A Novel Hybrid Approach for Optimizing the Localization of Wireless Sensor Networks

Wireless sensor networks are used for monitoring the environment and controlling the physical environment. Information gathered by the sensors is only useful if the positions of the sensors are known. One of the solutions for this problem is Global Positioning System (GPS). However, this approach is prohibitively costly; both in terms of hardware and power requirements. Localization is defined as finding the physical coordinates of a group of nodes. Localization is classified as an unconstrained optimization problem. In this work, we propose a new algorithm to tackle the problem of localization; the algorithm is based on a hybridization of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Simulation results are given to illustrate the robustness and efficiency of the presented algorithm.

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Weight Selection and PSO Based Three-Dimensional Localization for Wireless Sensor Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.2540 ◽

2013 ◽

Vol 427-429 ◽

pp. 2540-2544 ◽

Cited By ~ 1

Author(s):

Jia Liang Lv ◽

Ying Long Wang ◽

Huan Qing Cui ◽

Nuo Wei

Keyword(s):

Wireless Sensor Networks ◽

Particle Swarm Optimization ◽

Sensor Networks ◽

Optimization Problem ◽

Three Dimensional ◽

Wireless Sensor ◽

Swarm Optimization ◽

Localization Algorithms ◽

Key Technologies ◽

Simulation Results

Localization is one of the key technologies of wireless sensor networks, and the problem of localization is always formulated as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor networks. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a novel three-dimensional PSO method based on weight selection (WSPSO). Simulation results show that the proposed method outperforms standard PSO and existing localization algorithms.

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Towards Aid by Generate and Solve Methodology: Application in the Problem of Coverage and Connectivity in Wireless Sensor Networks

International Journal of Distributed Sensor Networks ◽

10.1155/2012/790459 ◽

2012 ◽

Vol 8 (10) ◽

pp. 790459 ◽

Cited By ~ 5

Author(s):

Placido Rogerio Pinheiro ◽

Andre Luis Vasconcelos Coelho ◽

Alexei Barbosa Aguiar ◽

Alvaro de Menezes Sobreira Neto

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Networks ◽

Genetic Algorithms ◽

Linear Programming ◽

Sensor Networks ◽

Optimization Problem ◽

Hybrid Approach ◽

Wireless Sensor ◽

Coverage And Connectivity ◽

Mathematical Programming Problems

The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recently introduced into the literature in order to solve the problem of dynamic coverage and connectivity in wireless sensor networks, showing promising results. The GS framework includes a metaheuristics engine (e.g., a genetic algorithm) that works as a generator of reduced instances of the original optimization problem, which are, in turn, formulated as mathematical programming problems and solved by an integer programming solver.

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