Vehicular Named Data Networking Research Articles

A practical and UC-secure decentralized key management and authentication scheme based on blockchain for VNDN

In Vehicular Named Data Networking (VNDN), most of the existing key management mechanisms still rely on the hierarchical key trust model or the Public Key Infrastructure (PKI), in which the centralized certificate authority is used as a Trusted Third Party (TTP) to provide a signature for the user?s public key. Thus, the TTP becomes vulnerable to attacks and maybe there exists a singlepoint failure problem. In addition, the in-network caching in the VNDN may incur a threat to the system and make it is vulnerable to the DoS attack caused by Interest flooding aimed at the Content-Store. To tackle these security issues, we firstly propose an efficient decentralized key management solution based on blockchain for VNDN. Secondly, based on the proposed key management scheme, a lightweight mutual authentication scheme and a key agreement protocol for V2X are respectively proposed in this paper. Finally, we analyze the security attributes of our solutions in the Universally Composable (UC) framework. Our analysis results show that our novel schemes can meet the security requirements of our solutions. In addition, our experimental results show our new schemes have highly efficient, lower computation and communication costs than other related schemes.

Adaptive Segmented Subscription for Efficient Data Dissemination in Vehicular Named Data Networks

Adaptive Segmented Subscription for Efficient Data Dissemination in Vehicular Named Data Networks

Hop Count Based Interest Selection and Content Forwarding Scheme for Vehicular Named Data Networks

Vehicular Named Data Networks (VNDN) face challenges in efficiently disseminating content due to high mobility and intermittent connectivity. To address these challenges, a Hop Count based Interest Selection and Content Forwarding (HISCF) scheme for VNDNs is proposed. The scheme focuses on mitigating interest flooding, reducing data packet duplication, and alleviating network congestion. HISCF consists of two components: interest selection and content forwarding. The selection process chooses a vehicle based on hop count and Interest Satisfaction Ratio (ISR) to forward the interest packet. Content forwarding is performed considering a hop count limit and pending interests, ensuring efficient content delivery. The HISCF scheme is evaluated using extensive simulations in ns-3 with ndnSIM. Performance metrics such as Data Packet Replication Count (DPRC), total number of interest packets forwarded, Interest Response Time (IRT) and routing overhead are analysed. Results show that HISCF outperforms naïve VNDN, reducing DPRC, minimizing interest packets forwarded, and decreasing average IRT. The findings demonstrate that HISCF effectively mitigates interest broadcast storms, reduces data packet duplication, and improves content delivery efficiency in VNDNs. This study contributes to VNDN research advancement and provides insights for designing effective content forwarding mechanisms in vehicular networks.

Two-Way Reliable Forwarding Strategy of RIS Symbiotic Communications for Vehicular Named Data Networks

Two-Way Reliable Forwarding Strategy of RIS Symbiotic Communications for Vehicular Named Data Networks

Revving up VNDN: Efficient caching and forwarding by expanding content popularity perspective and mobility

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

DSORL: Data Source Optimization With Reinforcement Learning Scheme for Vehicular Named Data Networks

DSORL: Data Source Optimization With Reinforcement Learning Scheme for Vehicular Named Data Networks

A comprehensive and configurable simulation environment for supporting vehicular named-data networking applications

The Named Data Networking (NDN) architecture, with its network-layer features, services, and properties, is well-suited for vehicular applications and inter-vehicle communication (IVC). In contrast, IP-based host-centric architectures struggle with challenges inherent to the vehicular ad-hoc network (VANET) context, such as node mobility, data security, efficient data forwarding, and routing. Named Data Networking takes a fundamental departure from today’s IP-based architectures for VANET/IVC and thus requires extensive experimentation and evaluation. To facilitate experimentation with Vehicular Named-Data Networking, we present the NDN4IVC, an open-source simulator/framework that facilitates testing of more realistic VANET applications via the NDN stack. This project utilizes two popular simulators for VANET simulation: the NS3 network simulator with the ndnSIM module and SUMO, a simulator of urban mobility. NDN4IVC allows real-time bidirectional communication between SUMO and NS3 to support more data about road traffic and vehicular mobility. The framework can model the impact of vehicular networks on road traffic and investigate complex interactions between the two domains. We included two sample applications in VANET to demonstrate how different NDN properties can be used. Experiments were conducted as proof-of-concept studies to demonstrate the potential of the framework and its functionality.

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.

