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<p>Mobile edge caching (MEC) has emerged as a promising and economical solution to complement conventional infrastructure caching. Nonetheless, the vulnerability of vehicle-to-vehicle (V2V) links causes connection loss and limits data exchange. On the other hand, the traditional content popularity-based request model would lead to lower cache hit ratios and increased content retrieval times. To tackle this issue, a novel scheme of MEC-assisted public vehicular network is proposed in this study, where the random linear network coding (RLNC) based caching strategy is applied to allow public vehicles to simultaneously obtain coded blocks from multiple vehicles and infrastructures on the move. Besides, a content request model that considers content popularity, historical interest, and social attributes is explored. A cost minimization problem is formulated under the proposed scheme, which is a highly non-trivial stochastic problem. To this end, the data volume of V2V offloading is obtained by a divide and conquer (DC) algorithm, and then the caching strategy is derived by a heuristic-based algorithm. Finally, extensive simulations show that the proposed content model can achieve a higher local offloading ratio and the proposed scheme has a much lower cost compared to the baselines.</p>

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Cost-Efficient and Preference-Aware Mobile Edge Caching in Public Vehicular Networks

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Xiaopei Chen,
  • Zhijian Lin,
  • Wenhao Wu,
  • Feng Chen,
  • Pingping Chen

摘要

Mobile edge caching (MEC) has emerged as a promising and economical solution to complement conventional infrastructure caching. Nonetheless, the vulnerability of vehicle-to-vehicle (V2V) links causes connection loss and limits data exchange. On the other hand, the traditional content popularity-based request model would lead to lower cache hit ratios and increased content retrieval times. To tackle this issue, a novel scheme of MEC-assisted public vehicular network is proposed in this study, where the random linear network coding (RLNC) based caching strategy is applied to allow public vehicles to simultaneously obtain coded blocks from multiple vehicles and infrastructures on the move. Besides, a content request model that considers content popularity, historical interest, and social attributes is explored. A cost minimization problem is formulated under the proposed scheme, which is a highly non-trivial stochastic problem. To this end, the data volume of V2V offloading is obtained by a divide and conquer (DC) algorithm, and then the caching strategy is derived by a heuristic-based algorithm. Finally, extensive simulations show that the proposed content model can achieve a higher local offloading ratio and the proposed scheme has a much lower cost compared to the baselines.