Maximal energy efficient task scheduling for homogeneous fog networks

In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency in homogeneous fog networks. With this model, the tradeoff between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the energy efficiency optimization problem for future intelligent internet of things (IoT) applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between energy efficiency and task scheduling performance in homogeneous fog networks. Compared with traditional strategies, the proposed MEETS algorithm can achieve much better energy efficiency performance under different network parameters and service conditions.