IDTracS: an Interest-Data-flow tracking-based forwarding scheme for vehicular named data networks

IDTracS: an Interest-Data-flow tracking-based forwarding scheme for vehicular named data networks

Towards Predictive Forwarding Strategy in Vehicular Named Data Networking

Vehicular Named Data Networking (V-NDN) is promising to improve the content delivery efficiency in Vehicular Ad Hoc Networks (VANETs). However, the potential broadcast storm caused by Interest packet flooding and return path failures caused by vehicle mobility can significantly degrade the content delivery performance. Existing forwarding strategies based on outdated position information cannot address these issues well. In this paper, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predictive forwarding strategy</i> (PRFS) for V-NDN. In PRFS, Long Short-Term Memory (LSTM) is employed to amend the NeighBor Table (NBT) for preciser neighboring vehicles' positions. In addition, the next-hop forwarder is selected among the neighbors, taking into account the link reliability and the Distance along the Road (DR) in both directions. Furthermore, a new mechanism is designed to notify the selected next-hop forwarder by embedding the forwarder identity in the Interest packet header, so as to accelerate the forwarding process. Finally, extensive simulations are carried out, and experimental results demonstrate that PRFS can reduce the number of forwarded Interest packets and data packets by 21.29% and 25.75%, respectively, and improve the success ratio of satisfied Interest packets by 35.1% compared to the existing baseline algorithms.

GeoISA: A new road-topology-assisted geo-based content discovery scheme for vehicular named data networking

Content discovery represents a key foundation in realizing the promising vehicular named data networking (VNDN). The highly dynamic topology of VNDN prevents content discovery from becoming a straightforward task. In the VNDN literature, vehicle-to-vehicle content discovery mainly has flooding and time-contention-based (TCB) schemes. However, these schemes have transmission overhead issues or/and do not guarantee Interest forwarding in multiple directions. Thus, the likelihood of packet loss and retransmission is high, or/and the likelihood of locating content sources opportunities is low. Therefore, we proposed a new road-topology-assisted geo-based content discovery scheme for VNDN, termed GeoISA, to address the problems mentioned. In GeoISA, we first designed a new Interest forwarding suppression control mechanism named Interest suppression area (ISA). ISA demarcates an Interest forwarding suppression geo-area to determine the nodes' forwarding direction similarity and ensure forwarding Interest over different forwarding directions. Each road's pathway direction is considered a different Interest forwarding direction. The nodes' geo-position and traveling direction and the road's number of lanes and lane identifiers are involved in ISA demarcation. Second, an ISA-assisted distance-based TCB Interest forwarding strategy (GIDiS) was proposed to mitigate broadcast concerns. Finally, we conducted a simulation-based evaluation to compare GeoISA performance with the performance of four state-of-the-art solutions in an urban area scenario. Results demonstrate that GeoISA outperforms the other considered solutions regarding the Interest satisfaction ratio and the delay and path length (hops count) of the content delivery at the expense of a moderate transmission overhead. GeoISA can help ensure widespread Interest forwarding in many directions to boost the discovery of content sources and network topology in TCB schemes and intelligent context-aware forwarding solutions.

Environment-Adaptive Dynamic Caching for Vehicular Named Data Networks in Dynamic Network Environments

Environment-Adaptive Dynamic Caching for Vehicular Named Data Networks in Dynamic Network Environments

Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN.

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme.

The impact of channel model on the performance of distance-based schemes in vehicular named data networks

&lt;span lang="EN-US"&gt;Distance-based schemes present one of the methods to avoid the broadcast problem in vehicular named data networks. However, such schemes overlook the most factor in performance evaluation which is the variation in received signal strength caused by the propagation model choice. Thus, they are evaluated under one propagation model while neglecting the effect of the others. This paper evaluates the impact of the propagation variation model over three distance-based schemes, namely rapid named data networking (RNDN), enhanced vehicle on named data networking (EVNDN) and opportunistic interest forwarding protocol (OIFP). Simulation experiments are performed over three propagation models. Simulation results show that Nakagami significantly degrades network performance. However, it has a noticeable positive effect over higher distance resulting in a higher interest satisfaction ratio as compared to the other models. The RNDN exhibits a higher number of retransmissions across the Nakagami. In contrast, a higher number of retransmissions is exhibited by EVNDN when compared to the other schemes over the Friis and random. The OIFP show a higher interest satisfaction ratio when compared to EVNDN and RNDN under all models. OIFP shows robustness towards the adverse fading effects resulting from the Nakagami and exhibits lower end to end delays.&lt;/span&gt;

An Intelligent Caching Strategy Considering Time-Space Characteristics in Vehicular Named Data Networks

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme.

