Neuromorphic and Bio-Inspired Computing for IoT

Abstract

The Internet of Things (IoT) has emerged as a transformative technology, connecting billions of devices across multiple domains. However, the exponential growth of IoT devices has raised significant challenges in terms of energy efficiency, latency, and computational complexity. Traditional computing architectures struggle to cope with these constraints due to their sequential and von Neumann design limitations. Neuromorphic and bioinspired computing, which emulate the human brain's structure and processing methods, provide a promising alternative. This review paper explores the principles of neuromorphic and bio-inspired computing, their integration with IoT systems, and their potential in enhancing performance, energy efficiency, and adaptability. Recent developments in hardware design, spiking neural networks, memristor-based systems, and hybrid architectures are discussed. Additionally, key applications in smart homes, healthcare, environmental monitoring, and autonomous systems are highlighted. Finally, challenges and future directions in this emerging field are presented.

KEYWORDS: IoT, Neuromorphic Computing, Bio-Inspired Computing, Spiking Neural Networks, Energy Efficiency, Memristors, Edge Computing.