A Research On Node Coverage Optimization Algorithm In Wireless Sensor Network

As an emerging mobile communication technology,wireless sensor network has brought broad prospects for the development of wireless communication and Internet of Things industries.However,the pros and cons of network coverage will directly affect the performance of wireless sensor network.Effective network coverage can not only improve the monitoring capabilities of the network,but also enhance network performance and prolong network life.Based on the existing virtual force coverage algorithm,this thesis uses the Received Signal Strength Indicator to construct a new virtual force model,and separately studies two cases of signal regular transmission and irregular signal transmission.At the same time,the energy-saving problem after the monitoring area is covered is optimized to extend the life cycle of the network.This thesis mainly completes the following aspects:(1)Aiming at the problem of node mobility and continuous oscillation caused by interaction between nodes in the original virtual force coverage algorithm,a coverage optimization algorithm based on the Received Signal Strength Indicator is proposed.The algorithm uses the Received Signal Strength Indicator value that can be obtained without additional configuration to construct a new virtual force coverage model,reducing the amount of node calculations.At the same time,the whale swarm algorithm of rectangular optimization method is introduced to expand the optimization range when the node continuously oscillates,and accelerate the node optimization speed,.Simulation results show that the algorithm can effectively reduce the number of node oscillations and moving distance,improve the convergence speed and stability of the algorithm,the network coverage rate reaches more than 95%.(2)Aiming at the problem of irregular signal transmission range of sensor nodes caused by network environment,a coverage optimization algorithm based on Radio Irregularity Model is proposed.Based on the coverage optimization algorithm based on the Received Signal Strength Indicator,the algorithm takes into account the anisotropy,continuous variability and non-uniformity of the wireless signal in the transmission process,so the signal irregularity model is introduced to simulate transmission characteristics of wireless signal in the actual environment.The coverage model only uses the signal strength value received by the sensor nodes to construct a virtual force coverage model,so it is applicable to all frequency bands.In this thesis,two signal attenuation models with working frequencies of 2.4GHz and 6GHz are selected as examples to construct virtual force coverage model to verify the feasibility of the coverage algorithm.The results show that all have good covering performance.At the same time,the feasibility and applicability of the coverage algorithm under the signal irregularity model are further verified in the three scenarios of no obstacles,walls and obstacle areas..(3)Aiming at the problem of high network node communication redundancy resulting in high network energy consumption,a coverage optimization algorithm based on concise-connected dominating set is proposed.On the basis of the coverage of the monitoring area,the virtual backbone network is formed by constructing a connected dominance set,which reduces network energy consumption and improves the life cycle of the network.Due to randomness of node numbering in the original connected dominance set generation algorithm and imperfection of the reduction rules,there are too many redundant nodes in the connected dominance set.Therefore,the eigenvector centrality value related to the network topology information is used as the unique id number of the node,while adding and perfecting the reduction rules in the original algorithm to reduce the number of node in the connected dominating set,which simplifies the structure of the virtual backbone network.Simulation results show that the algorithm can effectively reduce the number of dominant nodes while maintaining network connectivity,thereby reducing network energy consumption and prolonging the service life of the network..