Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

Abstract

With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems

“…[ 18 ]. Task Offloading Model [ 13 ]: In this policy, the tasks will be handled via one of the available servers on the basis of work in the Ref. [ 13 ].…”

“…This is because the communication channels’ resources have competed among UAVs and consumed more energy during the offloading process, regardless of the offloading policies (i.e., edge, cloud, work in the Refs. [ 13 , 18 , 20 ], and proposed model). However, the layer of compression has the ability to significantly decrease the consumed energy.…”

“…However, Huang et al [ 21 ] and Alhelaly et al. [ 13 ] have recently addressed the task offloading problem for IoT devices using UAV-aided fog-enabled IoT networks. Specifically, in the Ref.…”

“…In the meantime, task offloading and resource allocation are optimized in the Ref. [ 13 ] for multi-UAV-aided edge cloud computing networks, where UAVs are deployed as networks to cover high-density and dead areas.…”

“…[ 12 ], a novel UAV-aided mobile edge computing system is proposed where IoT devices, edge-cloud servers, and UAVs are jointly interacted to address the offer of edge computing for IoT devices with the aim of optimizing the service delay of IoT devices and energy of UAV. Moreover, an energy-aware model is introduced for multi-UAV-aided edge-cloud networks, where UAVs are efficiently cooperated to cover the high-density areas of the network with the aim of optimizing the energy consumption [ 13 ]. Further, the UAV-aided edge computing approach is presented in the Ref.…”