Two-layer distributed content caching for infotainment applications in VANETs

For vehicular ad-hoc networks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this paper, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS) based algorithm to solve it. In ADPS, we divide the original problem into three sub-problems, then we use the dynamic programming (DP) method to solve each sub-problem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large scale original problem. Both numerical simulation results and experiments in testbed show that the proposed caching scheme outperforms existed caching schemes, the transmission delay and cost can be reduced by 10% and 24% respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to popularity-based caching scheme

Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks

A Content Poisoning Attack Detection and Prevention System in Vehicular Named Data Networking

Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically, the attackers can jeopardize vehicles’ cache memory with a Content Poisoning Attack (CPA). The CPA is the most difficult to identify because the attacker disseminates malicious content with a valid name. In addition, NDN employs request–response-based content dissemination, which is inefficient in supporting push-based content forwarding in VANET. Meanwhile, VNDN lacks a secure reputation management system. To this end, our contribution is three-fold. We initially propose a threshold-based content caching mechanism for CPA detection and prevention. This mechanism allows or rejects host vehicles to serve content based on their reputation. Secondly, we incorporate a blockchain system that ensures the privacy of every vehicle at roadside units (RSUs). Finally, we extend the scope of NDN from pull-based content retrieval to push-based content dissemination. The experimental evaluation results reveal that our proposed CPA detection mechanism achieves a 100% accuracy in identifying and preventing attackers. The attacker vehicles achieved a 0% cache hit ratio in our proposed mechanism. On the other hand, our blockchain results identified tempered blocks with 100% accuracy and prevented them from storing in the blockchain network. Thus, our proposed solution can identify and prevent CPA with 100% accuracy and effectively filters out tempered blocks. Our proposed research contribution enables the vehicles to store and serve trusted content in VNDN

Named Data Networking in Vehicular Ad hoc Networks: State-of-the-Art and Challenges

International audienceInformation-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community

Cooperative Caching in Vehicular Networks - Distributed Cache Invalidation Using Information Freshness

Recent advances in vehicular communications has led to significant opportunities to deploy variety of applications and services improving road safety and traffic efficiency to road users. In regard to traffic management services in distributed vehicular networks, this thesis work evaluates managing storage at vehicles efficiently as cache for moderate cellular transmission costs while still achieving correct routing decision. Road status information was disseminated to oncoming traffic in the form of cellular notifications using a reporting mechanism. High transmission costs due to redundant notifications published by all vehicles following a basic reporting mechanism: Default-approach was overcome by implementing caching at every vehicle. A cooperative based reporting mechanism utilizing cache: Cooperative-approach, was proposed to notify road status while avoiding redundant notifications. In order to account those significantly relevant vehicles for decision-making process which did not actually publish, correspondingly virtual cache entries were implemented. To incorporate the real-world scenario of varying vehicular rate observed on any road, virtual cache entries based on varying vehicular rate was modeled as Adaptive Cache Management mechanism. The combinations of proposed mechanisms were evaluated for cellular transmission costs and accuracy achieved for making correct routing decision. Simulation case studies comprising varying vehicular densities and different false detection rates were conducted to demonstrate the performance of these mechanisms. Additionally, the proposed mechanisms were evaluated in different decision-making algorithms for both information freshness in changing road conditions and for robustness despite false detections. The simulation results demonstrated that the combination of proposed mechanisms was capable of achieving realistic information accuracy enough to make correct routing decision despite false readings while keeping network costs significantly low. Furthermore, using QoI-based decision algorithm in high density vehicular networks, fast adaptability to frequently changing road conditions as well as quick recovery from false notifications by invalidating them with correct notifications were indicated