Many-Objective Evolutionary Algorithms for Optimization of Vehicle-Road Cooperation Systems Based on Intelligent Wireless Sensor Networks

The vehicle-road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multisource traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS...

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Veröffentlicht in:IEEE internet of things journal Jg. 12; H. 21; S. 44012 - 44024
Hauptverfasser: Liu, Tingting, Zhang, Lei, Ding, Xuefeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Constraints
Cooperation
Costs
Critical components
Energy consumption
Evolutionary algorithms
Genetic algorithms
Intelligent transportation
Intelligent transportation systems
Internet of Things
many-objective optimization
Monitoring
Multiple objective analysis
Optimization
Optimization models
Roads & highways
Routing
Routing protocols
Sensors
Strategy
Traffic information
Transportation networks
vehicle-road system
Wireless sensor networks
ISSN:2327-4662, 2327-4662
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Zusammenfassung:The vehicle-road cooperation system (VRCS) is set to be a critical component of intelligent transportation systems. The rapid and precise acquisition of substantial multisource traffic data is pivotal, with intelligent wireless sensor networks (IWSNs) emerging as a promising tool for developing VRCS. Nonetheless, the complexity of communication environment and the massive transmission of information have made VRCS optimization an increasingly difficult task, involving the intricate processes of coverage and routing optimization within IWSNs. This challenge essentially transforms into a very complicated many-objective optimization problem, where multiple conflicting objectives and resource constraints need to be tackled simultaneously. Traditional optimization methods struggle to offer satisfactory solutions. Our study introduces a many-objective evolutionary algorithm that concurrently manages coverage and routing optimization in IWSNs. Initially, we present a 3-D sensing model to delineate the perception range of an individual sensor. Subsequently, we propose a comprehensive four-objective optimization model, encompassing coverage, connectivity, energy consumption, and deployment cost, to accurately depict the performance of IWSNs. Especially this integrated model includes a swift connectivity evaluation method and a specialized routing forwarding strategy. Furthermore, we suggest a node sleeping strategy to minimize energy consumption further. To find a solution to the resultant many-objective optimization problem, which refers to numerous objectives and constraints, we developed a swarm optimization algorithm based on rapid fitness evaluation strategy and differential co-evolution, aimed at boosting convergence speed and accuracy of the proposed approach. Comparative experiments indicate that our model and algorithm outperform other algorithms in terms of efficiency and effectiveness.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3541185