Research On Subset Of Coverage In Wireless Sensor Networks

Coverage control is a basic problem in WSN(Wireless Sensor Networks). The network quality of service depends on the network coverage control which has effect on the cost of network and performance of application. This thesis aims at solving coverage problem from the aspect of optimizing subset of coverage, namely how to schedule nodes’ status between working and sleeping to find the minimum cost subset of coverage in the premise of ensuring the network coverage in the target area that nodes deploy randomly with limited energy. The subset of coverage can both enhance the performance of WSN and prolong network lifetime. Here are main contents completed:(1)Maximum-minimum subset dispatching algorithm based on respective biogen which is called MMDARB is investigated. This algorithm improves jenga-inspired optimization algorithm existed, namely JOA, which considers the biogen of each player’s independent sensor subset as working nodes’ screening factor to find a maximum disjoint and minimum cost set of nodes by roulette wheel selection. And it can prolong network lifetime. Experimental results show that this algorithm utilizes resource efficiently to prolong the network lifetime and has a better performance in residual energy and network lifetime compared with the existing GREEDY algorithm, particle swarm optimization algorithm named PSO and JOA algorithm under the same experimental conditions by using independence of biogen between sensor nodes.(2)Balanced rate area coverage algorithm, namely BRACA, is designed. This algorithm introduces the idea of balanced coverage rate. It considers the balance of sensors to target area coverage rate and the residual energy as filtering factors. By adjusting the ratio of sensor’s residual energy to its average coverage rate, a minimum cost subset to cover the area of interesting is obtained by nodes as few as possible. Experimental results show that this algorithm has a better performance in network lifetime and running time compared with the existing PSO algorithm and MMDARB algorithm under the same experimental conditions.(3)Area coverage algorithm based on differential evolution, namely ACADE, is designed. Based on parameterless mutation binary differential evolution, this algorithm presents a repair strategy to let each subset of coverage to meet the coverage constraint conditions. The strategy considers the balance of sensors to target area coverage rate and the residual energy as repairing factors with operation of mutation, crossover and selection to expand population search scope and speed up the convergence. Experimental results show that this algorithm has a better performance in network lifetime and average residual energy compared with the existing PSO algorithm, MMDARB algorithm and BRACA algorithm under the same experimental conditions. And this algorithm not only consumes less and more balanced energy, but also prolongs the network lifetime.