MMRL: A Multi-Modal Reinforcement Learning Technique for Energy-efficient Medical IoT Systems

Abstract

The Internet of Medical Things (IoMT) couples the rapid growth of Internet of things (IoT) technologies with smart health systems, leveraging wireless battery-operated devices for remote health monitoring. Since 2019, a surge in the number of COVID-19 patients has increased rapidly, leading to increased strain on hospital resources and leaving some urgent patients behind. This is substantial cause to transform interactive health treatment into intelligent healthcare using edge computing and artificial intelligence (AI) techniques. However, running sophisticated AI-based edge computing techniques on IoT devices with limited battery is not sustainable. Hence, addressing the trade-off between energy-efficiency and smart AI techniques is imperative to maximize the device's lifetime. This paper proposes a Multi-Modal Reinforcement Learning (MMRL) algorithm that will help maximize the IoT device's lifetime using adaptive data compression, energy-efficient communication, and minimum latency, particularly for emergency events. The results showed a 500% longer battery life than the state-of-the-art algorithms in addition to high adaptability to different conditions.