of the Wireless Channel on Radio Fingerprinting

A. Al-Shawabka, F. Restuccia, S. D’Oro, T. Jian, B. Costa Rendon, N. Soltani, J. Dy, K. Chowdhury, S. Ioannidis and T. Melodia

Proceedings of the IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML

Radio fingerprinting uniquely identifies wireless devices by leveraging tiny hardware-level imperfections inevitably present in off-the-shelf radio circuitry. This way, devices can be directly identified at the physical layer by analyzing the unprocessed received waveform - thus avoiding energy-expensive upper-layer cryptography that resource-challenged embedded devices may not be able to afford. Recent advances have proven that convolutional neural networks (CNNs) - thanks to their multidimensional mappings - can achieve fingerprinting accuracy levels impossible to achieve by traditional low-dimensional algorithms. The same research, however, has also suggested that the wireless channel may negatively impact the accuracy of CNN-based radio fingerprinting algorithms by making device-unique hardware imperfections much harder to recognize.In spite of the growing interest in radio fingerprinting research by academia and DARPA, the wireless research community still lacks (i) a large-scale open dataset for radio fingerprinting collected in diverse environments and rich, diverse, channel conditions; and (ii) a full-fledged, systematic, quantitative investigation of the impact of the wireless channel on the accuracy of CNN-based radio fingerprinting algorithms. The key contribution of this paper is to bridge this gap by (i) collecting and sharing with the community more than 7TB of wireless data obtained from 20 wireless devices with identical RF circuitry (and thus, worst-case scenario for fingerprinting) over the course of several days in (a) an anechoic chamber, (b) in-the-wild testbed, and (c) with cable connections; and (ii) providing a first-of-its-kind evaluation of the impact of the wireless channel on CNN-based fingerprinting algorithms through (a) the 7TB experimental dataset and (b) a 400GB dataset provided by DARPA containing hundreds of thousands of transmissions from thousands of WiFi and ADS-B devices with different SNR conditions. Experimental results conclude that (i) the wireless channel impacts the classification accuracy significantly, i.e., from 85% to 9% and from 30% to 17% in the experimental and DARPA dataset, respectively; and that (ii) equalizing I/Q data can increase the accuracy to a significant extent (i.e., by up to 23%) when the number of devices increases significantly.


@inproceedings{al2020exposing,
  title={{Exposing the Fingerprint: Dissecting the Impact of the Wireless Channel on Radio Fingerprinting}},
  author={Al-Shawabka, Amani and Restuccia, Francesco and D’Oro, Salvatore and Jian, Tong and Rendon, Bruno Costa and Soltani, Nasim and Dy, Jennifer and Ioannidis, Stratis and Chowdhury, Kaushik and Melodia, Tommaso},
  booktitle={Proceedings of the IEEE Conference on Computer Communications (INFOCOM)},
  pages={646--655},
  year={2020},
  organization={IEEE}
}

2020

Conference

DeepWiERL: Bringing Deep Reinforcement Learning to the Internet of Self-Adaptive Things

F. Restuccia and T. Melodia

Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2020deepwierl,
  title={{DeepWiERL: Bringing Deep Reinforcement Learning to the Internet of Self-Adaptive Things}},
  author={Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM)},
  pages={844--853},
  year={2020},
  organization={IEEE}
}

2020

Journal

Toward Operator-to-Waveform 5G Radio Access Network Slicing

S. D’Oro, F. Restuccia, and T. Melodia

IEEE Communications Magazine

ABSTRACT BIBTEX HTML


@article{d2020toward,
  title={{Toward Operator-to-Waveform 5G Radio Access Network Slicing}},
  author={D'Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso},
  journal={IEEE Communications Magazine},
  volume={58},
  number={4},
  pages={18--23},
  year={2020},
  publisher={IEEE}
}

2020

Journal

No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments

K. Sankhe, M. Belgiovine, F. Zhou, L. Angioloni, F. Restuccia, S. D’Oro, T. Melodia, S. Ioannidis, and K. Chowdhury

IEEE Transactions on Cognitive Communications and Networking

ABSTRACT BIBTEX HTML


@article{sankhe2019no,
  title={{No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-layer Hardware Impairments}},
  author={Sankhe, Kunal and Belgiovine, Mauro and Zhou, Fan and Angioloni, Luca and Restuccia, Frank and D’Oro, Salvatore and Melodia, Tommaso and Ioannidis, Stratis and Chowdhury, Kaushik},
  journal={IEEE Transactions on Cognitive Communications and Networking},
  volume={6},
  number={1},
  pages={165--178},
  year={2019},
  publisher={IEEE}
}

