On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems
Abstract : Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, leading to better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.
? Most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions.
? The research that considers three or more targets does exist, but it is seldom compared with those considering one or two targets.
? According to the existing performance of WOA, an improvement that combines WOA with GWO is proposed, and it is given the name GWO-WOA.
? With the existing applications of GWO, it has been proved to have superior exploitation, good exploration ability, and high local optima avoidance.
? The objective of the data offloading problem is to minimize the amount of consumed energy, while the objective of energy harvesting is to maximize the received energy.
? We treat these two problems separately with independent objectives, one of minimizing the energy consumption for computation offloading, and the other of maximizing the received (charged) energy for wireless charging.
? We design novel and efficient sequential algorithms to solve these problems.
? Based on the way these two problems are formulated, PCO will be solved first to obtain the optimal data partitioning and time allocation for computation offloading.
• Mobile cloud computing (MCC) is proposed to break through the barrier between the request for complex applications and restricted resources.
• The model of computation offloading is proposed, the next step is to find out the proper and effective method to obtain the computation offloading decisions.
• The goal for our improvement on the algorithm is to propose an algorithm with better performance in the application.
• Then, normalization is proposed to be used in the model with the purpose of improving the model and eliminating the effects of dimensions. The goal of the model is to get the minimum value.
? We provide detailed quantitative performance analysis and study the impact of different system parameters and optimizing variables on the energy consumption and wireless charging performance.
? We show that data partitioning is a key variable affecting system energy consumption, while latency is paramount for wireless charging performance.
? However for faster machines, such as MEC servers, with the high-performance CPUs, this time-step may be significantly smaller.
? We observe no difference in the performance for small network sizes, however, for large network, having no transmit power control leads to slightly more data offloaded to the MEC-AP, hence increasing the energy consumption.
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