Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network

Abstract

The rapid development of the Internet of Vehicles (IoV) along with the emergence of intelligent applications have put forward higher requirements for massive task offloading. Even though Mobile Edge Computing (MEC) can diminish network transmission delay and ease network congestion, the constrained heterogeneous resources of a single edge server and the highly dynamic topology of vehicular edge networks may compromise the efficiency of task offloading, including latency and energy consumption. Vehicular edge networks are also vulnerable to malicious outside attacks. In this paper, we propose a new blockchain-enabled digital twin vehicular edge network (DTVEN) where digital twin (DT) is exploited to monitor network communication, computation, and caching (3C) resources management in real time to provide rich data for offloading decision-making, and blockchain is utilized to secure fair and decentralized offloading transactions among DTs. To ensure 3C resources sharing across edge servers, we design a DT-assisted edge cooperation scheme, which makes full use of edge resources in vehicular networks. Furthermore, a DT-based smart contract is built to achieve a quick and effective consensus process. Then, we apply a task offloading algorithm based on an improved cuckoo algorithm (ICA) and a resource allocation scheme based on greedy strategy to minimize network cost by comprehensively taking into account latency and energy consumption. Numerical results demonstrate that our proposed scheme outperforms the existing schemes in terms of network cost.

Digital Twin-Enabled Internet of Vehicles Applications

“…Finally, for task offloading and resource allocation, they improve the cuckoo algorithm and the allocation scheme based on the greedy strategy. They compare the network cost under six offloading schemes and finally prove that the cost of the method proposed in the paper is the lowest among the offloading time, offloading cost, and network cost [143].…”

Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms

“…It is a paradigm of data processing and application execution at the edge of a communication network, which means close to where the data is generated instead of sending it to a centralized data center or the cloud for processing [15] [16]. Instead of relying exclusively on remote servers for processing tasks, edge computing uses local computing resources, such as embedded devices, local servers, or network nodes, to perform faster and more efficient processing of data at the point of use [17].…”

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks

“…Ref. [30] examined a task offloading problem involving parked cars acting as servers, using blockchain for decentralised offloading. They proposed and solved this problem using the game system to minimise users' overall payments.…”

“…For example, the studies reported in [28,29] only looked at task offloading; they neglected to consider com-puting resource allocation, even though each vehicle was frequently given a variety of computationally demanding real-time tasks. Moreover, task offloading optimisation-which entails unloading the entire work to the MEC server-was ignored in the research in [30,31]. Each vehicle that engages in task offloading chooses whether to unload and where its work will be processed.…”