Liang Dong
ABSTRACT
In the Internet of Vehicles (IoV), which is characterized by vehicle mobility and distributed positioning of edge servers, balancing the load among distributed servers and reducing system costs become challenges. The paper addresses the establishment of a comprehensive system cost model to enhance flexibility and usability. In the joint optimization of server load, pricing, and energy consumption, the Analytic Hierarchy Process (AHP) is used to allocate weights to these three indicators. The paper proposes a Chebyshev and Reverse Learning Improved sparrow search algorithm (COSSA) to solve this model. Firstly, the algorithm's initialization utilizes Chebyshev hybrid mapping improvement, enhancing the population's distribution for later improvement of solution quality. Then, a reverse learning perturbation is applied to the optimal individuals in the population, preserving diversity and enhancing the algorithm's exploration capability. COSSA algorithm is compared with three other algorithms, validating the practicality and effectiveness of the model and the algorithm. First, experimental results comparing different indicators and system utilities demonstrate the algorithm's effectiveness. A comparison between equal weight allocation and AHP weight allocation illustrates the importance of differentiated weight allocation in real-world application scenarios.
- Shi W, Cao J, Zhang Q, Li Y, & Xu L. 2016. Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), 637-646. 10.1109/JIOT.2016.2579198.Google Scholar
- Cao K, Liu Y, Meng G, & Sun Q. 2020. An overview on edge computing research. IEEE access, 8, 85714-85728. 10.1109/JPROC.2019.2921977.Google Scholar
- Xu X, Zhang X, Liu X, 2020 Adaptive computation offloading with edge for 5G-envisioned internet of connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(8): 5213-5222.Google ScholarDigital Library
- Shen X, Chang Z, Niu S. 2022 Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles[J]. Sensors, 22(13): 4959.Google ScholarCross Ref
- Zhao J, Li Q, Gong Y, & Zhang K. 2022. Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Transactions on Vehicular Technology,68(8), 7944-7956. 10.1109/TVT.2019.2917890.Google ScholarCross Ref
- Su M, Wang G, & Chen J. 2022. Efficient task offloading with swarm intelligence evolution for edge‐cloud collaboration in vehicular edge computing. Software: Practice and Experience. https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.3125.Google Scholar
- ZHANG H, LI H, CHEN S, & HE X. 2019. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation. Electronics & Information Technology, 41(5), 1194-1201. 10.11999/JEIT180592.Google Scholar
- XUE J, SHEN B. 2020 A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 8(1): 22-34. https://doi.org/10.1080/21642583.2019.1708830.Google ScholarCross Ref
- Awadallah M A, Al-Betar, M A,L A Doush, I A, Makhadmeh, S N, & Al-Naymat G. 2023. Recent Versions and Applications of Sparrow Search Algorithm. Archives of Computational Methods in Engineering, 1-28. https://doi.org/10.1007/s11831-023-09887-z.Google ScholarCross Ref
- Chen C, Zeng Y Li, H, Liu Y & Wan S. 2022. A multihop task offloading decision model in mec-enabled internet of vehicles.IEEE Internet of Things Journal,10(4), 3215-3230. 10.1109/JIOT.2022.3143529Google Scholar
Index Terms
- Task Offloading Strategy Algorithm for Realistic Internet of Vehicles Scenarios
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