Wireless Sensor Network Node Deployment For The Purpose Of Low Consumption

As the nodes of wireless sensor network are deployed with random and densedistribution, Some coverage holes and multiple coverage will happen in the monitoring area.This will directly affect the reasonable allocation in node energy, communication bandwidthand processing capacity on the condition of limited resources. Therefore, the coveragedeployment is a basic problem to be solved for wireless sensor network.A three-dimension wireless sensor nodes deployment method for low energy isproposed and applied in the underwater. The main works of the paper are as follows:(1) Considering the characteristics of sensor network static nodes’ random deployment inthree-dimensional space, and based on the optimal coverage node set being met, the paperproposes a node deployment method in which the radius is adjustable and enventually thenetwork energy will be lowered. This method tends to seek an optimal node set with lessof nodes, low energy consumption, and high network coverage. According to the fact thatthe method tends to node has the adjustable sensing radius, and after using genetic algorithmto optimize the node radius combination. Compared with the simulation results and theinitial node deployment, The results of simulation show the coverage rate rises from78%to92.15%. Node dormancy rate increased from60%to71.25%. Energy consumptioncoefficient reduced from0.155to0.135. Based on the results, considering the singleobjective genetic algorithm optimization sub-goals conflict with limitations, this paperstudies a coverage method based on multi-objective genetic algorithm (NSGAII). This studyusing multi-objective genetic algorithm aims to optimize the coverage in three-dimensionalnetwork, work node numbers, and the equilibrium coefficient of network energyconsumption. The simulation results of pareto optimal solution set show that the maximumcoverage can reach93.35%, the maximum node dormancy rate can be76.25%, and theminimum energy consumption coefficient can be reduced to0.2033, and the pareto optimalsolution set is better than the NSGA algorithm.(2)Because of the shortcomings of basic PSO in the optimization, in this paper aiming at thethree-dimensional dynamic sensor networks, we research the dynamic cover which is based onthree-dimensional network of QPSO, and we combine quantum theory with particle swarmoptimization algorithm in order to avoid the problem of particle swarm optimization easy tofall into the premature convergence. Simulation results show that the particle swarm coverage increased from7%to9%, while the quantum particle swarm increased from7%to10%. The average travel distance quantum particle swarm is22.12m, the average traveldistance for the particle swarm is24.73m. Based on the results, considering the situation ofthe practical application and the particularity of nodes only vertical move in the underwaterenvironment, we have studied a distributed coverage control algorithm which is suitable forunderwater wireless sensor networks to increase coverage and maintain the connectivity ofnetwork. Simulation results show that the maximum increase of the coverage rate compared withrandom deployment method. in the same case and with the same node sensing radius. are respectively40%and30%.