Research On Target Coverage Method Based On Probabilistic Perception Model In Wireless Sensor Network

Target coverage is a basic problem in wireless sensor networks(WSNs).The previous study on target coverage was conducted mostly based on the 0/1 disk perception model that is an idealized monitoring model.In recent years,a probabilistic perception model that better accords with the actual application scenarios has been proposed.In the sensor network based on the probabilistic perception model,the target usually needs to be monitored by multiple sensors,so the 0/1 disk perception model is not suitable for the problem of probabilistic target coverage.In addition,the sensor nodes in traditional WSNs are powered by batteries with limited capacity,and energy limits the network lifetime.The energy of sensor nodes can be effectively supplemented as the energy harvesting and wireless charging technology has been gradually developed and applied.In this article,the problem of probabilistic target coverage in energy harvesting sensor network and mobile charging sensor network is studied,and some effective solutions are improved and proposed,respectively.The main research work of this article is as follows:(1)The problem of probabilistic target coverage in the energy harvesting sensor network is studied.Common sensors and solar sensors are deployed in the network.Solar sensors in different locations have different energy harvesting efficiency.The sensors adopt the probabilistic perception model for monitoring.This article takes maximizing the network lifetime as the optimization goal,and iteratively selects different sensor combinations.Each sensor combination enables all targets to reach the probability coverage requirement ?,and remains connected with the sink points at the same time.This article further proves that the problem is an NP-hard problem,and models it as a 0-1 integer linear programming model.To solve this problem,two heuristic algorithms are improved and proposed in this article,that is,network flow algorithm based on residual energy and greedy selection algorithm based on energy efficiency.Finally,the above algorithms are evaluated through a large number of simulation experiments.The experimental results show that the latter algorithm can better extend the network lifetime,which is approximately 14%to 27%higher than the PGS algorithm.(2)The instability of environmental energy and high production cost of energy harvesting nodes impedes the sensor networks from effective and sustainable operation.In this article the problem of probabilistic target coverage in the mobile charging sensor network is further studied.This article combines the probabilistic perception model with the adjustable perception radius,and solves the deployment problem of minimum ? cost for permanent target coverage.A mobile charger(MC)is used for periodic charging of sensor nodes in the mobile charging sensor network.The charging path is a shortest Hamiltonian circuit,and the MC has limited energy capacity.This article takes minimizing the deployment cost of the network as the optimization goal,deploying some sensors and turning on a suitable monitoring radius,so that all targets meet the probability coverage requirement ?,while keeping the entire network running permanently.This article proves that the minimum ?cost deployment problem is an NP-hard problem,and models it as a mixed integer nonlinear programming model.To solve this problem,a heuristic algorithm is designed in this article,which is a minimum cost deployment algorithm based on priority and bounded conditions and the effectiveness of the algorithm is verified in a series of simulation experiments.