Energy Efficient Computation Offloading in Mobile Edge Computing

This book provides a comprehensive review and in-depth discussion of energy-efficient computation offloading and resources management for mobile edge computing (MEC). It proposes an energy-efficient dynamic computing offloading scheme,a computation offloading and frequency scaling scheme,a delay-aware and energy-efficient computation offloading scheme,and a multi-task computation offloading scheme in multi-access MEC via NOMA. It is useful for researchers, professionals, and advanced level students in electrical and computer engineering, telecommunications, computer science, or other related disciplines.

Format: Hardback
Length: 156 pages
Publication date: 31 October 2022
Publisher: Springer International Publishing AG

This book offers a comprehensive and in-depth exploration of the cutting-edge research literature in the field of mobile edge computing (MEC), focusing on energy-efficient computation offloading and resource management strategies. It delves into various aspects, including task offloading, channel allocation, frequency scaling, and resource scheduling. Recognizing the dynamic nature of task arrival processes and channel conditions, the authors propose an energy-efficient dynamic computing offloading scheme to minimize energy consumption and ensure optimal end-device delay performance. To enhance energy efficiency further, they introduce a computation offloading and frequency scaling scheme that jointly addresses the stochastic task allocation and CPU-cycle frequency scaling, aiming to minimize energy consumption while maintaining system stability. Additionally, the book investigates delay-aware and energy-efficient computation offloading in dynamic MEC systems with multiple edge servers, employing an end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resources. Furthermore, it explores multi-task computation offloading in multi-access MEC scenarios, considering non-orthogonal multiple access (NOMA) and accounting for time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn near-optimal offloading solutions. This book is invaluable for researchers, practitioners, and advanced-level students in mobile edge computing, task offloading, resource management, electrical and computer engineering, telecommunications, computer science, and related disciplines. It serves as a valuable reference for professionals working in these fields.

Weight: 430g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031168215
Edition number: 1st ed. 2022