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

In past literature, it has been demonstrated that the use of mobile sinks (MSs) increases dramatically the lifetime of wireless sensor networks (WSNs). In applications where the MSs are humans, animals, or transportation systems, the mobility of the MS is often random and unpredictable, implying the necessity of novel and specific algorithms able to deal with large uncertainty on the MS mobility. In this paper, we define the yet unsolved problem of optimizing the lifetime of a WSN in the presence of uncontrollable and random sink mobility with QoS constraints. Then, we present a novel Swarm-Intelligence-based Sensor Selection Algorithm (SISSA), which optimizes network lifetime and meets pre-defined QoS constraints. Next, we mathematically analyze SISSA and derive analytical bounds on energy consumption, number of messages exchanged, and convergence time. The efficiency of SISSA and the accuracy of the model are experimentally evaluated with a testbed composed by 40 sensors, and the network lifetime provided by SISSA is compared to that by an ideal scheme. Experimental and analytical results conclude that SISSA is highly scalable and energy-efficient, and provides on the average the 56% of the lifetime provided by the ideal scheme in all the considered network parameter sets.