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}}), resource allocation at UAV (mathbf {P}_{mathbf {R}}) and offloading decisions at IoT devices (mathbf {P}_{mathbf {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.
Highlights
With the rapid development of internet of things (IoT), numerous novel applications and services, such as augmented/virtual reality (AR/Virtual reality (VR)), face recognition and e-health, have emerged and interacted with each other [1], which start demanding more on computing capability, network throughput and latency for better quality of service (QoS) [2, 3]
To find a way out of this dilemma, mobile edge computing (MEC) has been envisioned as the paradigm by placing servers with rich services in proximity to IoT devices, which can remarkably improve the quality of experience (QoE) of IoT devices and help to reduce the energy dissipation [4–8]
unmanned aerial vehicle (UAV)-assisted MEC provides IoT devices with remote resources, it still faces the challenge of communication and computation design due to the limited embedded battery of UAV and IoT devices, and several prior related works have been done to minimize the energy consumption of ground or sensor nodes (GNs/Sensor nodes (SN)) [10–12]
Summary
With the rapid development of internet of things (IoT), numerous novel applications and services, such as augmented/virtual reality (AR/VR), face recognition and e-health, have emerged and interacted with each other [1], which start demanding more on computing capability, network throughput and latency for better quality of service (QoS) [2, 3]. The computation capability will not be wasted at the situation that GN has low-rate transmission due to poor channel gain, UAV can process the cached data instead These pioneering works above just investigated the single task of IoT devices, and focused on either data collection [11, 12] or computing offloading [14–19, 21, 22], but ignored the effectiveness of data caching. In this paper, collaborative multi-task computation and caching offloading for UAV-enabled MEC networks is investigated to minimize the total energy consumption of GNs, where the trajectory along with communication and computing resource allocation at UAV is optimized, and task offloading decision at GNs is determined, to satisfy the QoE requirement of time-sensitive tasks of GNs. The main contributions of this paper are summarized as follows:. A practical constraint that the total computing resources allocated to all the associated IoT devices cannot excess the UAV’s computation capability F, is given by k∈K ak [n]fk,u[n] ≤ F
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