Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user's quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.INDEX TERMS proactive content caching, mobile edge computing, distributed learning, collaborative filtering, neural network.

No abstract

No abstract

The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars for improving quality-of-experience (QoE) and content retrieval cost in this work. We develop a low-complexity content popularity prediction mechanism in a hierarchical federated setting. In particular, we use a self-attention technique with an LSTM-based prediction mechanism to extract local content popularity patterns in self-driving cars. However, the local contents will not be sufficient to satisfy the passenger's requirements. Hence, using the popular contents of other self-driving cars will solve the requirement constraint but poses some privacy issues. We use the privacy-preserving decentralized model training framework of Federated Learning (FL) to tackle this issue. Specifically, we deploy the hierarchical Federated Averaging (FedAvg) algorithm on local models obtained from self-driving cars to develop a regional and global content popularity prediction model at RSU and MBS, respectively. Extensive simulations on real-world datasets show the proposed approach improves cache space utilization by maximizing the local cache hit ratio, and further, minimizes the content retrieval cost for self-driving cars as compared with alternative methods.INDEX TERMS proactive content caching, multi-access edge computing, federated learning, self-attention mechanism, recurrent neural network.

No abstract