Energy Efficient Mobile Edge Computing strategies using Data Compression and Hierarchical Fog Logics

Abstract

With advancement in technology, mobile devices becomes more smarter that provide various functionality to user whether it will be in term of face detection app with various functionality or augmented  reality app. But these applications contain computation intensive task that consume more energy of mobile device. Mobile edge computing (MEC) emerges as attractive solution where computation and storage task is performed at edge devices rather than mobile devices. But in order leverage the benefit of both mobile cloud computing (MCC) and mobile edge computing (MEC) we proposed Hierarchical network of fog computationlinked with cloud structure. We propose an energy efficient multi-user Fog/cloud model in which Fog server and cloud server collaboratively work and perform the computation of offloading task.  For utilizing channel bandwidth in more efficient way with security and efficient resource allocation, we propose joint data compression encryption approach with heuristic algorithm based radio and resource allocation. We also propose offloading decision strategy model for deciding which task is executed locally at mobile device itself and which is offloaded remotely at fog or cloud for computation. We demonstrate simulation result and performance analysis comparison with existingbenchmark methodology in term of energy consumption, throughput, network performance, and frame success ratio.