Delay tolerant communications in sparse vehicular networks

Classical ad-hoc network algorithms become impractical for Vehicular Ad Hoc Networks when density of vehicles is low in rural areas and during the periods when the vehicle traffic is low in cities (such as at night). End-to-end connectivity requirement of these algorithms causes deflection. Connections get broken when a suitable network element isn't available for data transfer. This problem which occurs in low dense partially connected Vehicular Ad hoc Networks can be solved by using delay tolerant approaches in routing algorithms. By using the store-carry-forward method, data is carried by network elements until a suitable network element is found. By this way, network connectivity is provided in a delayed manner between momentarily disconnected vehicles. In this paper, the impact of delayed transfer of data to an end-to-end transfer and the impact of data lifetime to the network performance are examined. Considering the speed of vehicles and the desired throughput, lifetime of data is determined according to the network connectivity. The results of this study contribute to the fulfilment of the Quality of Service (QoS) requirements in Vehicular Ad Hoc Network applications.

Location tracking of mobile entity becomes a research challenge for applications in vehicular networks, mobile sensor networks and mobile adhoc networks. The GPS based tracking in vehicular networks has limitations in certain scenarios such as inside tunnels, tall buildings, dense forests, etc. Tracking of fast-paced mobile entity (say mobile node) finds applications in vehicular networks such as chasing, hit-and-run accidents, etc. Connected dominating set(CDS) provides a virtual backbone in adhoc network. This paper uses connected dominating set(CDS) based virtual backbone in vehicular adhoc networks for tracking of fast-paced mobile node's location. We show that using a simple heuristics that the location updates for mobile node can be reduced to the tune of 40%.

Vehicular networks have become a promising platform for large-scale urban sensing. On-demand data retrieval is a crucial operation for many applications of vehicular networks. It is particularly challenging, however, to achieve high performance of on-demand data retrieval in sparse vehicular networks. Data replication based on random linear network coding can solve the coupon collection problem but suffers the problem of unnecessary data replications which waste the precious communication opportunities of sparse vehicular networks. With real traces we reveal the existence of community structures in vehicular networks, which causes excessive linearly correlated blocks. Motivated by the important observation, we propose a community-aware data replication scheme based on random linear network coding. To reduce the excessive correlated coded blocks because of community structures, we make probabilistic control on the data replication phase, taking the community structures into account. It is demonstrated through extensive simulations that the community-aware scheme can effectively save up to 50% communication opportunities whiling achieving high performance of on-demand data retrieval.

Vehicular networks are the key cornerstone of Intelligent Transportation Systems (ITS). With the growth of the technical revolution in 5G the next-generation networks, it is expected to meet various future communication requirements of Intelligent ITS. However, no single technology of 5G can efficiently accommodate the broad range of requirements of vehicular networks. However, the aggregation of various communication technologies with the 5G vehicular network could help to achieve ambitious goals like efficient network management, centralized view, and computation. The need for provisioning efficient network control, management, and high resource utilization in vehicular networks motivates the hierarchical 5G Next-generation vehicular networks. Blockchain has emerged as a viable solution to offer decentralization, transparency, and immutability among the stakeholders and manage trust in networking platforms. Motivated by the preceding discussion, in this chapter, we discuss the integration of software-defined networking (SDN), cloud computing over 5G mobile communication in vehicular networks, which is the key requirement of new hierarchical 5G-SDVNs. Then, programmable, efficient, and controllable network architecture is introduced for 5G-SDVN to achieve sustainable network development. Moreover, the centralization and flexibility provided by SDN and 5G communication technologies help to meet customer demand for various ITSs and vehicular network applications like bandwidth, high speed, and ubiquity, that can be employed in next-generation ITS. Finally, we present the SDN and cloud-enabled hierarchical future 5G-vehicular network architecture, its applications, issues, and challenges based on recent advances in technology and research.KeywordsBlockchainSoftware defined network5GIntelligent transport system

The Ad-hoc Vehicular networks (VANET) was developed through the implementation of the concepts of ad-hoc mobile networks(MANET), which is swiftly maturing, promising, emerging wireless communication technology nowadays. Vehicular communication enables us to communicate with other vehicles and Roadside Infrastructure Units (RSU) to share information pertaining to the safety system, traffic analysis, Authentication, privacy, etc. As VANETs operate in an open wireless connectivity system, it increases permeable of variant type's security issues. Security concerns, however, which are either generally seen in ad-hoc networks or utterly unique to VANET, present significant challenges. Access Control List (ACL) can be an efficient feature to solve such security issues by permitting statements to access registered specific IP addresses in the network and deny statement unregistered IP addresses in the system. To establish such secured VANETs, the License number of the vehicle will be the Identity Number, which will be assigned via a DNS server by the Traffic Certification Authority (TCA). TCA allows registered vehicles to access the nearest two or more regions. For special vehicles, public access should be restricted by configuring ACL on a specific IP. Smart-card given by TCA can be used to authenticate a subscriber by checking previous records during entry to a new network area. After in-depth analysis of Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average Delay, and Handover Delay, this research offers more secure and reliable communication in VANETs.

