Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks
arXiv:2510.21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments. As such, wireless rechargeable sensor networks (WRSNs) with mobile charging capabilities offer a promising solution to extend network lifetime. However, WRSNs face critical challenges from the inherent trade-off between maximizing the node survival rates and maximizing charging energy ef
The increasing complexity and scale of sensor deployments necessitate more robust power solutions, and AI/ML is now mature enough to optimize these systems effectively.
This development improves the longevity and reliability of critical sensor networks, which are foundational for many emerging technologies and monitoring applications.
Sensor networks can now be deployed in remote or inaccessible areas with significantly reduced maintenance, extending their operational lifespan and data collection capabilities.
- · IoT device manufacturers
- · Remote monitoring industries
- · AI/ML research in optimization
- · Smart city developers
- · Battery manufacturing for sensor networks
- · Maintenance service providers for remote sensors
Increased penetration of sensor networks in previously unfeasible locations due to extended operational life.
Reduced operational costs for large-scale sensor deployments, accelerating data-driven insights in various sectors.
Enhanced real-time environmental monitoring or infrastructure management leading to more adaptive and resilient systems.
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Read at arXiv cs.AI