Green UAV communications for 6G: A survey

Survey on computation offloading in UAV-Enabled mobile edge computing

For massive access to the Internet of Things, edge computing servers are installed on cellular ground base stations (GBSs) with fixed geographical locations, which easily suffer from traffic overload of the end user (EU) with high density and mobility. To provide reliable and flexible offloading service, unmanned aerial vehicles (UAV) are explored to assist edge computing, which relieves the computation offloading pressure of both EUs and GBS. However, most existing UAV researches focus on trajectory design to reduce offloading delay, which ignoring the variability of user distribution and the energy limitation of UAV. This article proposes a novel UAV-assisted edge computing framework, named as HOTSPOT, which locates the UAV in 3-D space according to the time-varying hot spot of user distribution and provides the corresponding edge computing offloading assistance. By formulating the UAV positioning problem into a maximum clique problem, a light-weighted deterministic algorithm is proposed based on stochastic gradient descent to search the optimal location of UAV. With the elaborate UAV position, HOTSPOT further gives an opportunistic offloading balanced scheme to reach low latency. Simulation results show that when the GBS load is 75%, HOTSPOT reduces the average offloading delay by 33%. When the GBS load reaches 90%, the average delay reduction is up to 80%.

HOTSPOT: A UAV-Assisted Dynamic Mobility-Aware Offloading for Mobile-Edge Computing in 3-D Space

—This paper studies the trajectory control and task ofﬂoading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV ﬂies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV has two roles to play: MEC server or wireless relay. The UAV makes task ofﬂoading decisions online, in which the collected tasks can be executed locally on the UAV or ofﬂoaded to the BS for remote processing. The TCTO problem involves multi-objective optimization as its objectives are to minimize the task delay and the UAV’s energy consumption, and maximize the number of tasks collected by the UAV, simultaneously. This problem is challenging because the three objectives conﬂict with each other. The existing reinforcement learning (RL) algorithms, either single-objective RLs or single-policy multi-objective RLs, cannot well address the problem since they cannot output multiple policies for various preferences (i.e. weights) across objectives in a single run. This paper adapts the evolutionary multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO problem. This algorithm can output multiple optimal policies in just one run, each optimizing a certain preference. The simulation results demonstrate that the proposed algorithm can obtain more excellent nondominated policies by striking a balance between the three objectives regarding policy quality, compared with two evolutionary and two multi-policy RL algorithms.

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Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing

UAV communication has received widespread attention in MEC systems due to its high flexibility and line-of-sight transmission. Users can reduce their local computing pressures and computation delay by offloading tasks to the UAV as an edge server. However, the coverage capability of a single UAV is very limited. Moreover, the data offloaded to the UAV will be easily eavesdropped. Thus, in this paper, we propose two secure transmission methods for multi-UAV-assisted mobile edge computing based on the single-agent and multi-agent reinforcement learning, respectively. In the proposed methods, we first utilize the spiral placement algorithm to optimize the deployment of UAVs, which covers all users with the minimum number of UAVs. Then, to reduce the information eavesdropping by a flying eavesdropper, we utilize the reinforcement learning to optimize the secure offloading to maximize the system utility by considering different types of users’ tasks with diverse preferences for residual energy of computing equipment and processing delay. Simulation results indicate that compared with the single-agent method and the benchmark, the multi-agent method can optimize the offloading in a better manner and achieve larger system utility.

Secure Transmission for Multi-UAV-Assisted Mobile Edge Computing Based on Reinforcement Learning

Unmanned aerial vehicles (UAVs) have been considered as a promising approach for providing additional computation capability and extensive coverage to ground mobile devices (MDs), especially in the scenario where the communication infrastructure is unavailable. This article investigates a UAV-assisted multiaccess edge computing system, where a number of ground MDs are served by a flying UAV and a ground base station, both of them are equipped with computation resources. The system aims to minimize the weighted cost of time latency and energy consumption, subject to the constraints on offloading decisions and resource competition. Since this problem is NP-hard, and the MDs are autonomous, we propose a game theory-based scheme to find the optimal solution and prove the existence of the Nash equilibrium. Meanwhile, we propose another two schemes as the benchmark to evaluate the efficiency and effectiveness of the game-theoretic solution. The simulation results show that the game-theoretic scheme is able to achieve a near-optimal performance, and the convergence time is also stable when the number of MDs increases.

Energy–Latency Tradeoff for Computation Offloading in UAV-Assisted Multiaccess Edge Computing System

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Energy–Latency Tradeoff for Computation Offloading in UAV-Assisted Multiaccess Edge Computing System