A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading

Abstract

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multiplayers computation offloading sequential game, and design the UAV-assisted Vehicular Computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC).

Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications

“…Intelligent computing methods can be considered to minimize the computation time as well as the energy of power-constrained devices. Different cost-optimization strategies are discussed in [ 45 ] to minimize power-constrained devices’ computation time and energy. We will consider one of the efficient computing techniques in our upcoming project.…”

Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

“…For numerical analysis, the average service time of FN is assumed to be 1 ms. Since OFNs generally have limited capacity compared to FNs [ 29 ], this paper assumes that the average size of the total capacity of FN ( C F ) and OFN ( C O ) is set as 20 and 10, respectively. For NoDiff, the requests are evenly distributed to FN and OFN by AP when OFN is available (i.e., both α and β are set to 1/2).…”

Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

“…A social IoV (SIoV) network was proposed in [ 25 ] and jointly optimized the resource allocation and the UAV’s trajectory. In addition, an SDN-based offloading strategy for vehicular networks was presented in [ 26 ] and the task execution time was minimized subject to quality of service (QoS) and energy consumption constraints. Beyond the conventional orthogonal multiple access (OMA) scenarios, a non-orthogonal multiple access (NOMA) setup was investigated in [ 27 ], whereas the stochastic offloading concept extended the deterministic binary and partial task offloading in [ 28 ].…”

“…As the UAVs fly in a three-dimensional (3-D) space and above rooftops, especial geometrical and mobility characteristics are introduced. Table 1 provides a brief description of the key elements of the aforementioned previous works, which give emphasis either on MEC IoV architectures, where the vehicles’ computing tasks are completed without cooperation of UAVs [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]; or UAV-enabled MEC network architectures that do not include RIS units [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]; or RIS-assisted UAV networks without MEC capabilities [ 32 , 33 , 34 ]; or RIS-assisted MEC network architectures with only ground-based nodes [ 35 , 36 , 37 , 38 ].…”