A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization. (13th July 2021)

• Record Type: Journal Article
• Authors: Han, Dezhi; Wang, Jing; Tang, Canren; Weng, Tien‐Hsiung; Li, Kuan‐Ching; Dobre, Ciprian
• Is Part Of: International journal of communication systems. Volume 34:Number 14(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-13
• Subjects: DV‐HOP -- energy consumption monitoring -- quantum particle swarm optimization -- the Internet of Things -- wireless sensor networks; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4924
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Wireless sensor networks (WSNs) have actively been considered in vast amount of applications in fields of science and engineering. The node location technology is one of the most critical technologies of WSNs. Aiming at the problem of distance vector‐hop (DV‐HOP) algorithm's excessive estimation error, we propose in this article a multi‐objective DV‐HOP localization algorithm based on differential evolution quantum particle swarm optimization (DQPSO‐DV‐HOP). First, the set of anchor nodes generated during the deployment phase that would cause large errors is eliminated, and a correction factor is introduced to modify the average hop distance to reflect the actual situation of the network better. In the node localization phase, the objective function we propose is optimized under a combination of the DE and QPSO algorithms, so the estimated results of unknown nodes are optimized and modified by using the QPSO algorithm of fast convergence, which is easy to converge to the optimal global value. Simulation results show that the localization stability, accuracy, and convergence given by the proposed DQPSO‐DV‐HOP algorithm are better than other schemes. High precision positioning algorithm can improve the accuracy of energy consumption monitoring and provide more accurate data for energy saving management. Abstract : The set of anchor nodes generated during the deployment phase that would cause large errors is eliminated. A correction factor is introduced to modify the average hop distance to reflect the actual situation of the network better. In the node localization phase, the objective function we propose is optimized under a combination of the differential evolution (DE) and quantum particle swarm optimization (QPSO) algorithms, so the estimated results of unknown nodes are optimized and modified by using the QPSO algorithm of fast convergence, which is easy to converge to the optimal global value.