An Energy Efficient Design of Computation Offloading Enabled by UAV

Abstract

The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

Multi-user multi-objective computation offloading for medical image diagnosis

“…Recently, computation offloading has received more and more attention as one of the most promising solutions to this issue, and various offloading strategies have been proposed ( Mao, Zhang & Letaief, 2016 ; Zhang et al, 2017a , 2019a ; Guo, Li & Guan, 2019 ; Li et al, 2019b , 2020b , 2020a ; Messous et al, 2019 ; Meng et al, 2019 ; Mitsis, Tsiropoulou & Papavassiliou, 2020 ; Zhu et al, 2020a , 2020b ; Alioua et al, 2020 ; Tang & Wong, 2022 ; Wang et al, 2021 ; Chen & Liu, 2021 ). The differences between various computation offloading methods are shown in Table 1 .…”

“… Zhang et al (2019a) , Li et al (2020b) , Wang et al (2021) , Chen & Liu (2021) are offloading strategies to reduce energy consumption. For instance, Wang et al (2021) proposed a trajectory control algorithm based on convex optimization and deep reinforcement learning by combining the motion trajectory, user association, and resource allocation of UAVs.…”

Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

“…A greedy search based on nonconvex optimization is proposed with the aim of minimizing the weighted sum energy of the UAV and the mobile devices by jointly optimizing the computational resource scheduling, allocation of bandwidth resources, and trajectory of the UAV. In Li et al (2020b) , an energy-efficient, UAV-based, offloading architecture that optimizes the bit allocations in different regions of user tasks and the trajectory of the UAV using successive approximation is proposed. However, the proposed scheme does not address how to partition the fixed clustered slots, and further, the time delay is not considered in the proposed scheme.…”

HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet

“…When computation tasks are offloaded to the edge server, extra transmission delay will also be generated, except for inherent processing latency and energy consumption. Therefore, the trade-off between latency and energy consumption is not only one of the main goals for task offloading but also an important metric for evaluating the performance of an MEC system [ 9 , 10 , 11 ]. However, task offloading decisions in MEC are easily affected by many uncertain factors, such as unstable mobile wireless channels, resulting in unpredictable latency and more energy consumption caused by unnecessary task re-transmission, which may seriously degrade the system performance [ 12 , 13 ].…”