RLSS: A Reinforcement Learning Scheme for HD Map Data Source Selection in Vehicular NDN

In the autonomous driving era, high-definition (HD) maps are an essential building block to enable fine-grained environmental perception, precise localization, and path planning. However, with rich multidimensional information, the size of HD map data is huge and cannot be stored onboard, where the dynamic map data need to be distributed in real time via vehicular networks and how to design the distribution mechanism (i.e., determining the data source for requests) becomes crucial. For the end-to-end communication protocols (i.e., TCP/IP), the main limitation is the vehicle mobility and high dynamic of the network topology, which can degrade the transmission performance dramatically. Therefore, in this article, we propose a reinforcement learning-based data source selection scheme, named RLSS, for efficient HD map distribution in vehicular named data networking (NDN) scenarios, which aims at seeking the best data source (roadside infrastructures or nearby vehicles) in accordance with the map data requests. Specifically, in RLSS, we adopt a deep reinforcement learning-based architecture to learn a neural network as an agent to make the decision of data source selection, which can work online after offline training based on historical selection action performance. In addition, to solve the “cold start” problem for a new vehicle, we propose a model aggregation algorithm and weight update approach to learn the model parameters from its nearby vehicles, which can guarantee the performance while saving the communication cost. Finally, we implement RLSS in NS-3 by adopting the tools of the ndnSIM, SUMO, and Gym. Extensive simulations demonstrate that RLSS can significantly improve the transmission performance in terms of delay, throughput, and packet loss when compared with state-of-the-art data source selection schemes.

Interest Broadcasting and Timing Attack in IoV (IBTA-IoV): A novel architecture using Named Software Defined Network

Advancement in Internet has led to the creation of a digital community in which everything is connected and is accessible remotely. However, such advancement comes with many problems as traditional networks have become more complex and harder to configure according to predefined policies. Named Software-Defined Network (NSDN) is an emerging paradigm in computer networks. It is gaining interest because it promises to control and manage networks more simply and efficiently than traditional networks. NSDN is considered the adoption of Named Data Network (NDN) and Software Defined Network (SDN). Named Data Network (NDN) is a great platform to overcome the traditional Internet addressing limitations. Instead of IP addresses, content names are used to communicate between various nodes in NDN. On other hand, the Internet of Vehicle (IoV) is an emerging new application of the Internet of Things (IoT) that is getting popularity because it promises to extend network connectivity to a large extent. IoV relies on the broadcast pattern to exchange useful information with a specific range of vehicles. However, this exchange of information often leads to many problems in terms of security and duplication of packets. This article proposes an efficient scheme to address problems related to the routing (broadcast storm and isolated network) of Named Software-Defined Internet of Vehicle (NSDIoV). Moreover, a Timing Attack problem is also addressed in this article. This article proposes a novel scheme named Interest Broadcasting and Timing Attack in IoV (IBTA-IoV). The first part of the proposed scheme addresses the Timing Attack issue while the second part of the proposed scheme tackles the Interest packets broadcasting and isolated network problem. IBTA-IoV is compared with native Vehicular Named Data Network (VNDN), IBFS, and IBFS with the attacker. The evaluation results show that the proposed solution in this article is simple, efficient, and provides high performance in terms of variable traffic scenarios.

Dynamic Naming Scheme and Lookup Method Based on Trie for Vehicular Named Data Network

Content naming and lookup are decisive functions of the future architecture named data network (NDN). The core concept of NDN is the content distribution between consumers and content providers. The NDN supports advance vehicular networks that is famous with vehicular-named data network (VNDN) with different naming schemes such as hybrid, flat, attribute-based, and hierarchical names. These schemes are used in a static way for vehicular network, in summary, the hybrid, flat, and attribute-based makes a complex structure, and on the other hand, hierarchical names long in length and name lookup performance are a bottleneck in NDN, which can directly affect the network performance. Therefore, we introduce a dynamic naming scheme and lookup method (DNSL) for VNDN to mitigate these issues. We argue that the dynamic naming scheme is a better approach to VNDN, while the static name is a cost-effective, hefty, integrated fashion, and improper for the vehicular network. This study focuses on (1) a dynamic naming scheme using dynamic-tag and (2) a lookup method based on node partition of trie; the trie approach is very famed in data structure and extensively used for the lookup content, insertion, and deletion processes. Our experimental evaluation shows that the DNSL scheme is highly efficient, scalable, and provably correct for VNDN.

Dynamic Cooperative Cache Management Scheme Based on Social and Popular Data in Vehicular Named Data Network

Vehicular Named Data Network (VNDN) is considered a strong paradigm to deploy in vehicular applications. In VNDN, each node has its cache, but due to limited cache, it directly affects the performance in a highly dynamic environment, which requires massive and fast content delivery. To reduce these issues, the cooperative caching plays an efficient role in VNDN. Most studies regarding cooperative caching focus on content replacement and caching algorithms and implement these methods in a static environment rather than a dynamic environment. In addition, few existing approaches addressed the cache diversity and latency in VNDN. This paper proposes a Dynamic Cooperative Cache Management Scheme (DCCMS) based on social and popular data, which improves the cache efficiency and implements it in a dynamic environment. We designed a two-level dynamic caching scheme, in which we choose the right caching node that frequently communicates with other nodes, keep the copy of the most popular content, and distribute it with the requester’s node when needed. The main intention of DCCMS is to improve the cache performance in terms of reducing latency, server load, cache hit ratio, average hop count, cache utilization, and diversity. The simulation results show that our proposed DCCMS scheme improves the cache performance than other state-of-the-art approaches.

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Detworking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.