American University of Beirut

Placing a certain number of sinks at appropriate locations in WSNs reduces the number of hops between a sensor and its sink resulting in less exchanged messages between nodes and consequently less energy consumption. Since finding the optimal number of the sinks to be added and their locations is an NP Hard problem, we propose in this paper, a topological level solution that uses a meta-heuristic based on Particle Swarm Optimization (PSO) to decide on the number of sinks and their locations; more specifically we use Discrete PSO (DPSO) with local search. Traffic Flow Analysis (TFA) is used to calculate the fitness function of the network defined as the maximum worst case delay. Since TFA is usually used to analyze networks with one sink, we present the extension that allows it to be used with multiple sinks. Furthermore, we formulated the problem, discretized it, and applied PSO while introducing local search to the inner workings of the algorithm. Extensive experiments were conducted to evaluate the efficiency of DPSO. DPSO was compared with Genetic Algorithm-based Sink Placement (GASP), which is considered the state-of-the-art in solving the multiple sink placement problem. In all scenarios, DPSO was 2 to 3 times faster than GASP. When compared with respect to delay, DPSO achieved less delay in most scenarios, except for few scenarios where it performed similar to GASP or a bit worst. Topologies with random as well as heavy tailed distribution were used in the experiments. Moreover, we present via simulation the substantial benefit of adding more sinks to a wireless network.