Internet of things (IoT) are envisioned to be an essential in our daily lives, but most IoT devices (IDs) are battery powered and have limited resource. Recently, fog computing (FC) has been proposed to support IoT systems, where part or all of the data are offloaded from IDs to fog nodes for processing or computation. In this paper, we propose to optimise the partial computation offloading in an OFDMA based FC IoT system, while ensuring fairness among IoT links with respect to their energy consumption. In particular, we minimize the energy consumption of the worst-case link by jointly optimizing the size of offloaded data and the assignment of subcarriers, while guaranteeing the rate requirement. The formulated min-max energy efficiency optimization problem (MEP) is solved using Lagrangian dual decomposition and subgradient projection, bases on which we propose an iterative algorithm. Our simulation results show that the proposed resource allocation algorithm is more energy efficient than the existing algorithms for FC supported IoT, while achieving fairness among IoT links.

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2018 IEEE International Conference on Communications, ICC 2018
School of Information Technology

Feng, J. (Jie), Zhao, L. (Liqiang), Du, J. (Jianbo), Chu, X. (Xiaoli), & Yu, F.R. (2018). Energy-efficient resource allocation in fog computing supported IoT with min-max fairness guarantees. In IEEE International Conference on Communications. doi:10.1109/ICC.2018.8422317