Optimizing the Communication Cost in Energy Efficient IoT Devices through an Adaptive Algorithm for Swarm Robotics

The exponential growth of the Internet of Things (IoT) has led to an urgent need for highly energy-efficient communication strategies, especially for battery-powered or self-sustaining devices. In this work, we present a comprehensive framework for minimizing communication energy in IoT nodes operating in swarm robotic systems. We examine and integrate multiple low-power wireless technologies (BLE, LoRaWAN, MQTT, CoAP) with advanced Medium Access Control (MAC) protocols. We additionally propose adaptive scenarios leveraging both ambient energy harvesting and passive backscatter transmission. Our solution employs adaptive scheduling and dynamic transmission power management. Specifically, a Deep Q-Learning (DQL) agent dynamically adjusts transmission parameters based on the current energy condition. We develop analytical power consumption models incorporating equations for duty cycling, listening energy, and transmission energy. We then test the framework on a prototype IoT rover. Our experiments demonstrate that our optimizations extend device lifetime by up to 25% while maintaining rapid and reliable data transmission. The proposed methods substantially improve the balance between energy consumption and communication performance. This represents a significant advancement toward extending the operational lifetime of IoT and swarm robotic systems.