2020

Conference

Impairment Shift Keying: Covert Signaling by Deep Learning of Controlled Radio Imperfections

K. Sankhe, F. Restuccia, S. D’Oro, T. Jian, Z. Wang, A. Al-Shawabka, J. Dy, T. Melodia, S. Ioannidis, and K. Chowdhury

Proceedings of IEEE Military Communications Conference (MILCOM)

ABSTRACT BIBTEX HTML


@inproceedings{sankhe2019impairment,
  title={{Impairment Shift Keying: Covert Signaling by Deep Learning of Controlled Radio Imperfections}},
  author={Sankhe, Kunal and Restuccia, Francesco and D'Oro, Salvatore and Jian, Tong and Wang, Zifeng and Al-Shawabka, Amani and Dy, Jennifer and Melodia, Tommaso and Ioannidis, Stratis and Chowdhury, Kaushik},
  booktitle={Proceedings of the IEEE Military Communications Conference (MILCOM)},
  pages={598--603},
  year={2019},
  organization={IEEE}
}

2019

Conference

U-Verse: A Miniaturized Platform for End-to-End Closed-Loop Implantable Internet of Medical Things Systems

R. Guida, N. Dave, F. Restuccia, E. Demirors, and T. Melodia

Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys)

ABSTRACT BIBTEX HTML


@inproceedings{guida2019u,
  title={{U-Verse: A Miniaturized Platform for End-to-End Closed-Loop Implantable Internet of Medical Things Systems}},
  author={Guida, Raffaele and Dave, Neil and Restuccia, Francesco and Demirors, Emrecan and Melodia, Tommaso},
  booktitle={Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys)},
  pages={311--323},
  year={2019}
}

2019

Conference

MillimeTera: Toward A Large-Scale Open-Source mmWave and Terahertz Experimental Testbed

M. Polese, F. Restuccia, A. Gosain, J. M. Jornet, S Bhardwaj, V. Ariyarathna, S. Mandal, K. Zheng, A. Dhananjay, M. Mezzavilla, J. Buckwalter, M. Rodwell, X. Wang, M. Zorzi, A. Madanayake and T. Melodia

Proceedings of ACM Workshop on Millimeter-Wave Networks and Sensing Systems (mmNets)

ABSTRACT BIBTEX HTML


@inproceedings{polese2019millimetera,
  title={{MillimeTera: Toward A Large-Scale Open-Source mmWave and Terahertz Experimental Testbed}},
  author={Polese, Michele and Restuccia, Francesco and Gosain, Abhimanyu and Jornet, Josep and Bhardwaj, Shubhendu and Ariyarathna, Viduneth and Mandal, Soumyajit and Zheng, Kai and Dhananjay, Aditya and Mezzavilla, Marco and Buckwalter, Jim and Rodwell, Mark and Wang, Xin and Zorzi, Michele and Madanayake, Arjuna and Melodia, Tommaso},
  booktitle={Proceedings of ACM Workshop on Millimeter-Wave Networks and Sensing Systems (mmNets)},
  pages={27--32},
  year={2019}
}

2019

Conference

Sl-EDGE: Network Slicing at the Edge

S. D’Oro, L. Bonati, F. Restuccia, M. Polese, M. Zorzi and T. Melodia

Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

ABSTRACT BIBTEX HTML


@inproceedings{d2020sl,
  title={{Sl-EDGE: Network Slicing at the Edge}},
  author={D'Oro, Salvatore and Bonati, Leonardo and Restuccia, Francesco and Polese, Michele and Zorzi, Michele and Melodia, Tommaso},
  booktitle={Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  pages={1--10},
  year={2020}
}

2019

Conference

PolymoRF: Polymorphic Wireless Receivers Through Physical Layer Deep Learning

F. Restuccia and T. Melodia

Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2020polymorf,
  title={{PolymoRF: Polymorphic Wireless Receivers Through Physical Layer Deep Learning}},
  author={Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of the ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc)},
  pages={271--280},
  year={2020}
}

2019

Journal

Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey

J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, and T. Melodia

Ad Hoc Networks

ABSTRACT BIBTEX HTML

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network’s optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning’s popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.


