Research Of DV-Hop Algorithm For Wireless Sensor Networks

Wireless Sensor Network is a self-organized multi-hop network which is composed of tensof thousands of sensor nodes that can detect and sense the objective world and have the abilityto communicate through radio、 ultrasound、 infra-red with each other. It has a broadapplication prospect in earthquake monitoring、 Animal habitat research、 deep seaexploration、precision agriculture、smart home and other fields and will have a significantimpact on human life and production. Sensor nodes access to their own position is the basis inmany applications of WSN, Otherwise, the application and the user will not be able to knowwhere the thing has happened and the information of the nodes collected will lose value. Inaddition, the functions of network configuration、node management、topology control androuting generation also need the support of node location information, so the node localizationtechnology in wireless sensor networks is a very important and key thing.After years of research and development, a lot of node localization technology has beenproposed, but the relationship between positioning accuracy and implementation cost is stilllike the fish and bear’s paw, the localization technology which has a high precision depends onthe support of costly and power-hungry measuring module, so it is necessary to research thenode localization technology which have low cost and high precision.This paper deeply studies the principle of DV-Hop positioning algorithm and analyzes theextrinsic and intrinsic sources of error, then summarizes the research status of the algorithmthrough accessing to relevant literatures. In order to reduce the intrinsic error and improve thepositioning accuracy, firstly, the improved DV-Hop algorithm based on hop optimization anditerative weighted least squares algorithm (TDDV-Hop) is proposed, secondly, the improvedDV-Hop algorithm based on RSSI and optimization of artificial fish swarm algorithm (RPFDV-Hop)is proposed.The improvements of TDD-Hop algorithm are as follows: Firstly, based on the relationshipof the similarities and differences of neighbor set with the distance between nodes optimizes thehop. Secondly, the unknown node uses the information of several nearly beacons to estimate theaverage distance of each hop, then using weighted methods to calculate the distance betweenthe unknown node and beacon node. Thirdly, the choice of last equation in equations isoptimized. Finally the iterative weighted least square estimation method is used to calculate thecoordinate of unknown nodes. Simulation results show that the stability and positioningaccuracy is obviously higher than the original algorithm and the other two improved algorithms.The improvements of RPFD-Hop algorithm are as follows: Firstly, based on the Mean Shiftalgorithm filters the RSSI values, beacon nodes calculate and broadcast the average distance perhop and the average path loss per hop, all nodes use the broadcasting infromation to estimatethe path loss exponent based on the least square estimation method, then optimize the hopbetween nodes using the distance calculated based on the model of log-normal distribution.Secondly, the beacon nodes calculate the average distance of each hop and the unknown nodes estimate the average distance of each hop with different beacon nodes based on weightedmethod. Finally, the improved artificial fish swarm algorithm based on particle swarmoptimization algorithm is used to compute the coordinate of unknown nodes. Simulation resultsshow that the performance of improved algorithm is greatly improved as a result of applicationof RSSI ranging technology and intelligent optimization algorithm.