[논문]Accurate Range-free Localization Based on Quantum Particle Swarm Optimization in Heterogeneous Wireless Sensor Networks

Wu, Wenlan; Wen, Xianbin; Xu, Haixia; Yuan, Liming; Meng, Qingxia

doi:10.3837/tiis.2018.03.006

Accurate Range-free Localization Based on Quantum Particle Swarm Optimization in Heterogeneous Wireless Sensor Networks 원문보기

KSII Transactions on internet and information systems : TIIS, v.12 no.3, 2018년, pp.1083 - 1097

Wu, Wenlan (Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology) , Wen, Xianbin (Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology) , Xu, Haixia (Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology) , Yuan, Liming (Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology) , Meng, Qingxia (Tianjin University, School of Computer Science and Technology)

Abstract ▼ AI-Helper

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

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문제 정의

In multi-hop HWSN localization, the goal is to estimate the location of all UNs by using ANs and partial information of the distances between some pair of ANs and UNs. For the sake of simplicity, we employ the shortest path method to obtain a possible path between a source sensor and a destination sensor with the minimum number of hops.

제안 방법

By avoiding complex conditional CDF formulas exploited in reference 10 and reference 11, the obtained EHP in this paper is higher in efficiency, lower in energy consumption, and easy to calculate. In addition, the proposed method is more accurate for the situation of low network node density and low proportion of beacon nodes. Unfortunately, With the increase of sensor nodes density, positioning accuracy is not satisfactory comparing with the existed EHP method for HWSN.
Up to now, no method can obtain the satisfying localization results for HWSN. In this paper, to optimize the positioning algorithm, we propose a novel localization algorithm based on EHP and QPSO in the realm of multi-hop range-free localization schemes. Firstly, we assume that transmission coverage of each node is circular, i.
In addition, QPSO algorithm which complies with the heterogeneous nature of WSNs is proposed successfully to optimize the coordinates of UNs. The proposed algorithm outperforms the state-of-the-art range-free ones in terms of localization accuracy and energy consumption.

이론/모형

QPSO transforms the node localization problem into the optimization problem. In the process of searching, the mean square error is used to be the fitness function of QPSO [21-23]. It is required to minimize this fitness function by multiple iterations.

성능/효과

7, we analyze the computational complexity of the localization algorithms. Experimental result indicates that among the localization approaches, energy consumption of the proposed algorithm is between EHP approach and classical approaches (i.e. Amorphous, DV-hop and LAEP). However, Fig.
8 plots the localization NRMSE and energy consumption in different density sensor nodes, respectively. The simulation results show that QPSO algorithm has better convergence accuracy and higher evolution velocity compared with PSO algorithm.

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DOI : 10.3837/tiis.2018.03.006
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