A Multi-Objective Task Offloading Optimization for Vehicular Fog Computing

Sura Kh Abdullah,Adnan J Jabir

doi:10.24996/ijs.2022.63.2.33

Abstract

Internet of Vehicle (IoV) is one of the most basic branches of the Internet of Things (IoT), which provides many advantages for drivers and passengers to ensure safety and traffic efficiency. Most IoV applications are delay-sensitive and require resources for data storage and computation that cannot be afforded by vehicles. Thus, such tasks are always offloaded to more powerful nodes, like cloud or fog. Vehicular Fog Computing (VFC), which extends cloud computing and brings resources closer to the edge of the network, has the potential to reduce both traffic congestion and load on the cloud. Resources management and allocation process is very critical for satisfying both user and provider needs. However, the strategy of task offloading to fog node in constraints of energy and latency is still an open issue. Several research works have tackled the resource scheduling problem in the field of VFC; however, the recent studies have not carefully addressed the transmission path to the destination node, nor has it considered the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives while taking the deadline constraint into considerations by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Four different execution/transmission models are proposed where vehicle resources are utilized for tasks execution and transmission, and the well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the models which involve the vehicles in the transmission process have reduced the latency and the total energy for the VFC system significantly in comparison with other models and the current state of the art methods.

Highlights

Internet of Vehicle (IoV) is one of the most basic branches of the Internet of Things (IoT), which provides many advantages for drivers and passengers to ensure safety and traffic efficiency
IoT has been successfully invested in various life fields that are considered as the backbone of smart cities and their economies such as home automation systems, Intelligent Transportation Systems (ITS) or Internet of Vehicle (IoV), systems of surveillance seismic vibrations in buildings, tracking levels systems of pollution and radiation in the city, trash management systems, and much more [3]
Vehicular Ad-hoc Networks (VANETs) provides two kinds of communications, vehicle to vehicle (V2V) and vehicle to infrastructure (V2I), where the latter allows vehicles to communicate with the Road Side Unit (RSU) that is equipped with high computing capabilities[4]

Summary

System Architecture and Proposed Models

The layers of the system model are presented, the proposed execution/transmission models are described in detail. The proposed system model consists of three layers; the central management layer, the distributed RSUs layer, and the layer of the vehicular network These layers are described as follows: 1) Central management layer: consists of MBS, which is positioned at the midst of the road, such that its coverage area is large enough that all vehicles can have access to it. + with similar coverage areas and different specifications are distributed along the road They send information messages to the MBS to update their states. In the proposed system, when a task is generated by a vehicle , the latter asks the MBS for the best execution node and the best transmission path to the destination node by sending the computational requirement of the task ( and ). Each gene represents a task generated by a vehicle, while its content identifies where this task will be executed

Initial Population

Objective Function Evaluation

Findings

Conclusions