Towards Optimizing Communication Cost in Energy Efficient IoT Devices for Swarm Robotics

Abstract

The rapid expansion of the Internet of Things (IoT) has led to an increasing demand for energy-efficient communication strategies, particularly for battery-operated or energy-efficient devices. This paper explores optimization techniques for enabling energy-efficient IoT device communication, ensuring uninterrupted operation by balancing energy consumption. We investigate adaptive transmission strategies and various communication protocols to optimize energy usage while maintaining reliable data exchange. Our approach leverages dynamic power control and energy-aware scheduling to enhance communication efficiency through the use of DQL(Deep Q Learning) for parameter optimization. Experimental results and simulations demonstrate the effectiveness of these optimization techniques in prolonging network lifespan and ensuring sustainable IoT deployments for swarm robotics. A comparative evaluation with traditional IoT communication protocols showed that our optimization mechanism extended device lifetime by up to 25 percent, while achieving a balance between energy efficiency and data reliability.