Dynamic Offloading for Energy Harvesting Mobile Edge Computing: Architecture, Case Studies, and Future Directions

Bin Li, Zesong Fei, Jian Shen, Xiao Jiang, Xiaoxiong Zhong
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Mobile edge computing (MEC) is envisioned as a new paradigm by integrating the mobile computing functionality into 5G wireless networks, aiming at empowering communication networks with low-latency services. In general, mobile devices have finite battery lifetime (e.g., machine-type devices) and the energy harvesting is advocated to provide perpetual energy supply for achieving sustainable operation, which is very important for facilitating sustainable computing in future applications. In this
more &raquo; ... aper, we propose a wireless powered MEC network architecture that employs device-to-device (D2D) communications underlaying heterogeneous networks (HetNets) to enable the computational tasks offloading to resource-rich edge servers. A dynamic offloading decision is made to execute the computation tasks. Then, we focus on the energy-efficient offloading scheme, and joint offloading and user association scheme. From the illustrative results, we provide insights for the design of this new network architecture. Furthermore, several open research topics are discussed. INDEX TERMS Mobile edge computing, energy harvesting, device-to-device (D2D) communication, computation offloading. Due to the limited processing power and storage capacity of mobile devices, running computation-intensive applications for resource-poor devices in upcoming 5G Internet of Things (IoTs) cannot be completed in real-time. Consequently, the tension between resource-hungry applications and resource-constrained devices arises a significant design challenge. It is advocated that the computation-intensive tasks can be offloaded to a centralized cloud, such a paradigm is generally known as mobile cloud computing [5], [6] . However, there exists an inherent limitation in cloud computing, namely, the long propagation distance from the end user to the remote cloud center. This may fail to catch up the requirements of delay sensitive applications (e.g., speech/face recognition, immersive gaming and augmented reality (AR) application) because of the high communication latency and possible congestions between mobile devices and the VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
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