Replication-Based Data Dissemination in Connected Internet of Vehicles

Wireless Sensor Networks (WSNs) are popular in smart homes to implement context-aware and intelligent services. Due to fixing bugs and adding new functions, the software on sensor nodes needs to be updated depending on efficient bulk data dissemination. Existing page transfer of data dissemination protocols employ pipelining technique to achieve spatial multiplexing. The conventional pipelining inefficiency, however, becomes a bottleneck in terms of dissemination delay and communication overhead. In this paper, we propose SAPM, a novel Self-Adjusting Pipelining Mechanism for efficient bulk data dissemination. By considering the impact of link correlation, detecting accumulated pages in sensor nodes, and carefully selecting the number of pages to request, SAPM shortens the dissemination delay effectively and reduces communication overhead to improve current bulk data dissemination protocols' performance. Our evaluation results on test bed experiments and extensive simulations show that SAPM reduces the completion time of state-of-the-art bulk data dissemination protocols by 27.8% on average compared to naive solution and the number of transmissions by 17.4% on average under high link correlation while having a low memory overhead.

Intelligent Transportation System (ITS) and Internet of Vehicles (IoV) allows the vehicles to disseminate and exchange various messages among nearby vehicles in the network. It includes road safety, road accidents, location sharing, driver assistance, navigation, collision warning, traffic data, and toll payment. Moreover, the exchange of these messages is carried out in an insecure channel. Hence, it leads to several issues during transmission of these messages in resource constrained IoV networks. It includes reliable data dissemination, dynamic topology, mobility of the vehicles, vehicle user’s privacy, and lightweight authentication mechanism. The existing Vehicle to Vehicle (V2V) data dissemination techniques have limitations in security attacks such as side channel, device stolen, cloning attacks, high computational overhead, throughput, and high communication cost. In this article, we propose P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV: Privacy Preserving Lightweight Secure (V2V) Data Dissemination Scheme for IoV environment using lightweight cryptographic primitives such as SHA-256, concatenation and XoR operation. We evaluate P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV in terms of security and vehicle user’s privacy against several security attacks. We do an in-depth security analysis of the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV by using informal security analysis and provides security proofs. We also perform a comparative analysis of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV with the existing state-of-the-art IoV environment using various performance parameters. The test-bed experimental result shows that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV outperforms the existing V2V data dissemination technique in terms of computational cost, communication overhead, and energy consumption.

Internet of Vehicles (IoV) have been attracting increasing interests in recent years, targeting to support services between vehicles or vehicles and infrastructures involving driving safety, traffic and infotainment. However, the dynamic nature of IoV make it quite challenging by applying traditional TCP/IP protocol stack, considering the complex signaling and addressing procedure of TCP/IP as well as the strict End-to-End service requirements in vehicular environment. To address these issues, an information centric data dissemination scheme (IDDS) in IoV is proposed. At first, the framework of our IDDS is introduced with the PDU (Protocol Data Unit) type, data structure and protocol signaling procedure given. After that, to increase the data delivery ratio and reduce the incurred latency, a defer time based forwarder selection scheme is presented, with which the "Interest" and "Reply" packets could be exchanged on the link with better transmission performance, thus mitigating the negative impact of high mobility of vehicles. Simulation results show that IDDS could outperforms some traditional strategies in terms of average hop count and content acquisition delay, thus significantly increasing the data dissemination efficiency in vehicular information-centric networks.

Security and efficient data dissemination over wireless network has been the focus of research in academic, military and commercial domains for many years. Unfortunately, proposed security solutions for data dissemination over wireless network are not efficient enough. In this work, a novel security and efficient data dissemination scheme which integrates authentication into a SE-Raptor (security enhanced Raptor) codes is investigated. In SE-Raptor codes, encoding packets with degree one are encoded again, and many promiscuous packets are took part in encoding. Simulation results show that the proposed method has better performance in terms of encoding ratio, degree ratio, encoding and decoding efficiency. Additionally, our analysis shows that data confidentiality, bogus data protection and deny of service properties are provided by the proposed scheme.

