Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

Security Enhancement for NOMA-UAV Networks

“…2 for different locations of the UAV, i.e., above the secure user (0, 0, 0), above the farthest user (110, 40, 0) and above the horizontal center of the three users (56.67, 3.33, 0). From the results, we can see that the transmission rate of U 1 with these UAV positions can all achieve the rate threshold 0.5 bit/s/Hz, according to the optimization problem (14). For the secrecy rate of U 1 , it is close to the transmission rate when the UAV hovers above U 1 , due to the fact that its transmit power is the lowest, and it can be hidden in the high-power signals of other users.…”

“…Zeng et al proposed an effective algorithm to jointly optimize the hovering locations, durations and flying trajectory in [13], to minimize the energy consumption of the UAVs. In [14], Zhang et al proposed an effective scheme to minimize the energy consumption in UAVassisted mobile edge computing systems. In [15], Yang et al made a fundamental energy tradeoff in UAV wireless networks by considering both the circular and straight trajectories.…”

Multilevel Task Offloading and Resource Optimization of Edge Computing Networks Considering UAV Relay and Green Energy

“…In the terminal equipment, the calculation tasks of different sizes are selected for simulation analysis to verify the offloading mode and performance of the system. The simulation settings are based on the work of [16], [21], and [22]. Table 1 shows the detailed simulation parameters unless otherwise specified.…”

“…Such a flexible network layout can maximize the advantages of the fusion network, which can not only undertake considerable data processing but also increase the network reliability. Zhang, T. et al [21] aimed at minimum energy consumption and studied the edge offloading of UAV-assisted relaying and computation by applying the iterative algorithm through the Lagrangian duality method and successive convex approximation (SCA) technique. Hu, X. et al [22] studied the architecture of a UAV-assisted MEC.…”

“…Furthermore, the delay limitation problem is not considered. Unlike [21] and [22], the present study perceives that the UAV assumes only one function of relay or MEC when the task is offloaded, and this method establishes a multilevel edge network resource optimization model (MENROM) to optimize the function selection of the UAV in the MEC network. The above resource allocation algorithm does not consider the current actual energy state of the UAV.…”