A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization

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 e...

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Bibliographic Details
Published in:International journal of communication systems Vol. 34; no. 14
Main Authors: Han, Dezhi, Wang, Jing, Tang, Canren, Weng, Tien‐Hsiung, Li, Kuan‐Ching, Dobre, Ciprian
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 25.09.2021
Subjects:
Algorithms
Convergence
DV‐HOP
Energy consumption
energy consumption monitoring
Evolutionary algorithms
Evolutionary computation
Localization
Nodes
Particle swarm optimization
quantum particle swarm optimization
the Internet of Things
Wireless sensor networks
ISSN:1074-5351, 1099-1131
Online Access:Get full text
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Summary: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. 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.
Bibliography:Funding information
National Natural Science Foundation of China; National Natural Science Foundation of China, Grant/Award Numbers: 61672338, 61873160
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content type line 14
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.4924