Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (extended version) (1507.04094v2)

Abstract: 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 computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

• The paper presents a unified framework that optimally balances local computation powered by harvested energy and task offloading via wireless energy transfer.
• It employs convex optimization techniques to derive closed-form control policies that maximize the probability of successful computation under strict energy constraints.
• Numerical results demonstrate significant energy savings over baseline approaches, highlighting the system’s scalability and practical efficiency in varying network conditions.

Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer

This paper presents an innovative approach to enhance energy efficiency in mobile cloud computing by integrating microwave power transfer (MPT) and computation offloading into a unified framework. The authors target the persistent challenge of limited battery life in mobile devices, proposing a system where a base station (BS) either powers a mobile device through MPT or offloads computation tasks to the cloud, optimizing energy utilization in either case.

The key aspect of the framework is its ability to determine the optimal strategy for computation, whether locally on the device or via offloading, based on channel state information (CSI) and CPU-cycle statistics. The proposed model employs convex optimization techniques to derive closed-form solutions for optimum control policies, aiming to maximize the probability of successful computation under specific energy constraints.

System and Methodology

The proposed system comprises a multi-antenna BS and a single-antenna mobile device. The BS, part of a cloud network, uses beamforming to either transfer power or relay computation tasks. The mobile device, leveraging harvested energy, can choose between local computation, powered by MPT, or offloading tasks to the cloud.

The authors divide the problem into two primary modes:

1. Local Computing: The mobile computes data tasks using harvested energy, optimizing CPU-cycle frequencies to minimize energy consumption within a deadline.
2. Offloading: The mobile offloads tasks to the BS, optimizing the time allocation between MPT and communication to enhance energy savings.

For local computation, the energy constraints create a non-convex optimization problem. The authors address this by employing convex relaxation techniques, ensuring the solution is both tractable and optimal. Contrarily, offloading involves optimizing transmission durations using a derived threshold on transmission power and channel gain, solved using a convex optimization approach.

Numerical Results and Implications

The paper provides substantial numerical evidence demonstrating the feasibility and efficiency of the proposed system. The optimal control policies show significant energy savings over baseline approaches, like using fixed CPU frequencies or equal time partitioning for MPT and offloading. This efficiency grows with the increasing channel bandwidth or BS transmit power, offering a scalable solution adaptable to varying network configurations.

Theoretical Implications: By integrating MPT with mobile cloud computing, the paper lays the groundwork for enhanced energy efficiency protocols in future mobile networks. As mobile computation and communication requirements grow, these findings could pave the way for more sustainable network architectures.

Practical Implications: In scenarios such as IoT networks or wearable technology, these solutions contribute to extending device operational lifetime and reducing reliance on battery replacement or wired charging. The framework's adaptability to different network conditions also promises broader applicability across diverse technological landscapes.

Future Research Directions

The paper opens several avenues for future exploration:

1. Full-Duplex Transmission: Implementing full-duplex systems could allow simultaneous MPT and offloading, potentially enhancing efficiency.
2. Multi-task Programs: Extending the framework to multi-task environments could improve task partitioning and resource allocation strategies.
3. Multi-user Systems: Further research could explore multi-user scenarios, necessitating more complex resource allocation strategies under shared network conditions.

Overall, this paper makes a significant contribution to the field of wireless energy transfer and mobile cloud computing, offering an adaptable and efficient solution to the challenge of energy consumption in mobile devices.