UAV-Assisted Wireless Powered Cooperative Mobile Edge Computing: Joint Offloading, CPU Control, and Trajectory Optimization

Abstract

This paper investigates the UAV-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power and the UAV's trajectory. To tackle the non-convex problem, a successive convex approximation (SCA)based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. Simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieves the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve the smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes. And, by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.

Privacy-Aware Wireless Power Transfer for Aerial Computation Offloading via Colonel Blotto Game

“…In addition, each tier is composed of 120×120 square tile grids with the unit length of one side, i.e., 1m. All the AUEs adopt the Ka-band sharing the same 8,8) , (15,20)] and the acceleration vector a[k] are always set to (0, 0). The positive weighting factor are set to λ 1 =0.9, and λ 2 =10 −15 , respectively.…”

“…An attractive solution is to apply far-field wireless power transfer (WPT) to provide a controllable, sustainable, and longer-range energy supply for the AUEs [5]. Recently, the integration of far-field WPT into the MEC system has gained many research interests, aiming to improve the system performance by optimizing task offloading and resource allocation jointly [6]- [8]. However, these works mainly focus on the use of radio frequency (RF)based WPT at the terrestrial or aerial infrastructure to prolong the lifetime of energy-limited UEs on the ground.…”

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

“…And an energy-effective cooperative resource allocation policy for the WPT-MEC system was also proposed in [16] to guarantee the fairness of mobile users. In addition, Liu et al in [17] presented a UAV-assisted WPT-MEC system, and studied the joint optimization of task offloading, CPU control, and trajectory design. There are still some existing works considering cache-aided WPT-IoT networks.…”

Joint Stochastic Computational Resource and UAV Trajectory for Wireless-Powered Space-Air-Ground IoRT Networks

“…where g U k denotes the small-scale fast fading power component following the unit mean exponential distribution [14] [16]. A is the path loss constant.…”

“…Recently, a UAV-enabled WPT architecture has been proposed, which employs the UAV as an energy transmitter to provide energy for ground equipment. This framework endows the system with excellent performance in improving the computing capability [7] [11], increasing the system capacity [12] and reducing the energy consumption of the system [13] [14] [15]. Considering the imperfect ground infrastructure in the IoRT network, it is necessary to develop UAV-enabled WPT edge computing system owing to the flexible deployment and low operation cost of the UAV.…”