The Study Of Localization Algorithm Using Harmony Search In Wireless Sensor Network

As a relatively novel monitoring and information processing technology, Wireless Sensor Network(WSN) is widely used in agriculture, smart home, military, traffic and many other fields. Many applications of wireless sensor network need the location information of monitoring sensor node to make the monitoring data with significance. Currently, the traditional positioning methods using estimating distance or ranging technology have poor positioning accuracy. In recent years, heuristic optimization algorithm has gradually been used to solve the problem of the wireless sensor network node localization.This paper mainly studies Harmony Search Algorithm(HS) and Multi-objective Adaptive Harmony Search Algorithm——Non-dominated Sorting Adaptive Harmony Search Algorithm(NSAHS) applied in WSN node localization. The following three node localization algorithms are studied:(1) Study of a self-adaptive harmony search algorithm based on MCB(Monte Carlo boxed) for wireless sensor networks node localization. Based on the Sampling theory of MCB,the coordinates of unknown nodes are generated randomly under the restriction of the network topology constraints. In order to improve the search efficiency and the localization accuracy, the algorithm uses adaptive harmony memory considering rate(HMCR) and pitch adjusting rate(PAR). The results show that the algorithm can effectively address the flip ambiguity and improve the localization accuracy, and our approach outperforms the compared methods, in terms of both localization error and computational cost.(2) Study of the Localization Algorithm Based-on Two-hop Constraint and Adaptive Harmony Search Algorithm for Wireless Sensor Network. The localization algorithm formulates a function that concludes the constraint of two-hop neighbor reference nodes’ communication radius, and the unknown node’s position is estimated by applying adaptive harmony search algorithm to solve the minimum value of the function. Two-hop constraint to flip check is used to reduce the location errors in the localization algorithm, and the localization algorithm applies time round scheme to reduce the cumulative errors caused by iteratively localizing. The results show that the localization algorithm can improve the localization accuracy.(3) Study of the Localization algorithm based on cluster and multi-objective adaptive harmony search for wireless sensor network. In the proposed algorithm, clustering algorithm is adopted to reduce the complexity, and according to distance information and topological relations between the nodes, each cluster establishes local multi-objective model. A multi-objective adaptive harmony search algorithm, which uses the non-dominated sorting and crowding distance sorting algorithm in NSGAII to update the harmony memory, is adopted to solve multi-objective optimization problems. The simulation results show that the algorithm has high location accuracy.