@article{jagannath2019machine,
  title={{Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey}},
  author={Jagannath, Jithin and Polosky, Nicholas and Jagannath, Anu and Restuccia, Francesco and Melodia, Tommaso},
  journal={Ad Hoc Networks},
  volume={93},
  pages={101913},
  year={2019},
  publisher={Elsevier}
}

2019

Conference

Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks

L. Zhang, F. Restuccia, T. Melodia and S.M. Pudlewski

Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)

ABSTRACT BIBTEX HTML

Recent research advances in wireless security have shown that advanced jamming can significantly decrease the performance of wireless communications. In advanced jamming, the adversary intentionally concentrates the available energy budget on specific critical components (e.g., pilot symbols, acknowledgement packets, etc.) to (i) increase the jamming effectiveness, as more targets can be jammed with the same energy budget; and (ii) decrease the likelihood of being detected, as the channel is jammed for a shorter period of time. These key aspects make advanced jamming very stealthy yet exceptionally effective in practical scenarios. One of the fundamental challenges in designing defense mechanisms against an advanced jammer is understanding which jamming strategies yields the lowest throughput, for a given channel condition and a given amount of energy. To the best of our knowledge, this problem still remains unsolved, as an analytic model to quantitatively compare advanced jamming schemes is still missing in existing literature. To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks. We first mathematically model a number of advanced jamming schemes at the signal processing level, so that a quantitative relationship between the jamming energy and the jamming effect is established. Based on the model, theorems are derived on the optimal advanced jamming scheme for an arbitrary channel condition. The theoretical findings are validated through extensive simulations and experiments on a 5-radio 2x2 MIMO testbed. Our results show that the theorems are able to predict jamming efficiency with high accuracy. Moreover, to further demonstrate that the theoretical findings are applicable to address crucial real-world jamming problems, we show that the theorems can be incorporated to state-of-art reinforcement-learning based jamming algorithms and boost the action exploration phase so that a faster convergence is achieved.


@inproceedings{zhang2019jam,
  title={{Jam Sessions: Analysis and Experimental Evaluation of Advanced Jamming Attacks in MIMO Networks}},
  author={Zhang, Liyang and Restuccia, Francesco and Melodia, Tommaso and Pudlewski, Scott M},
  booktitle={Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)},
  pages={61--70},
  year={2019}
}

2019

Conference

DeepRadioID: Real-Time Channel-Resistent Optimization of Deep Learning-based Radio Fingerprinting Algorithms

F. Restuccia, S. D’Oro, A. Al-Shawabka, M. Belgiovine, L. Angioloni, S. Ioannidis, K. Chowdhury and T. Melodia

Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)

ABSTRACT BIBTEX HTML

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy by leveraging the unique hardware-level imperfections imposed on the received wireless signal by the transmitter's radio circuitry. Most of existing approaches utilize hand-tailored protocol-specific feature extraction techniques, which can identify devices operating under a pre-defined wireless protocol only. Conversely, by mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard. One of the most crucial challenges in radio fingerprinting is to counteract the action of the wireless channel, which decreases fingerprinting accuracy significantly by disrupting hardware impairments. On the other hand, due to their sheer size, deep learning algorithms are hardly re-trainable in real-time. Another aspect that is yet to be investigated is whether an adversary can successfully impersonate another device's fingerprint. To address these key issues, this paper proposes DeepRadioID, a system to optimize the accuracy of deep-learning-based radio fingerprinting algorithms without retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitter's side, we can apply tiny modifications to the waveform to strengthen its fingerprint according to the current channel conditions. We mathematically formulate the Waveform Optimization Problem (WOP) as the problem of finding, for a given trained neural network, the optimum FIR to be used by the transmitter to improve its fingerprinting accuracy. We extensively evaluate DeepRadioID on a experimental testbed of 20 nominally-identical software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices provided by the DARPA RFMLS program. Experimental results show that DeepRadioID (i) increases fingerprinting accuracy by about 35%, 50% and 58% on the three scenarios considered; (ii) decreases an adversary's accuracy by about 54% when trying to imitate other device's fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset.


