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However, IoV systems that rely solely on vehicular sensors often encounter limitations in forecasting events beyond current roadways, which are critical for regional transportation management. Moreover, the inherent temporal dependency in VEI application data poses risks of interruptions, impeding the seamless tracking of incremental information. To address these challenges, this paper introduces a joint task offloading and resource allocation strategy within an MEC environment that collaboratively integrates vehicles and Roadside Infrastructure Sensors (RISs). The strategy carefully considers the Doppler shift from vehicle mobility and the Tolerance for Interruptions of Incremental Information (T3I) in VEI applications. We establish a decision\u2010making framework that actively balances delay, energy consumption, and the T3I metric by formulating the task offloading as a stochastic network optimization problem. Utilizing Lyapunov optimization, we dissect this complex problem into three targeted subproblems that include optimizing local computational capacity, MEC computational capacity and comprehensive offloading decisions. To tackle the efficient offloading, we develop algorithms that separately optimize offloading scheduling, channel allocation and transmission power control. Notably, we incorporate a Potential Minimum Point (PMP) algorithm to boost parallel processing and simplify computational scale through matrix decomposition. Evaluations of our algorithm show that it excels in both complexity and accuracy, with accuracy improvements ranging from 74.3% to 114.0% in asymmetric resource environments. Simulation and experimental studies on offloading performance validate the effectiveness of our framework, which significantly balances network performance, reduces latency, and improves system stability.<\/jats:p>","DOI":"10.1155\/int\/8064086","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T03:50:38Z","timestamp":1746849038000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Collaborative Integration of Vehicle and Roadside Infrastructure Sensor for Temporal Dependency\u2010Aware Task Offloading in the Internet of Vehicles"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3446-7122","authenticated-orcid":false,"given":"Kaiyue","family":"Luo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2450-6252","authenticated-orcid":false,"given":"Yumei","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7957-2487","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7671-311X","authenticated-orcid":false,"given":"Konglin","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/mnet.001.1900659"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9148373"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.001.2200594"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2018.2841349"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21041174"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.2994972"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcomstd.001.1900053"},{"key":"e_1_2_11_8_2","doi-asserted-by":"crossref","unstructured":"MahendranR. 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