Energy and task completion time trade-off for task offloading in fog-enabled IoT networks

Abstract

In order to improve the quality of experience in executing computation-intensive tasks of real-time IoT applications in a fog-enabled IoT network, resource-constrained IoT devices can offload the tasks to resource-rich nearby fog nodes. It causes a reduction in energy consumption compared with local processing, although it extends task completion time due to communication latency. In this paper, we propose a task offloading scheme that optimizes task offloading decision, fog node selection, and computation resource allocation, investigating the trade-off between task completion time and energy consumption. Weighting coefficients of time and energy consumption are determined based on specific demands of the user and residual energy of devices’ battery. Accordingly, we formulate the task offloading problem as a mixed-integer nonlinear program (MINLP), which is NP-hard. A sub-optimal algorithm based on the hybrid of genetic algorithm and particle swarm optimization is designed to solve the formulated problem. Extensive simulations prove the convergence of the proposed algorithm and its superior performance in comparison with baseline schemes.