Vehicular networks aim to provide a communication framework for moving vehicles, road infrastructure and pedestrians. Such kind of networks is the entrance to a new era of services that will make areas such as security and safety, information, transactions, entertainment and sustainability (green transportation) more efficient than they are today, especially in the upcoming era of autonomous vehicles and self-driving cars. However, the severe nature of vehicular environments makes efficient inter-vehicular communication very difficult to achieve. Vehicular Delay-Tolerant Networks (VDTN), as these networks are called, have very sparse, intermittent connections, and the lack of a fixed topology gives rise to one of the main challenges that they face: packet routing. A range of routing algorithms has been proposed in recent years to optimize communication in vehicular networks, and significant progress has been made in the matter but the quest for the optimal performance is still ongoing. In this paper, we explore a deep learning approach to the routing problem in VDTN, proposing a routing architecture and algorithm based on Deep Neural Networks that helps routers make packet forwarding decisions based on the current conditions of its surroundings. In order to assess the performance of the proposed architecture, simulations were run showing important gains in terms of network overhead and hop count with respect to popular routers, while maintaining acceptable packet delivery rates and average delivery delays.

Vehicle-to-vehicle networks have gained significant interest in recent times from researchers all around the world. V2V/V2I are emerging as an efficient solution for achieving road safety and securely transmitting data from one vehicle to other. However, in such opportunistic environments such as a sparse vehicular network where disruption, dynamic network topology, fast vehicle movement, and environmental conditions are the major concerns, data forwarding is extremely challenging. Traditional ad-hoc routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) fail to deliver in such laborious conditions. Routing protocols of Delay/Disruption Tolerant Networks(DTN) exploit the Store-Carry-Forward Mechanism (SCF) strategy in these cases. Taking advantage of temporary connections to disseminate information is the focus of DTN. DTN helps to increase information propagation coverage in sparse areas where there are very few devices. In this paper, we first compare the performance of AllJoyn framework and Direct Delivery protocol in a single-hop scenario and then draw the attention towards a multi-hop scenario by comparing other three routing protocols that use DTN as the underlying paradigm. These protocols are Flooding, Epidemic, and PRoPHET. The performances are evaluated by transmitting the files of size 1, 4, and 10 MB from a static sender to a mobile receiver in the multi-hop scenario. We also develop an Android application that contains the implementation of these routing protocols along with file sharing functionality. From the outcomes of this experiment, it can be said that Epidemic routing protocol performs the best in our considered multi-hop scenario in terms of transfer delay, coverage, and throughput whereas, Direct Delivery routing protocol performs better than AllJoyn in single-hop communication.

The newly emerged vehicular communication network is seen as a key technology for solving the increasingly serious vehicular traffic congestion as well as improving road safety, and applications of vehicular networks are also emerging at the same time. Predicting vehicular visiting time is vital to both solving the vehicular system problem and building efficient vehicular networking. It is an open and unsolved problem that how much the vehicular staying duration of visits to different areas can be predicted. In this paper, we use real vehicular traces in Beijing and Shanghai and an area partition model to explore the limit of predictability of the vehicular visiting time in different areas in large scale cities and analyze the effect of different precision and slot time on predictability. We conclude that using a proper time slot is an efficient way of prediction and a higher predictability can be achieved if the requirement for precision is reduced. Among all the cases we study, we find the highest potential predictability in Beijing and Shanghai in the case with smallest slot time and lowest requirement for precision.

Vehicular communications networks (VCNs) are created by vehicles equipped with short and medium range wireless communication technology. They include vehicular ad-hoc networks (VANETs), vehicle-to-vehicle and vehicle-to-infrastructure communications. VCNs enable a plethora of important applications and services, ranging from active safety or safety of life applications to traffic information, music/maps download and multi-hop internet connection. Recently, the promises of wireless communications to support vehicular safety applications have led to several national/international projects around the world. These include the consortia like Vehicle Safety Consortium (US), Car-2-Car Communication Consortium (Europe) and Advanced Safety Vehicle Program (Japan), standardization efforts like IEEE 802.11p (WAVE), and field trials like the large-scale Vehicle Infrastructure Integration Program (VII) in the US. All these efforts have as a main goal to improve safety in vehicular environments by the use of wireless communications, but also consider transport efficiency, comfort and environment. In comparison to other communication networks, VCNs come with unique attractive features: unlimited transmission power, predictable mobility and plethora of potential applications. However, to bring its potency to fruition, VCNs have to cope with formidable challenges that include: rapidly changing topology subject to frequent fragmentations and congestions, lack of connectivity redundancy, and the stringent application requirement on real-time and robust message delivery. In this chapter, we present a detailed description of the state of the art of this fast-moving research area pointing to research, projects and standardization efforts that have been done. We explore the unique features and challenges that characterise these highly dynamic networks as well as their requirements with respect to applications, types of communication, self-organization and security. We discuss various forwarding and routing strategies focussing on position-based techniques including ‘anchor-based routing‘. We survey various ‘intelligent flooding’ and information dissemination approaches. Scenarios for highways and cities are taken as example. We conclude by exploring future research directions in this field.

