**Edge Computing Technologies**

The utilization of edge computing technologies presents a multitude of benefits that have the potential to revolutionize various industries and enhance the overall digital ecosystem. Firstly, one of the key advantages lies in the significant reduction of latency. By processing and analyzing data closer to the source, edge computing minimizes the time it takes for data to travel to and from the cloud. This is particularly crucial for applications requiring real-time or near-real-time responses, such as autonomous vehicles, industrial automation, and immersive virtual reality experiences. The reduced latency not only improves the overall user experience but also enables time-sensitive decision-making, leading to increased efficiency and productivity.

Secondly, edge computing offers enhanced security and privacy. With traditional cloud computing, sensitive data often has to be transmitted and stored in remote data centers, raising concerns about potential security breaches or unauthorized access. Edge computing mitigates these risks by keeping data closer to its origin, reducing

the exposure to external threats during transmission. Additionally, by enabling local processing, edge computing allows for data to be anonymized or encrypted at the edge devices themselves, further bolstering privacy protection. This capability is particularly relevant in industries handling sensitive data, such as health care and finance, where compliance with stringent data protection regulations is paramount. Whether you are an industry professional, an academic researcher, or simply curious about the cutting-edge advancements shaping our digital landscape, this book provides a comprehensive guide to understanding and harnessing the potential of edge computing.

systems, and predictive maintenance in industrial settings. The combination of edge computing and AI/ML also reduces the need for constant data transmission to the cloud, optimizing bandwidth usage and minimizing latency, which is critical for

Another promising integration lies in the convergence of edge computing and the Internet of Things (IoT). The IoT ecosystem is characterized by a vast network of interconnected devices that generate and exchange large volumes of data. By integrating edge computing with IoT, data can be processed, filtered, and analyzed at the edge devices themselves, reducing the need for data transmission to centralized cloud servers. This enables faster response times, improved scalability, and enhanced reliability of IoT applications. For instance, in smart cities, edge computing can enable real-time analysis of sensor data to optimize traffic management, energy consumption, and waste management systems. Similarly, in health care, edge computing integrated with IoT devices can support remote patient monitoring, real-time diagnostics, and timely

This edited book is organized into two sections with eleven chapters. The first section on "Edge Computing" looks generally at edge computing technologies like cloud, fog, network, and swarm computing. Chapter 1, "Cloud Computing to Fog Computing: A Paradigm Shift", provides an overview of healthcare services using cloud and fog computing. Chapter 2, "Perspective Chapter: A View – Cloud-Edge Computing Technology " looks at different types of cloud computing and edge computing services. Chapter 3, "Network Powered by Computing: Next Generation of Computational Infrastructure", deals with the convergence of data communications, cloud, and high-performance computing. Chapter 4, "Perspective Chapter: Cloud Lock-in Parameters – Service Adoption and Migration", highlights technical advancements that contribute to interoperable migration in heterogeneous cloud environments. The first section ends with Chapter 5, "Swarm Computing: The Emergence of a Collective Artificial Intelligence at the Edge of Internet", which provides a compre-

intervention, enhancing the delivery of healthcare services.

hensive vision of the key aspects of the swarm computing paradigm.

The next section is on "Edge Computing Applications". Edge computing has been applied to control unmanned aerial vehicles (UAVs), IoT, digital epidemiology, hyperparameter tuning, 5G emerging technologies, and the Industrial Internet of Things (IIoT). In Chapter 6, "Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems", the authors propose a methodology to significantly improve the system's performance. Chapter 7, "IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card", deals with optimizing battery life methods for microcontrollers. Chapter 8,

"Perspective Chapter: 5G Enabling Technologies – Revolutionizing Transport, Environment, and Health", examines how 5G technologies can revolutionize transport, health, and the environment. Chapter 9, "Federated Learning Hyper-Parameter Tuning for Edge Computing", proposes to facilitate the integration of federated learning and edge computing by optimizing federated learning hyper-parameters, which can significantly reduce training overhead and make it more affordable for edge computing Chapter 10, "Perspective Chapter: Edge Computing in Digital

time-sensitive applications.

**Organization of the Book**
