Survey of energy-efficient fog computing: Techniques and recent advances

Undoubtedly, energy efficiency forms a fundamental pillar of the fog computing model. Processing data at the network's edge leads to a substantial reduction in energy consumption when contrasted with the alternative of transmitting all data to remote data centers, typically associated with cloud computing. This energy-saving approach not only promotes a more environmentally friendly footprint but also serves to prolong the operational life of battery-powered IoT devices, a particularly critical aspect, especially in remote or challenging-to-access environments. Thus, fog computing plays a crucial role in the operation of massive energy-saving IoT or green IoT networks. This study offers a comprehensive survey of recent research endeavors focused on achieving energy-efficient fog computing and eco-friendly fog computing solutions for IoT networks. The article initiates with an introductory overview of fog computing and subsequently delves into an in-depth exploration of various energy-conservation techniques tailored for fog computing environments. These techniques encompass energy-conscious architectural designs, data aggregation and compression strategies, low-power hardware implementations, energy-aware scheduling methods, task offloading mechanisms, resource utilization optimization, virtualization techniques, and energy harvesting approaches. In addition, this investigation introduces novel methodologies and outlines prospective research pathways to bolster the energy efficiency of fog computing. Moreover, practical applications are presented to highlight the potential advantages and obstacles associated with deploying energy-conscious strategies, providing insights into their effectiveness and practical implications in real-world scenarios. Essentially, this article can be considered a roadmap towards the realization of a sustainable fog computing ecosystem for extensive IoT networks. In addition, opens the door for interested researchers to follow and continue the vision of energy-efficient computing.