Energy-efficient computation offloading and resource allocation in fog computing for Internet of Everything

With the dawning of the Internet of Everything (IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation (ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.

[1]  Caijun Zhong,et al.  Symbol Detection of Ambient Backscatter Systems With Manchester Coding , 2018, IEEE Transactions on Wireless Communications.

[2]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[3]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[4]  Rajkumar Buyya,et al.  Fog Computing: Principles, Architectures, and Applications , 2016, ArXiv.

[5]  Eui-Nam Huh,et al.  Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm , 2016, IEEE Potentials.

[6]  Xiao Ma,et al.  Cost-efficient workload scheduling in Cloud Assisted Mobile Edge Computing , 2017, 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS).

[7]  Shengli Xie,et al.  Cognitive machine-to-machine communications: visions and potentials for the smart grid , 2012, IEEE Network.

[8]  Caijun Zhong,et al.  Wireless Information and Power Transfer in Relay Systems With Multiple Antennas and Interference , 2015, IEEE Transactions on Communications.

[9]  Meixia Tao,et al.  Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-Latency , 2018, IEEE Transactions on Wireless Communications.

[10]  Wenzhong Li,et al.  Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.

[11]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[12]  Yan Chen,et al.  Study of Connectivity Probability of Vehicle-to-Vehicle and Vehicle-to-Infrastructure Communication Systems , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).

[13]  Jiajia Liu,et al.  Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks , 2018, IEEE Transactions on Vehicular Technology.

[14]  Weiwei Xia,et al.  An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing , 2017, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).

[15]  Di Liu,et al.  A key management scheme based on hypergraph for fog computing , 2018, China Communications.

[16]  Yunyi Liu,et al.  A Dual-Link Soft Handover Scheme for C/U Plane Split Network in High-Speed Railway , 2018, IEEE Access.

[17]  Weiwei Xia,et al.  Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks With Mobile Edge Computing , 2018, IEEE Access.

[18]  Yifan Yu,et al.  Mobile edge computing towards 5G: Vision, recent progress, and open challenges , 2016, China Communications.

[19]  Ke Zhang,et al.  Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things , 2018, IEEE Communications Magazine.