| With the development of Micro-Electro-Mechanical Systems, wireless communication and digital electronics technology, wireless sensor network composed of sensor nodes which are of low cost, Low power and multifunction has developed in a high speed. Generally, WSN is composed of massive mobile sensor nodes. Sensor nodes always are deployed on self-service physical environment without continual power, so sensor nodes cannot choose but depend on own or independent sources of energy (battery, solar energy). The limited energy hinders the development of wireless sensor networks. Therefore, the efficient use of energy in wireless sensor networks becomes the key issues. Network deployment energy consumption is closely related to sensor node moving distance, this thesis Makes a thorough study on this issue.This thesis describes the basic principles of Ant Colony Optimization (ACO) and analyses the advantages and disadvantages of that ACO solves wireless sensor network deployment issue, Double Limited Ant Colony Optimization(DLACO) is proposed based on ACO. DLACO limits the update path of the pheromone for improving the convergence speed, limits the maximum of pheromone for avoiding early-maturing. This thesis describes the basic principles of Particle Swarm Optimization (PSO), combined with ACO, Ant Colony Optimization-Particle Swarm Optimization (ACO-PSO) is proposed. The algorithm redefines the speed and position of the PSO for solving the discrete problem, full uses the fast convergence of PSO for finding the second-best solution, thus affects the initial pheromone distribution and then take full advantage of the positive feedback of ACO to improve the solution efficiency. ACO, DLACO, ACO-PSO are applied to simulation experiment based on shortest path networks deployment, simulation results show that the DLACO and ACO-PSO could cover the area effectively and could also reduce the movement distance of the mobile nodes in different scope problems. The algorithms have the better adaptability. |
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