Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

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Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data

To overcome the limited coverage in traditional wireless sensor networks, \emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives of user devices and incentive human involvement, this paper presents a novel approach that seamlessly integrates MCS with wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS), for supporting crowd sensing with energy consumption and offering rewards as incentives. The optimization problem is formulated to simultaneously maximize the data utility and minimize the energy consumption for service operator, by jointly controlling wireless-power allocation at the \emph{access point} (AP) as well as sensing-data size, compression ratio, and sensor-transmission duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the optimal power allocation policy is shown to have a \emph{threshold}-based structure with respect to a defined \emph{crowd-sensing priority} function for each MS. Given fixed sensing-data utilities, the compression policy achieves the optimal compression ratio. Extensive simulations are also presented to verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206

Joint Computation and Communication Cooperation for Mobile Edge Computing

This paper proposes a novel joint computation and communication cooperation approach in mobile edge computing (MEC) systems, which enables user cooperation in both computation and communication for improving the MEC performance. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite block, and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the computation and communication resource allocation at both the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. We provide the optimal solution to this problem. Numerical results show that the proposed joint cooperation approach significantly improves the computation capacity and the energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design.Comment: 8 pages, 4 figure

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications