Task Offloading in Multi-Access Edge Computing Enabled UAV-Aided Emergency Response Operations

Abstract

In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixedinteger nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush-Kuhn-Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.INDEX TERMS Multi-access edge computing, task offloading, resource allocation, messy genetic algorithm A solution to reduce the execution latency of the application and UAV energy consumption is to offload the task

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

“…According to the work that has been finished [30], the channel coefficient of the UAV-LEO channel is modelled as h mj = u mj l mj (10) where u mj and l mj represent the path loss factor and the small range fading, respectively. In particular, the path loss factor can be written as…”

“…In this paper, we wish to address these issues. First, UAVs, with their high mobility and flexible deployment, can act as aerial base stations [8][9][10]. We propose a satellite-UAV-IoT device network architecture where multiple LEO satellites collaborate to offload computational tasks.…”

A Survey on IoT Task Offloading Decisions in Multi-access Edge Computing: A Decision Content Perspective

“…The offload decision running on the Edge Server is conducive to fast data communication with TD. Compared with the offloading decision-making scheme deployed on TD, the offloading decision-making mechanism deployed on the edge server grasps more complete information on task offloading requirements and available computing resources in the region (Akter et al, 2023;Chu et al, 2023;Song et al, 2023;Sun et al, 2023;Yang et al, 2022;Zheng et al, 2023). In order to achieve better global task offloading scheduling, the offloading decision algorithm can also be deployed in multiple network layers and rows at the same time.…”

“…This is conducive to the allocation of computing and communication resources, and can better realize parallel task offloading in the MEC with abundant computing resources. In addition, TD only needs to communicate with the decision controller, thus avoiding a large number of many-to-many information exchanges between TDs (Akter et al, 2023;He et al, 2023;Sun et al, 2023;Trinh & Muntean, 2023;You et al, 2022;Zheng et al, 2023). Distributed decision-making is beneficial to reduce the computing pressure of the controller, and is more suitable for working in a network environment with unstable communication quality.…”

Large-Scale Service Function Chaining Management and Orchestration in Smart City

“…The management and orchestration (MANO) process ensures that NS are delivered in a well-controlled chain that meets the requirements and policies associated with the SFC request. Edge computing can be leveraged to this orchestration chain to further enhance the edgebased processing and resource allocation, particularly for IoT-aided smart city services [13][14][15][16][17]. In the process flow of handling SFC requests, there are several potential challenges and future directions for researchers and network operators, including resource constraints, VNF failures, SFC provisioning delays, management overhead, integration issues, etc.…”