Privacy-preserving and energy efficient task offloading for collaborative mobile computing in IoT: An ADMM approach

Abstract The pervasive application of Internet of Things (IoT) has pushed the proliferation of edge computing. There exists potential in edge computing to satisfy the concerns of task delay, network bandwidth, battery endurance and data privacy. The superiority of edge computing is endorsed by the deployment of edge nodes to the device user’s proximity. Within partial capabilities of the cloud server, edge nodes efficaciously alleviate the burden on core network. Considering a mission critical system, enduring battery life is even more accentuated over task latency to maintain the device on operation. So in this paper, we put forward an energy efficient task offloading problem subject to the overall task delay based on Alternating Direction Method of Multipliers (ADMM) in a three-tier MEC network, equipped with both edge nodes and the cloud. The offloading choice is the approximate convergence with demanded precision concluded by persistent ADMM iterations. We also address the privacy disclosure concerns in the data transmission among IoT devices and apply differential privacy to the intricate optimization problem. More specifically, we associate privacy-preserving method with the exhaustive task offloading processes and iteration procedures. At last, simulations and experiments demonstrate the performance and convergence of our proposed algorithm.