Synchronous Wireless Sensor and Sink Placement Method Using Dual-Population Co-evolutionary Constrained Multiobjective Optimization Algorithm

Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 19; H. 6; S. 7561 - 7571
Hauptverfasser: Yu, Qianqian, Yang, Chen, Dai, Guangming, Peng, Lei, Chen, Xiaoyu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.06.2023
Schlagworte:
Constraints
Effectiveness
Energy consumption
Evolutionary algorithms
Integer programming
Microbalances
Mixed integer
Multiple objective analysis
Network reliability
Pareto optimization
Placement
Structural health monitoring
Wireless sensor networks
ISSN:1551-3203, 1941-0050
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Zusammenfassung:Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are critical constraints that determine the lifetime of wireless sensor networks. In this study, OWSP was formulated as a constrained multi-objective optimization problem with mixed-integer programming. Accordingly, a dual-population constrained multiobjective optimization (DCCMO) algorithm, which includes new crossover and mutation operators, was developed. In DCCMO, weak cooperation between two offspring populations is exploited to improve the efficiency of the solution search. The performance of DCCMO was compared to that of five other state-of-the-art algorithms using numerical examples with varying network parameters. DCCMO not only successfully matches the constrained Pareto front but also balances energy consumption and information effectiveness while exhibiting greater diversity and faster convergence than all other tested algorithms.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3211853