How can agent-based simulation optimize UAV battery recharging in IoT?
Agent-Based Simulation of UAV Battery Recharging for IoT Applications: Precision Agriculture, Disaster Recovery, and Dengue Vector Control
This paper explores using multi-agent simulation (specifically agent-based modeling) to optimize battery recharging for a swarm of drones in IoT applications like precision agriculture, disaster recovery, and mosquito control. It proposes a decentralized approach where drones decide individually whether to recharge or continue working, inspired by the El Farol Bar problem. Two recharging policies are compared: a simple baseline based on battery level and a more sophisticated one incorporating predictions of other drones' behavior.
Key points for LLM-based multi-agent systems: Decentralized decision-making without communication reduces overhead, inductive reasoning (as seen in the El Farol Bar problem) can be a useful model for agent behavior, and the presented simulation framework provides a basis for experimenting with different agent policies and environmental parameters. The framework also shows how the balance between exploration (continuing work) and exploitation (recharging) affects overall swarm performance.