Rademics Research Institute

Abstract

The Internet of Things (IoT) represents a transformative shift in the way devices communicate and interact within an interconnected ecosystem. As the demand for real-time data processing and efficient communication escalates, edge computing emerges as a pivotal innovation, enhancing the performance and scalability of IoT systems. This chapter explores the crucial role of edge computing in shaping future IoT communication strategies, emphasizing its impact on data processing, latency reduction, security enhancements, and the integration of artificial intelligence. By decentralizing data management and enabling localized processing, edge computing optimizes bandwidth utilization and fosters intelligent decision-making at the device level. Additionally, the chapter addresses the challenges associated with implementing edge computing solutions, such as interoperability standards and infrastructure investments. Through a comprehensive analysis, this work aims to provide insights into how edge computing can drive innovation in IoT communication, ultimately facilitating the development of more resilient, efficient, and secure IoT ecosystems. 

Introduction

The IoT represents a paradigm shift in technology and communication, fundamentally altering the way devices connect and interact within a vast, interconnected ecosystem [1]. The concept of IoT encompasses a diverse array of devices, sensors, and actuators that communicate with one another through the internet, enabling a seamless exchange of data [2]. As IoT devices proliferate across various sectors, including healthcare, transportation, and industrial automation, the demand for efficient communication protocols becomes paramount [3]. This chapter delves into the role of edge computing in shaping the future of IoT communication strategies, underscoring its significance in enhancing data processing, reducing latency, and fostering intelligent decisionmaking [4-6]. As IoT ecosystems expand, the volume of data generated continues to escalate exponentially [7]. Traditional cloud computing architectures struggle to cope with this surge, leading to increased latency and bandwidth constraints [8]. Edge computing addresses these challenges by bringing computation and data storage closer to the source of data generation [9]. By processing data locally, edge devices can deliver immediate insights and actions, which was particularly crucial for applications requiring real-time responses, such as autonomous vehicles and smart city infrastructures [10-12]. This localized approach not only alleviates the burden on centralized cloud systems but also ensures that critical information can be accessed and acted upon without delay [13].