Collaborative offloading for UAV-enabled time-sensitive MEC networks

Abstract

Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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“…There are works concerned with optimizing force and trajectory [25,26]. Some components optimized the network's time or latency [27,28]. They may employ standard and conventional mathematical or cutting-edge techniques in these works.…”

Sustainable Internet of Vehicles System: A Task Offloading Strategy Based on Improved Genetic Algorithm

“…Considering the time-varying computing tasks and resource conditions, the Markov decision process was used to solve the mixed integer nonlinear programming problem, which effectively improved MEC computing task offloading efficiency. [18] studied a UAV-assisted multi-task MEC network considering the requirements of time-sensitive tasks. In addition to satisfying different task requirements, they effectively reduced the energy consumption of IoT devices in total.…”

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

“…The scheduling of computing resources, bandwidth allotment, and the UAV's trajectory were optimized in the minimization problem. The authors in [67] also studied the IoT devices' energy consumption minimization problem in a single UAV-assisted system with time-sensitive tasks. Interestingly, this study proposed the use of the UAV for both task computing and caching.…”