@inproceedings{restuccia2019deepradioid,
  title={{DeepRadioID: Real-Time Channel-Resistent Optimization of Deep Learning-based Radio Fingerprinting Algorithms}},
  author={Restuccia, Francesco and D'Oro, Salvatore and Al-Shawabka, Amani and Belgiovine, Mauro and Angioloni, Luca and Ioannidis, Stratis and Chowdhury, Kaushik and Melodia, Tommaso},
  booktitle={Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)},
  pages={51--60},
  year={2019}
}

2019

Journal

IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility

F. Restuccia, P. Ferraro, S. Silvestri, S. K. Das, and G. Lo Re

IEEE Transactions on Mobile Computing

ABSTRACT BIBTEX HTML


@article{restuccia2018incentme,
  title={{IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility}},
  author={Restuccia, Francesco and Ferraro, Pierluca and Silvestri, Simone and Das, Sajal K and Re, Giuseppe Lo},
  journal={IEEE Transactions on Mobile Computing},
  volume={18},
  number={7},
  pages={1571--1584},
  year={2018},
  publisher={IEEE}
}

2019

Journal

Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach

L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski

IEEE Transactions on Mobile Computing

ABSTRACT BIBTEX HTML


@article{zhang2018taming,
  title={{Taming Cross-Layer Attacks in Wireless Networks: A Bayesian Learning Approach}},
  author={Zhang, Liyang and Restuccia, Francesco and Melodia, Tommaso and Pudlewski, Scott M},
  journal={IEEE Transactions on Mobile Computing},
  volume={18},
  number={7},
  pages={1688--1702},
  year={2018},
  publisher={IEEE}
}

2019

Conference

Hiding Data in Plain Sight: Undetectable Wireless Communications Through Pseudo-Noise Asymmetric Shift Keying

S. D’Oro, F. Restuccia, and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML

Undetectable wireless transmissions are fundamental to avoid eavesdroppers or censorship by authoritarian governments. To address this issue, wireless steganography “hides” covert information inside primary information by slightly modifying the transmitted waveform such that primary information will still be decodable, while covert information will be seen as noise by agnostic receivers. Since the addition of covert information inevitably decreases the SNR of the primary transmission, a key challenge in wireless steganography is to mathematically analyze and optimize the impact of the covert channel on the primary channel as a function of different channel conditions. Another core issue is to make sure that the covert channel is almost undetectable by eavesdroppers. Existing approaches are protocol-specific and thus their performance cannot be assessed and optimized in general scenarios. To address this research gap, we notice that existing wireless technologies rely on phase-keying modulations (e.g., BPSK, QPSK) that in most cases do not use the channel up to its Shannon capacity. Therefore, the residual capacity can be leveraged to implement a wireless system based on a pseudo-noise asymmetric shift keying (PN-ASK) modulation, where covert symbols are mapped by shifting the amplitude of primary symbols. This way, covert information will be undetectable, since a receiver expecting phase-modulated symbols will see their shift in amplitude as an effect of channel/path loss degradation. Through rigorous mathematical analysis, we first investigate the SER of PN-ASK as a function of the channel; then, we find the optimal PN-ASK parameters that optimize primary and covert throughput under different channel condition. We evaluate the throughput performance and undetectability of PN-ASK through extensive simulations and on an experimental testbed based on USRP N210 software-defined radios. Results indicate that PN-ASK improves the throughput by more than 8x with respect to prior art. Finally, we demonstrate through experiments that PN-ASK is able to transmit covert data on top of IEEE 802.11g frames, which are correctly decoded by an off-the-shelf laptop WiFi card without any hardware modifications.