The Internet of Vehicles (IoV) is undoubtedly at the core of the future of intelligent transportation. It will prevail over the road ecosystem, and it will have a huge impact on our lives throughout the provision of seamless connectivity among diverse transportation means. For the network to operate efficiently, the data needs to be quickly spread throughout the network, which requires low computational and bandwidth overheads. However, the dynamics of vehicular environments due to frequent node mobility poses many challenges to realize efficient data dissemination. This work addresses this type of problem by proposing a novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group’n Route (GnR). Both mechanisms resort to machine learning and graph metrics that reflect the social relationships between the nodes. Our performance evaluation reveals that the clustering algorithm yields stable results with varying road scenarios, which are becoming an advisable approach in the presence of mobile IoV nodes. Also, the designed routing protocol achieves two orders of magnitude smaller overhead and almost double the delivery rate when it is compared to traditional routing protocols, which thereby justify that the combination of our two proposed clustering and routing methods are a plausible alternative to support IoV communications in real-world setups.

A holistic sensor network design for energy conservation and efficient data dissemination

Internet of Vehicles (IoV) is emerging rapidly as one of the applications of the Internet of things. It is fully dedicated to the vehicular communication and networking that fosters to enable smooth, fast, and safe travel experience and well as pedestrian safety. In the much-awaited fifth generation of cellular communication, IoV is set to play a significant role as an enhancement to vehicular ad hoc network (VANET). It will wipe off much of the channel-related transmission issues and would support the cause of enabling intelligent transportation. In this chapter, we present a novel mechanism to enable efficient message dissemination over IoV. We propose medium access control (MAC) protocol for its realization supported with results that would assist the future researchers in this field to have a vivid picture of all the advantages this technology is set to bring.

The vehicular adhoc networks (VANETs) is an integral part of the intelligent transportation system required for delivering safety and non-safety messages to vehicles in real-time via vehicle to vehicle and vehicle to everything communication modes. The purpose of VANETs is to provide efficient routing, message security, congestion-free traffic, and other services related to entertainment and comfortable journey. All these VANETs services, at the core, are related to efficient and secure data dissemination. In this paper, we have focused on clustering in VANETs for efficient data dissemination and resource sharing tasks. Considering the importance of fog computing for fast computing and processing, we have focused on clustering techniques in Fog-based VANET as well that can enhance the range of transmission and expand the number of available resources. This survey paper discusses the existing clustering techniques in VANETs scenario, cluster head selection parameters, the context-based techniques, and cluster performance parameters in VANETs.

Ride‐sharing services, such as ride‐hailing and carpooling, have become attractive travel patterns for worldwide users. Due to the high dynamic topology, heterogeneous wireless communication mode, and centralization, the Internet of Vehicles (IoV) is much more vulnerable to security issues such as privacy theft, single point of failure, data island, and unauthorized access, resulting in great security risks, while ride‐sharing services provide convenience. Blockchain technology used to solve the security problems of the IoV has become a current research hotspot, including authentication and privacy protection. Nevertheless, the existing algorithms still face challenges such as large amount of computation, low throughput, low scalability, consensus, and node security. Achieving an efficient, lightweight, and scalable secure blockchain–based IoV system still needs to be solved urgently. In this paper, we propose an effective consensus algorithm called Modified Proof of Reputation (MPoR). Firstly, by using the average network access time of the whole network nodes as the filtering threshold, the number of consensus nodes can be controlled adaptively. Then, a new multiweight reputation algorithm is proposed to quantify the reputation value of nodes, so as to detect and eliminate malicious nodes in the consensus node pool. Theoretical analysis and extensive simulation experiments reflect that under the IoV scenario, MPoR can adaptively select the number of consensus nodes, to effectively improve the consensus efficiency. When malicious nodes are less than 1/3 of the total nodes in the network, MPoR can effectively resist latent attack and collusive attack and has strong robustness.

Industrial Internet of Things (IIoT) is the other name of industrial Internet. It integrates a variety of existing industrial automation technologies with computing, machine learning, and communication technologies. Vehicular ad hoc network, an application of IIoT, is a self‐organized network of vehicles which tends to provide improved road safety, diminished traffic congestion, and ultimate comfort to the travellers. In VANETs, vehicles exchange data with each other directly or through roadside units (RSUs). Data dissemination in VANETs experiences numerous challenging issues including broadcast storm, network partitions, intermittent connectivity between vehicles, and limited bandwidth. In literature, various data dissemination schemes are proposed. However, most of these schemes are designed for either urban or highway VANET scenarios and evaluated under sparse or dense traffic conditions. Moreover, these schemes do not effectively overcome the aforementioned issues simultaneously. In this paper, we present a new data dissemination protocol for VANETs, which disseminates the emergency messages in different scenarios under varying traffic conditions. During dense traffic conditions, DDP4V employs the segmentation of transmission region of a vehicle in order to select the most appropriate next forwarding vehicle (NFV). Accordingly, it divides the transmission region of a vehicle in three distinct segments and selects vehicle(s) inside the highest priority segment to forward the message to all neighbour vehicles, whereas it also uses implicit acknowledgements for guaranteed message delivery during sparse traffic Conditions. Simulation results show that DDP4V protocol outperforms the other existing related protocols in terms of coverage, network overhead, collision, and end‐to‐end delay.