Utility maximization data scheduling in drone-assisted vehicular networks

Survey on blockchain-based applications in internet of vehicles

Considering bandwidth-hungry and low latency requirement of vehicular communications applications, we propose a novel dynamic reinforcement learning-based slicing framework and optimization solutions for efficient resource provisioning in virtualized network for D2D-based vehicle-to-vehicle (V2V) communication. The aim is to balance resource utilization and quality of service (QoS) satisfaction levels for multiple slices. The slicing framework is designed as a three-stage layered framework. In the first stage, we propose dynamic deep reinforcement learning-based virtual resource allocation scheme to allocate distinct resources to slices. In the second stage, we aggregate the D2D resource portion of the slice resource for D2D-based V2V communication. In the third stage, due to the computational complexity and signaling overhead of the physical resource allocation, we transform the problem into a convex optimization problem and solve with an alternating direction method of multipliers-based distributed algorithm. Performance results are provided in terms of resource utilization, QoS satisfaction and throughput to show the benefit of integrating resource slices dedicated to supporting interslice D2D-based V2V communication in vehicular network.

For vehicular networking purposes the main communication standard approach on global scale is the IEEE 802.11p. This protocol-based system, together with the 3G cellular network, was the main elements of our car communications test-bed network. The developed test-bed implements an intelligent heterogeneous traffic safety network between cars and infrastructure. It also offers the possibility to exploit vehicle based sensor and observation data in order to generate intelligent real-time services and a service platform for vehicles. The main goal was to improve traffic safety with accident and weather condition related accurate services, but also to offer a platform for true bi-directional Internet-like networking experience tailored cost-effectively to vehicular environments. This paper presents the field test results of a new standard system for car communication, IEEE 802.11p vehicular networking measurements in Northern Finland (with supporting simulations). The results achieved in developed heterogeneous networking system (with special services) pilot testing are presented also. Based on the experience gained from both field measurements and pilot deployment, we propose a system deployment strategy for simple scenarios with general system efficiency estimation. It is shown that the solution has clear potential for a comprehensive heterogeneous vehicular communication and safety network.

Intelligent Transportation Systems (ITS) are gaining momentum among researchers. ITS encompasses several technologies, including wireless communications, sensor networks, data and voice communication, real-time driving assistant systems, etc. These states of the art technologies are expected to pave the way for a plethora of vehicular network applications. In fact, recently we have witnessed a growing interest in Vehicular Networks from both the research community and industry. Several potential applications of Vehicular Networks are envisioned such as road safety and security, traffic monitoring and driving comfort, just to mention a few. It is critical that the existence of convenience or driving comfort services do not negatively affect the performance of safety services. In essence, the dissemination of safety services or the discovery of convenience applications requires the communication among service providers and service requesters through constrained bandwidth resources. Therefore, service discovery techniques for vehicular networks must efficiently use the available common resources. In this thesis, we focus on the design of bandwidth-efficient and scalable service discovery protocols for Vehicular Networks. Three types of service discovery architectures are introduced: infrastructure-less, infrastructure-based, and hybrid architectures. Our proposed algorithms are network layer based where service discovery messages are integrated into the routing messages for a lightweight discovery. Moreover, our protocols use the channel diversity for efficient service discovery. We describe our algorithms and discuss their implementation. Finally, we present the main results of the extensive set of simulation experiments that have been used in order to evaluate their performance.

Vehicular networking is an innovation with a variety of applications such as road safety, traffic efficiency, and autonomous driving. It encompasses vehicle-to-vehicle and vehicle-to-infrastructure communication to support cooperative communication. However, there are many challenges to secure exchanged messages. Currently, secure data communication in vehicular networks is mostly carried out by central security supporting elements, i.e Public Key Infrastructure (PKI). Due to increasing numbers of vehicles, connected and autonomous cars, the corresponding environmental concerns, and the evolved mobility forms, PKI-based security will not be sufficient for the security of the future vehicular ecosystem. Therefore, a new vehicle identity scheme and a novel distributed and autonomous model for recognizing authorized and non-authorized vehicles are needed. Biological systems can provide examples of how evolution has solved similar problems of identifying actors in distributed systems. In ant colonies, for example, nestmate recognition is a distributed and cooperative colony-wide process. Based on the principles at work in an ant colony, we propose a novel, distributed and dynamic, collective and cooperative, intelligence-based recognition of identity in vehicular networks. We will develop detailed analogies between ant colonies and vehicular networks, model the identity recognition processes of social insects, and simplify and tune these models for use in vehicle networks. This will enable a resilient, efficient and secure distributed communication system based on Cyberphysical Identification Component (CIC) profiles.