This paper studies wireless sensor networks node deployment problem and proposes intelligent single particle optimizer based wireless sensor networks adaptive coverage. According to the probability model measure characteristic of wireless sensor nodes, the method adaptively determines the optimal deployment of sensor nodes using intelligent single particle optimizer, achieving sensor node based adaptive deployment. Simulation results show that the method has better coverage results and coverage than particle swarm optimization, shuffled frog leaping algorithm, ant colony optimization, and chaotic particle swarm optimization.
Aiming at the task allocation in multi-target tracking of wireless sensor networks, the discrete particle swarm optimization based on nearest-neighbor is presented to reduce the communication energy consumption between nodes. First, task allocation is initialized with nearest neighbor algorithm. Then the fitness function is compared through change task allocation matrix to achieve task allocation. Simulation results show that task allocation based on particle swarm optimization can effectively reduce communication energy consumption than nearest neighbor optimization in the relatively sparse nodes coverage.
Experiences of past earthquakes demonstrate that pipeline systems have no proper performance when exposed to severe earthquakes. In this study, sensor and damper placement approaches are presented for doing reliable health monitoring and seismic retrofitting of the piping networks. Since most of the available sensor placement methods are based on modal analysis results, the authors propose a new scheme that relies on the nonlinearity which utilizes nonlinear time history analysis results, and genetic algorithm is selected to act as the methodology of optimization as well. The results demonstrate that the proposed optimal sensor configuration strategy is more accurate and efficient than the extended modal assurance criterion method. To assess the number of sensors, a sensitivity analysis is undertaken in which the number of sensors computed optimally by the proposed algorithm contains the least convergence error. In addition, the number of iterations and the time consumed in the proposed approach are consi...