Introduction to the special section on blockchain techniques for the internet of vehicles security (VSI-bciov)

Device-to-device (D2D) is a new paradigm in cellular networks that enhances network performance by introducing increased spectral efficiency and reduced communication delay. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, we investigate a new and interesting data dissemination problem where the receivers are not explicitly known and data must be disseminated to the receivers within a probabilistic delay budget. We propose to exploit data depositories, which can temporarily house data and deliver them to interested receivers upon requests. We formally formulate the delay-constrained profit maximization problem for data deposition in D2D networks and show its NP-hardness. Under the unique mobile opportunistic network setting, a practical solution must be distributed, localized, and online. To this end, we introduce three algorithms for Direct Online Selection of 1-Depository, Direct Online Selection of L-Depositories, and Mixed Online Selection of L-Depositories. To demonstrate and evaluate the system, we implement a prototype using Google Nexus handsets and conduct experiments for five weeks. We further carry out simulations based on real-world mobility traces for evaluation of large-scale networks and various network settings that are impractical to experiment.

In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped with computing and storage capabilities to communicate with surrounding infrastructure. In this paper, we propose a subcategory of IoV as the Internet of buses (IoB), where public buses enable a service as a data carrier in a smart city by introducing a neural network-based sustainable data dissemination system (NESUDA), where opportunistic sensing comprises delay-tolerant data collection, processing and disseminating from one place to another place around the city. The objective was to use public transport to carry data from one place to another and to reduce the traffic from traditional networks and energy consumption. An advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for data allocation. We used the Auckland transport (AT) buses data set from the transport agency to validate our model for the level of accuracy in predicted bus arrival time and scheduled arrival time to disseminate data using bus services. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. The offloading capacity of our proposed data dissemination system showed that it could be utilized to effectively complement traditional data networks. Moreover, the maximum offloading capacity at each parent stop could reach up to 360 GB with a huge saving of energy consumption.

The Internet of Vehicles (IoV) is rapidly transforming the transportation landscape by enabling real-time communication between vehicles, infrastructure, and external networks. However, ensuring the security, efficiency, and responsiveness of IoV systems presents significant challenges. This paper proposes a comprehensive framework to address these complexities, integrating state-of-the-art technologies for secure and efficient vehicle operations. A fine-tuned large language model (LLM) is introduced to enhance navigation through unpredictable traffic, optimizing decision-making for both human-driven and autonomous vehicles. To safeguard data dissemination, blockchain technology is utilized, providing decentralized security, privacy, and trust across vehicle-to-everything (V2X) communications. In addition, a custom-built driver monitoring system (DMS) is developed to monitor driver alertness and respond to emergencies by activating autonomous driving systems when necessary. The framework further integrates IoV with smart traffic systems to optimize real-time traffic flow and route planning. The use of 5G technology ensures Ultra Reliable Low Latency Communication (URLLC), facilitating seamless, real-time data transmission between vehicles and infrastructure. This holistic approach addresses critical issues in data security, driver safety, and traffic management, offering a robust foundation for the future of IoV.

Driven by the increasing demands of vehicular tasks, edge offloading has emerged as a promising paradigm to enhance quality of experience (QoE) in Internet of Vehicles (IoV) networks. This approach enables vehicles to offload computation-intensive tasks to edge servers, resulting in reduced computation delays and lower energy consumption. However, traditional binary offloading limits the efficiency of edge offloading. To address this gap, we propose a partial offloading strategy that jointly optimizes the offloading ratio, computation, and communication resources in IoV. Recognizing the varying priorities of vehicular tasks regarding task delay and energy consumption, we formulate two distinct scenarios: one focused on minimizing delay and the other on minimizing energy consumption. Furthermore, we employ a reinforcement learning approach to establish a multi-dimensional joint optimization function by setting different objectives for each scenario. Based on this framework, we introduce a multi-state iteration deep deterministic policy gradient algorithm (SIDDPG), which effectively determines task partitioning and resource allocation. Simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of task delay and energy consumption.