Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

By Changsheng You, Kaibin Huang, Hyukjin Chae. IEEE Journal on Selected Areas in Communications, 2015

414 citations recorded by the source corpus.

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e78163c115aacfcb26d5f583a0e69975942e774f

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Citations in the corpus

Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling (2017, background): “[11] proposed a framework where a MD can harvest energy from a base station or offload to it.”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, background): “, a high-speed multicore CPU, is available at the MEC server, and thus ignore its execution delay [19], [21], [22], [33].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, background): “, the fraction of workload to be offloaded [137], [138].”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, neutral): “Moreover, dynamic computation offloading policies have been developed for MEC systems powered by energy harvesting [19] and wireless power transfer [20], for applications where replacing/recharging the device batteries is costly and difficult.”
Joint computation and communication cooperation for mobile edge computing (2017, background): “, [3]–[10] and the references therein) investigating the joint computation and communication optimization to improve the performance of MEC.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “This framework was further developed in [81] where both the local comput-”
Joint offloading and computing optimization in wireless powered mobile-edge computing systems (2017, methodology): “We set the same computation task of R = Ri input bits for all users, Ci = 10(3) cycles/bit, and κi = 10−28 [7].”
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “single-user mobile cloud computing system is investigated in [11], which includes the evaluation of the optimal CPU cycle frequencies for local computing and time division for offloading to adapt to the transferred power, based on the convex optimization theory.”
Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading (2017, background): “On the other hand, to reduce the total energy consumption under a given latency requirement, the authors in [8] have derived the optimal threshold-based offloading policy with joint communication and computation resource allocation and the authors in [9] have proposed the optimal mode selection betw”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, methodology): “This study was later extended in [8], where a closed-form optimal mode selection between local computing and cloud computing with the microwave power transfer technique was developed.”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “How does one effectively manage computation, communication, and storage resources between different players in smart cities to optimize the overall system performance? To answer this question, we must devise resource management schemes that consider energy [156]–[159], latency [160], traffic handlin”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, background): “, by more than three orders of magnitude [6], [9].”
Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing (2018, background): “To characterize the most essential computation and communication tradeoff and as commonly adopted in [10]–[16], we consider that the number of CPU”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, background): “In addition, the works in [11]–[14] introduced the use of energy harvesting or wireless power transfer (WPT) technologies into the cellular-based MEC systems, which has enabled the UEs to have sustainable energy support to their transmissions and computation, but at the cost of increasing the comput”
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “Similar to [13]–[16], each user can simultaneously perform energy harvesting, local computing and computation offloading while the UAV can simultaneously transmit energy and perform computation.”
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading (2017, background): “There are few existing studies on wireless powered MEC system [18]–[20].”
Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, methodology): “They are integrated with the technologies of powering mobiles by wireless energy transfer in [5] and energy harvesting in [6] to further enhance mobile energy savings.”
Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach (2019, background): “Many significant issues in MEC have been extensively investigated, including offloading task model [3], [4], energy efficiency [5]–[7], latency reduction [8]–[10], and joint optimization of communication and computing [11], [12].”