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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.<\/jats:p>","DOI":"10.1186\/s13677-023-00496-6","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T01:01:39Z","timestamp":1691715699000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network"],"prefix":"10.1186","volume":"12","author":[{"given":"Chunhai","family":"Li","sequence":"first","affiliation":[]},{"given":"Qiyong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Mingfeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhaoyu","family":"Su","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Dapeng","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Taherkordi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"issue":"4","key":"496_CR1","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/MNET.102.2100394","volume":"36","author":"Z Guo","year":"2022","unstructured":"Guo Z, Yu K, Bashir AK, Zhang D, Al-Otaibi YD, Guizani M (2022) Deep information fusion-driven poi scheduling for mobile social networks. 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