Particle Swarm Optimization Based Approach to Solve the Multiple Sink Placement Problem in WSNs Haidar Safa, Wassim El-Hajj, Hanan Zoubian Computer Science Department American University of Beirut Beirut, Lebanon {haidar.safa, wassim.el-hajj, hgz01 }@aub.edu.lb Abstract—A wireless sensor network (WSN) is a collection of tiny and limited-capability sensor nodes that report their sensed data to a data collector, referred to as a sink node. WSNs are used in many applications, but are challenged by memory and energy constraints. To address these issues, solutions have been proposed on different levels including the topological level where multiple sinks can be used in the network to reduce the number of hops between a sensor and its sink node. Topological level solutions are very crucial in time-sensitive applications where the maximum worst case delay incurred by a message to get from a sensor to the corresponding sink should be minimal or at least less than a certain value. In turn, the maximum worst case delay can be minimized by choosing near optimal locations of the sinks. Consequently the network lifetime will be extended since the energy consumed by the sensor nodes will be reduced. In this paper, we propose an efficient and robust approach based on Particle Swarm Optimization (PSO) heuristic to solve the multiple sink placement problem; more specifically we use Discrete PSO (DPSO) with local search (LS). We start by formulating the problem then discretizing it and finally applying PSO while introducing local search to the inner workings of the algorithm. When compared to Genetic Algorithm-based Sink Placement (GASP), which is considered the state-of-the-art in solving the multiple sink placement problem, our approach improved the results in most scenarios while requiring less runtime. Keywords: Wireless Sensor Networks, Sink Placement Problem, Particle Swarm Optimization, Genetic Algorithms I. INTRODUCTION Wireless Sensor Networks (WSNs) are currently widely used in military applications, civil applications, habitat monitoring, and environmental observations. WSNs are composed of low-cost, low-power, multifunctional, and tiny sensor nodes that collect physical information, and forward them to sink nodes which act as data collectors. Sensors can be deployed densely and randomly in a certain area without predetermining their positions. The fact that requires them to exhibit self-configuring and self-organizing capabilities. The biggest challenge in WSNs remains to be the limited energy of the sensor nodes because sensor nodes act as data sources and routers at the same time. Moreover, the traffic load on the various sensors is not balanced since nodes near the sink get more depleted than others. To improve the use of available resources and increase the lifetime of WSNs, most researcher adapted approaches that aim to reduce the number of transmitted messages in the network [3-5, 20]. Such approaches include routing [6, 8-10], data reduction [7], event filtering [11, 12], load balancing, energy efficient circuitry, and topological control & planning [5]. In this paper, we suggest a technique that falls under the topological planning area where a certain number of sinks is added to the network for the purpose of making the network more manageable and prolonging its lifetime by reducing the energy dissipated at each sensor node. This problem is referred to as: multiple sink placement problem. Unfortunately, finding the appropriate number of sinks and their best locations is an NP-Hard problem, so no exact solution can be found for large WSNs. A good approximation algorithm can still produce good results leading to (1) better load balancing, (2) fewer number of intermediate hops needed to reach the closest sink, (3) less delay incurred by a message from a sensor to the corresponding sink, and (4) reduced amount of utilized resources. All these advantages translate to increased network lifetime. In this paper, we use an enhanced version of the Particle Swarm Optimization (PSO) to solve the sink placement problem in WSN. This enhanced version, Discrete PSO (DPSO) with local search (LS), is composed of (1) discretizing the sink placement problem, (2) using local search, and (3) applying PSO to find a solution. Our aim is to find the best combination of sink locations that minimizes the fitness function defined as “the maximum worst case delay in the network”. As shown later in the section V, our proposed approach outperforms a state of the art algorithm called Genetic Algorithm-based Sink Placement (GASP) [1, 2], which is usually used to solve the sink placement problem. PSO is a heuristic based on artificial life and evolutionary computing. Its behavior is different from pure evolutionary computing methods such as genetic algorithms (GA) and Evolutionary Simulated Annealing (ESA) and according to [27], it even performs better. In swarm intelligence (SI) [26, 28], the strategy changed from being based on competition as in GA and the focus became on a social model of optimization. If a particle’s (individual’s) neighbor has a better performance for a problem [25], the particle tries to imitate its neighbor. Basically, the individuals create a working social network where they collaborate together to get to a better result. The rest of the paper is structured as follows. In section II, we discuss the approaches suggested in literature to solve the sink placement problem. In section III, we present the analytical details needed to calculate the fitness function which is used as our evaluation metric. In section IV, we detail our IEEE ICC 2012 - Wireless Networks Symposium 978-1-4577-2053-6/12/$31.00 ©2012 IEEE 5445