@inproceedings{d2019hiding,
  title={{Hiding Data in Plain Sight: Undetectable Wireless Communications Through Pseudo-Noise Asymmetric Shift Keying}},
  author={D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={1585--1593},
  year={2019},
  organization={IEEE}
}

2019

Conference

The Slice Is Served: Enforcing Radio Access Network Slicing in Virtualized 5G Systems

Proceedings of IEEE International Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML


@inproceedings{d2019slice,
  title={The slice is served: Enforcing radio access network slicing in virtualized 5G systems},
  author={D’Oro, Salvatore and Restuccia, Francesco and Talamonti, Alessandro and Melodia, Tommaso},
  booktitle={IEEE INFOCOM 2019-IEEE Conference on Computer Communications},
  pages={442--450},
  year={2019},
  organization={IEEE}
}

2019

Conference

Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop

F. Restuccia and T. Melodia

Proceedings of IEEE Conference on Computer Communications (INFOCOM)

ABSTRACT BIBTEX HTML

The explosion of 5G networks and the Internet of Things will result in an exceptionally crowded RF environment, where techniques such as spectrum sharing and dynamic spectrum access will become essential components of the wireless communication process. In this vision, wireless devices must be able to (i) learn to autonomously extract knowledge from the spectrum on-the-fly; and (ii) react in real time to the inferred spectrum knowledge by appropriately changing communication parameters, including frequency band, symbol modulation, coding rate, among others. Traditional CPU-based machine learning suffers from high latency, and requires application-specific and computationally-intensive feature extraction/selection algorithms. Conversely, deep learning allows the analysis of massive amount of unprocessed spectrum data without ad-hoc feature extraction. So far, deep learning has been used for offline wireless spectrum analysis only. Therefore, additional research is needed to design systems that bring deep learning algorithms directly on the device's hardware and tightly intertwined with the RF components to enable real-time spectrum-driven decision-making at the physical layer. In this paper, we present RFLearn, the first system enabling spectrum knowledge extraction from unprocessed I/Q samples by deep learning directly in the RF loop. RFLearn provides (i) a complete hardware/software architecture where the CPU, radio transceiver and learning/actuation circuits are tightly connected for maximum performance; and (ii) a learning circuit design framework where the latency vs. hardware resource consumption trade-off can explored. We implement and evaluate the performance of RFLearn on custom software-defined radio built on a system-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers and VERT2450 antennas. We showcase the capabilities of RFLearn by applying it to solving the fundamental problems of modulation and OFDM parameter recognition. Experimental results reveal that RFLearn decreases latency and power by about 17x and 15x with respect to a software-based solution, with a comparatively low hardware resource consumption.


@inproceedings{restuccia2019big,
  title={{Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop}},
  author={Restuccia, Francesco and Melodia, Tommaso},
  booktitle={Proceedings of IEEE Conference on Computer Communications (INFOCOM)},
  pages={2152--2160},
  year={2019},
  organization={IEEE}
}

2019

Journal

FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems

F. Restuccia, P. Ferraro, S. Silvestri, T. S. Sanders, S. K. Das, and G. Lo Re

ACM Transactions on Sensor Networks

ABSTRACT BIBTEX HTML


@article{restuccia2018first,
  title={{FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems}},
  author={Restuccia, Francesco and Ferraro, Pierluca and Sanders, Timothy S and Silvestri, Simone and Das, Sajal K and Re, Giuseppe Lo},
  journal={ACM Transactions on Sensor Networks (TOSN)},
  volume={15},
  number={1},
  pages={1--35},
  year={2018},
  publisher={ACM New York, NY, USA}
}

2019

Journal

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

F. Restuccia, S. D’Oro, and T. Melodia

IEEE Internet of Things Journal

ABSTRACT BIBTEX HTML


@article{restuccia2018securing,
  title={{Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking}},
  author={Restuccia, Francesco and D’Oro, Salvatore and Melodia, Tommaso},
  journal={IEEE Internet of Things Journal},
  volume={5},
  number={6},
  pages={4829--4842},
  year={2018},
  publisher={IEEE}
}

2018

Journal

Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results

S. D’Oro, F. Restuccia, T. Melodia, and S. Palazzo

IEEE/ACM Transactions on Networking

ABSTRACT BIBTEX HTML


@article{d2018low,
  title={{Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results}},
  author={D’Oro, Salvatore and Restuccia, Francesco and Melodia, Tommaso and Palazzo, Sergio},
  journal={IEEE/ACM Transactions on Networking},
  volume={26},
  number={6},
  pages={2815--2828},
  year={2018},
  publisher={IEEE}
}

