Content Distribution In Vehicular Networks Research Articles

An edge communication based probabilistic caching for transient content distribution in vehicular networks

Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies.

Smart Proactive Caching: Empower the Video Delivery for Autonomous Vehicles in ICN-Based Networks

The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users’ future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users’ future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users’ next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users’ future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.

LoICen: A novel location-based and information-centric architecture for content distribution in vehicular networks

The information-centric networking (ICN) paradigm has gained increasing attention as a solution for boosting content delivery in vehicular network applications. The content-naming oriented search and in-network data caching procedures of ICN can improve content delivery by avoiding the need to determine and maintain end-to-end routing paths in vehicular networks, which is challenging due to the mobility of vehicles, and intermittent and short-lived connections between them. However, the use of the ICN paradigm in vehicular networks aggravates the broadcast storm problem, and constantly suffers from breaks in the reverse path used for content discovery and delivery. In this paper, we propose the location-based and information-centric (LoICen) architecture to improve the content request procedure and reduce the broadcast storm problem in intelligent vehicular networks. In the LoICen architecture, vehicles opportunistically obtain the location information of the vehicles that might have desired content in their cache. This location information is used whenever possible to improve content search and discovery by directing Interest packets to the area where the content may be located, mitigating the broadcast storm problem by selecting only the most suitable neighboring vehicle at each hop to continue forwarding the Interest packet, and improving content delivery by selecting more stable paths. Simulation results show that the LoICen architecture outperformed related work in terms of content delivery ratio, while reducing the delay for the content delivery and the number of transmissions of Interest packets for content search.

Understanding Processing Overheads of Network Coding-Based Content Distribution in VANETs

Content distribution in vehicular networks, such as multimedia file sharing and software updates, poses a great challenge due to network dynamics and high-speed mobility. In recent years, network coding has been shown to efficiently support distribution of content in such dynamic environments, thereby considerably enhancing the performance. However, the related work in the literature has mostly focused on theoretic or algorithmic aspects of network coding so far. In this paper, we provide an in-depth analysis on the implementation issues of network coding in wireless networks. In particular, we study the impact of resource constraints (namely CPU, disk, memory, and bandwidth) on the performance of network coding in the content distribution application. The contribution of this paper is twofold. First, we develop an abstract model of a general network coding process and evaluate the validity of the model via several experiments on real systems. This model enables us to find the key resource constraints that influence the network coding strategy and thus to efficiently configure network coding parameters in wireless networks. Second, we propose schemes that considerably improve the performance of network coding under resource constrained environments. We implement our overhead model in the QualNet network simulator and evaluate these schemes in a large-scale vehicular network. Our results show that the proposed schemes can significantly improve the network coding performance by reducing the coding overhead.

Cross-layer Mobile Chord P2P protocol design for VANET

Efficient content distribution is one of the emerging applications in vehicular networks. To provide scalable content distribution in vehicular networks, Chord Peer-to-Peer (P2P) overlay could be applied. Most P2P protocols, including Chord, are designed for wired-line network, and might perform poorly in mobile networks. Mobile Chord (MChord) is proposed to enhance the P2P performance over Vehicular Ad hoc Network (VANET). In addition, cross-layer design to improve MChord performance in VANET is also investigated. Extensive NS-2 simulations with vehicular mobility traces are conducted to evaluate the P2P overlay performance in VANET. MChord and its cross-layer design outperforms the original Chord in various aspects, including application layer forwarding steps, query response ratio, correct query response ratio, and application delay.