Pedestrian collision avoidance in vehicular networks

Broadcast transmissions are currently finding extensive applications in vehicular ad-hoc networks, albeit primarily in the research phase. Given the importance of knowing the updated network details of each node in the network, and also considering the fact that the delay in transmission of messages is a crucial factor in collision avoidance, it is imperative to implement a broadcast network which will ensure minimum delay in transmission of messages between the nodes. In this paper, we primarily implement a multi-hop broadcast vehicular network for collision avoidance. We consider the mobility and traffic density of vehicles and simulate the end-to-end delay in message transmission for a sparse and dense network scenario. In order to ensure high reliability and get the optimum delay, we extend the scenario to include three different routing protocols - AODV, Cluster-based routing and OSPF - and perform a comparison based on the end-to-end transmission delay to determine the optimum routing algorithm. In order to account for all possibilities, the case of link failure as a consequence of sparse network scenario is also considered.

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%.

Low density of vehicles in rural areas and during the periods when the vehicle traffic is low in cities (such as at night) makes the classical ad-hoc network algorithms impractical for Vehicular Ad Hoc Networks. End-to-end connectivity requirement of these algorithms causes this deflection. Connections get broken when a suitable network element isn't available for data transfer. This problem which occurs in the low dense Vehicular 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 has been found. By this way, network connectivity is provided in a delayed manner between the vehicles which are momentarily not connected. 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 the vehicles and the desired throughput, lifetime of data is determined according to the network connectivity. Results contributes to the fulfilment of the Quality of Service (QoS) requirements in Vehicular Network applications.

Utility maximization data scheduling in drone-assisted vehicular networks

Survey on blockchain-based applications in internet of vehicles

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 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

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.

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.

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.

Object tracking through image sequences is one of the important components of many vision systems, and it has numerous applications in driver assistance systems such as pedestrian collision avoidance or collision mitigating systems. Blurred images produced by a rolling shutter camera or occlusions may easily disturb the object tracking system. In this article, a method based on convolutional and recurrent neural networks is presented to further enhance the performance and robustness of such trackers. It is proposed to use a convolutional neural network to detect an intended object and feed the tracker with found image. Moreover, by using this structure the tracker is updated every ′n′ frames. A recurrent neural network is designed to learn the object behavior for estimating and predicting its position in blurred frames or when it is occluded behind an obstacle. Real-time implementation of the proposed approach verifies its applicability for improvement of the trackers performance.

A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment.

Prediction of stopping distance for autonomous emergency braking using stereo camera pedestrian detection

In order to meet low-latency and ultrareliable requirements on safety services in vehicular networks, this paper proposes a novel Collision supervision and avoidance (CSA) algorithm for the contention based scalable media access control protocol. The twodimensional Markov chain model of adaptive backoff state transition criterion in CSA has been built, which could efficiently match the backoff states of nodes to the dynamic changes of vehicular networks. The scalable transmissions can be achieved through supervised trend and matching backoff mechanisms with three adaptive backoff modes. The packet transmit probabilities for the backoff modes have been derived with the theoretical result of the enhanced throughput. The simulation results show the remarkable scalability performance such as normalized throughput > 0.92, PDR > 86% and delay < 6.5ms even in the high-density and high-mobility environment.

Dedicated short range communications (DSRC) has been regarded as one of the most promising technologies to provide robust communications for large scale vehicle networks. It is designed to support both road safety and commercial applications. Road safety applications will require reliable and timely wireless communications. However, as the medium access control (MAC) layer of DSRC is based on the IEEE 802.11 distributed coordination function (DCF), it is well known that the random channel access based MAC cannot provide guaranteed quality of services (QoS). It is very important to understand the quantitative performance of DSRC, in order to make better decisions on its adoption, control, adaptation, and improvement. In this paper, we propose an analytic model to evaluate the DSRC‐based inter‐vehicle communication. We investigate the impacts of the channel access parameters associated with the different services including arbitration inter‐frame space (AIFS) and contention window (CW). Based on the proposed model, we analyze the successful message delivery ratio and channel service delay for broadcast messages. The proposed analytical model can provide a convenient tool to evaluate the inter‐vehicle safety applications and analyze the suitability of DSRC for road safety applications. Copyright © 2009 John Wiley &amp; Sons, Ltd.