Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Abstract

This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer nonconvex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems.Index Terms-Internet of things, unmanned aerial vehicle (UAV), mobile edge computing (MEC), wireless power transfer (WPT), resources allocation, flow-shop scheduling.

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

“…A UAV-enabled MEC system wherein the UAV is equipped with WPT technology was investigated in [ 62 ]. The main objective of that study was to minimize the total energy consumption of the UAV by jointly optimizing the IoT device association, resource allocation, UAV hovering time, WPT duration, uplink data rate, and service sequence of the IoT devices.…”

“…Thus, the adaptive CW based on the contact duration increases the throughput of the protocol. JRWS [ 62 ] uses a basic TDMA based approach for data transmission. It focuses on minimizing the total energy consumption by jointly optimizing the IoT device association, resource allocation, UAV hovering time, WPT duration, and service sequence of the IoT devices.…”

“…In the contention-based mechanism, several control packets must be exchanged to establish the connection between the sender and the receiver, which increases the overhead. In contrast, in most of the contention-free MAC protocols [ 62 , 63 , 67 , 68 ], control packets do not need to be exchanged if a centralized system schedules the timeslots among the receivers. However, in both cases, the protocol overhead may increase if certain values must be calculated in each slot or iteration.…”

Time Allocation Optimization and Trajectory Design in UAV-Assisted Energy and Spectrum Harvesting Network

“…After harvesting enough energy, these remote SUs transmit data to UAV instead of BS due to better channel state, and other SUs (nearby SUs) send data to BS at the same time. Even though UAV is an energy-limited equipment, its energy consumption mainly focuses on flight and hovering, and the energy consumption in communication is relatively small [37] [38]. Therefore, taking the UAV as the energy source will not have a great impact on its working time.…”

“…Different from P4 of nearby SUs, only SUs with throughput less than K are optimized in P7 due to each SU has its own energy harvesting time t ue [i]. And otherM SUs follow the results (38) and (39) from P6 which aims to spare as much time as possible. Thus P7 is similar to P4, only the number of SUs and disposable time is reduced.…”