Rechargeable wireless sensor network deployment optimization considering road traffic equilibrium and charging vehicle routing

Minghua Zeng; Danye Wang; Rong Zhang; Wei Xu; Ni Dong; Minghua Zeng; Danye Wang; Rong Zhang; Wei Xu; Ni Dong

doi:10.3934/jimo.2026162

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 10: 4676-4695. doi: 10.3934/jimo.2026162

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Research article

Rechargeable wireless sensor network deployment optimization considering road traffic equilibrium and charging vehicle routing

1.
School of Automotive and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2.
School of Traffic and Transportation Engineering, East China Jiao Tong University, Nanchang 330013, China
3.
Xinjiang Vocational & Technical College of Communications, Urumqi, Xinjiang 831400, China
4.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

Received: 27 March 2026 Revised: 19 July 2026 Accepted: 20 July 2026 Published: 02 September 2026
90B50, 90B20, 90B06

Fully rechargeable wireless sensor networks (WSNs) serve as crucial infrastructure for the reliable collection and transmission of real-time road traffic information in emerging smart transportation systems. We study the optimization of rechargeable WSN layout, where sensors are recharged by mobile charging vehicles (MCVs). We propose a bi-level programming model for WSN layout optimization. The upper level minimizes sensor energy consumption subject to constraints including data flow balance and a sensor number budget. The lower level comprises two parallel models (MCV routing for sensor recharging and automated vehicle traffic flow equilibrium) that significantly affect optimal WSN deployment. A heuristic solution method is designed by integrating particle swarm optimization, depth-first search, and the Frank–Wolfe algorithm. Numerical experiments verify the model and method's effectiveness, yielding optimal deployment schemes for WSNs and meaningful results, such as a lower initial state of charge, a bigger recharging threshold; recharging priorities reduce optimal WSN energy consumption but increase MCV energy consumption.
- Deployment optimization,
- rechargeable WSN,
- MCV routing,
- bi-level programming,
- particle swarm optimization,
- traffic assignment
Citation: Minghua Zeng, Danye Wang, Rong Zhang, Wei Xu, Ni Dong. Rechargeable wireless sensor network deployment optimization considering road traffic equilibrium and charging vehicle routing[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 4676-4695. doi: 10.3934/jimo.2026162

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Abstract

Fully rechargeable wireless sensor networks (WSNs) serve as crucial infrastructure for the reliable collection and transmission of real-time road traffic information in emerging smart transportation systems. We study the optimization of rechargeable WSN layout, where sensors are recharged by mobile charging vehicles (MCVs). We propose a bi-level programming model for WSN layout optimization. The upper level minimizes sensor energy consumption subject to constraints including data flow balance and a sensor number budget. The lower level comprises two parallel models (MCV routing for sensor recharging and automated vehicle traffic flow equilibrium) that significantly affect optimal WSN deployment. A heuristic solution method is designed by integrating particle swarm optimization, depth-first search, and the Frank–Wolfe algorithm. Numerical experiments verify the model and method's effectiveness, yielding optimal deployment schemes for WSNs and meaningful results, such as a lower initial state of charge, a bigger recharging threshold; recharging priorities reduce optimal WSN energy consumption but increase MCV energy consumption.

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© 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)