Agent-Based Simulation of UAV Battery Recharging for IoT Applications: Precision Agriculture, Disaster Recovery, and Dengue Vector Control

TL;DR

The paper tackles UAV battery autonomy in IoT contexts by decoupling drone communication during recharging decisions and applying an El Farol Bar inspired policy. It develops an ABMS implementation in NetLogo with drones as agents, a limited capacity charging station, and two policies BL and CT, where CT uses past recharge history via predictors to forecast demand. Through 6000 simulation runs across 60 scenario sets with 100 repetitions each, CT shows more reliable results under extreme usage, while BL remains reasonable in milder regimes. The findings suggest that decentralized, history-informed recharging strategies can extend swarm endurance for precision agriculture, disaster response, and dengue control, providing a potential baseline for future policy improvements.

Abstract

The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.

Figures (14)

• Figure 1: Graphical representation of UAV architectures. Based on: Fahlstrom2012
• Figure 2: Four types of Multi-UAV architectures. Based on: Valavanis2015
• Figure 3: Battery exchange techniques, based in Campi2019a.
• Figure 4: Agents’ decision process in each simulation cycle.
• Figure 5: Past, present, and future of the agriculture technology, based on Fathallah2017.