2018

Journal

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

F. Restuccia, N. Ghosh, S. Bhattacharjee, S. K. Das, and T. Melodia

ACM Transactions on Sensor Networks

ABSTRACT BIBTEX HTML


@article{restuccia2017quality,
  title={{Quality of Information in Mobile Crowdsensing: Survey and Research Challenges}},
  author={Restuccia, Francesco and Ghosh, Nirnay and Bhattacharjee, Shameek and Das, Sajal K and Melodia, Tommaso},
  journal={ACM Transactions on Sensor Networks (TOSN)},
  volume={13},
  number={4},
  pages={1--43},
  year={2017},
  publisher={ACM New York, NY, USA}
}

2017

Conference

iSonar: Software-defined Underwater Acoustic Networking for Amphibious Smartphones

F. Restuccia, E. Demirors, and T. Melodia

Proceedings of ACM International Conference on Underwater Networks & Systems (WUWNet)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2017isonar,
  title={{iSonar: Software-defined Underwater Acoustic Networking for Amphibious Smartphones}},
  author={Restuccia, Francesco and Demirors, Emrecan and Melodia, Tommaso},
  booktitle={Proceedings of ACM International Conference on Underwater Networks & Systems (WUWNet)},
  pages={1--9},
  year={2017}
}

2017

Conference

Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications

L. Zhang, F. Restuccia, T. Melodia, and S. Pudlewski

Proceedings of IEEE Conference on Communications and Network Security (IEEE CNS)

ABSTRACT BIBTEX HTML


@inproceedings{zhang2017learning,
  author={Zhang, Liyang and Restuccia, Francesco and Melodia, Tommaso and Pudlewski, Scott M.},
  booktitle={Proceedings of IEEE Conference on Communications and Network Security (IEEE CNS)}, 
  title={{Learning to Detect and Mitigate Cross-layer Attacks in Wireless Networks: Framework and Applications}}, 
  year={2017},
  volume={},
  number={},
  pages={1-9}
}

2017

Journal

Accurate and Efficient Modeling of 802.15.4 Unslotted CSMA/CA through Event Chains Computation

D. De Guglielmo, F. Restuccia, G. Anastasi, M. Conti, and S.K. Das

IEEE Transactions on Mobile Computing

ABSTRACT BIBTEX HTML


@article{de2016accurate,
  title={{Accurate and Efficient Modeling of 802.15. 4 Unslotted CSMA/CA through Event Chains Computation}},
  author={De Guglielmo, Domenico and Restuccia, Francesco and Anastasi, Giuseppe and Conti, Marco and Das, Sajal K},
  journal={IEEE Transactions on Mobile Computing},
  volume={15},
  number={12},
  pages={2954--2968},
  year={2016},
  publisher={IEEE}
}

2016

Journal

Optimizing the Lifetime of Sensor Networks with Uncontrollable Mobile Sinks and QoS Constraints

F. Restuccia and S. K. Das

ACM Transactions on Sensor Networks

ABSTRACT BIBTEX HTML


@article{restuccia2016optimizing,
  title={{Optimizing the Lifetime of Sensor Networks with Uncontrollable Mobile Sinks and QoS Constraints}},
  author={Restuccia, Francesco and Das, Sajal K},
  journal={ACM Transactions on Sensor Networks (TOSN)},
  volume={12},
  number={1},
  pages={1--31},
  year={2016},
  publisher={ACM New York, NY, USA}
}

2016

Journal

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

F. Restuccia, S.K. Das, and J. Payton

ACM Transactions on Sensor Networks

ABSTRACT BIBTEX HTML


@article{restuccia2016incentive,
  title={{Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges}},
  author={Restuccia, Francesco and Das, Sajal K and Payton, Jamie},
  journal={ACM Transactions on Sensor Networks (TOSN)},
  volume={12},
  number={2},
  pages={1--40},
  year={2016},
  publisher={ACM New York, NY, USA}
}

2016

Conference

Lifetime Optimization with QoS of Sensor Networks with Uncontrollable Mobile Sinks

Restuccia, Francesco, and Das, Sajal K

In 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2015lifetime,
  abbr = {Conference},
  title = {Lifetime Optimization with QoS of Sensor Networks with Uncontrollable Mobile Sinks},
  author = {Restuccia, Francesco and Das, Sajal K},
  booktitle = {2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  bibtex_show = {true},
  pages = {1--9},
  html = {https://ieeexplore.ieee.org/abstract/document/7158130},
  year = {2015},
  organization = {IEEE}
}

2015

Conference

Preserving QoI in Participatory Sensing by Tackling Location-Spoofing Through Mobile WiFi Hotspots

Restuccia, Francesco, Saracino, Andrea, Das, Sajal K, and Martinelli, Fabio

In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2015preserving,
  abbr = {Conference},
  title = {Preserving QoI in Participatory Sensing by Tackling Location-Spoofing Through Mobile WiFi Hotspots},
  author = {Restuccia, Francesco and Saracino, Andrea and Das, Sajal K and Martinelli, Fabio},
  bibtex_show = {true},
  booktitle = {2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)},
  pages = {81--86},
  year = {2015},
  html = {https://ieeexplore.ieee.org/abstract/document/7133998},
  organization = {IEEE}
}

2015

Conference

Preserving QoI in Participatory Sensing by Tackling Location-spoofing Attacks through Mobile WiFi Hotspots

F. Restuccia, A. Saracino, S. K. Das, F. Martinelli

Proceedings of the International Workshop on Information Quality and Quality of Service for Pervasive Computing (IQ2S)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2015preserving,
  title={{Preserving QoI in Participatory Sensing by Tackling Location-spoofing Attacks through Mobile WiFi Hotspots}},
  author={Restuccia, Francesco and Saracino, Andrea and Das, Sajal K and Martinelli, Fabio},
  booktitle={IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)},
  pages={81--86},
  year={2015},
  organization={IEEE}
}

2015

Journal

Analysis and Optimization of Protocol for Mobile Element Discovery in Sensor Networks

F. Restuccia, G. Anastasi, M. Conti, S. K. Das

IEEE Transactions on Mobile Computing

ABSTRACT BIBTEX HTML


@article{restuccia2013analysis,
  title={{Analysis and Optimization of a Protocol for Mobile Element Discovery in Sensor Networks}},
  author={Restuccia, Francesco and Anastasi, Giuseppe and Conti, Marco and Das, Sajal K},
  journal={IEEE Transactions on Mobile Computing},
  volume={13},
  number={9},
  pages={1942--1954},
  year={2013},
  publisher={IEEE}
}

2014

Conference

FIDES: A Trust-based Framework for Secure User Incentivization in Participatory Sensing

F. Restuccia and S. K. Das

Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2014fides,
  title={{FIDES: A Trust-based Framework for Secure User Incentivization in Participatory Sensing}},
  author={Restuccia, Francesco and Das, Sajal K},
  booktitle={Proceedings of IEEE International Symposium on a World of Wireless, Mobile, and Multimedia Networks (WoWMoM)},
  pages={1--10},
  year={2014},
  organization={IEEE}
}

2014

Conference

Performance Analysis of a Hierarchical Discovery Protocol for WSNs with Mobile Elements

F. Restuccia, G. Anastasi, M. Conti, S. K. Das

Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

ABSTRACT BIBTEX HTML


@inproceedings{restuccia2012performance,
  title={{Performance Analysis of a Hierarchical Discovery Protocol for WSNs with Mobile Elements}},
  author={Restuccia, Francesco and Anastasi, Giuseppe and Conti, Marco and Das, Sajal K},
  booktitle={Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},
  pages={1--9},
  year={2012},
  organization={IEEE}
}

2012

Conference

A Hybrid and Flexible Discovery Algorithm for Wireless Sensor Networks with Mobile Elements

K. Kondepu, F. Restuccia, G. Anastasi, M. Conti

Proceedings of IEEE International Symposium on Computers and Communications (ISCC)

ABSTRACT BIBTEX HTML


@inproceedings{kondepu2012hybrid,
  title={{A Hybrid and Flexible Discovery Algorithm for Wireless Sensor Networks with Mobile Elements}},
  author={Kondepu, Koteswararao and Restuccia, Francesco and Anastasi, Giuseppe and Conti, Marco},
  booktitle={Proceedings of IEEE International Symposium on Computers and Communications (ISCC)},
  pages={295--300},
  year={2012},
  organization={IEEE}
}

2012