Utility maximization data scheduling in drone-assisted vehicular networks

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.

Vehicular ad-hoc networks (VANETs) aim to increase the safety of passengers by making information available beyond the driver’s knowledge. The challenging properties of VANETs such as their dynamic behavior and intermittently connected feature need to be considered when designing a reliable communication protocol in a VANET. In this study, we propose an epidemic and density adaptive protocol for data dissemination in vehicular networks, namely EpiDOL, which utilizes the opposite lane capacity with novel probability functions. We evaluate the performance in terms of end-to-end delay, throughput, overhead and usage ratio of the opposite lane under different vehicular traffic densities via realistic simulations based on SUMO traces in ns-3 simulator. We found out that EpiDOL achieves more than 90% throughput in low densities, and without any additional load to the network 75% throughput in high densities. In terms of throughput EpiDOL outperforms the Edge-Aware and DV-CAST protocols 10% and 40% respectively.

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 & Sons, Ltd.

Vehicular networks often consist of computational devices that collect data representing the external environment (e.g., road traffic volume, terrain conditions, and vehicle tracking). The data collected is prone to errors for two reasons: first, the processing algorithms in devices often have only limited capabilities that result in a fuzzy and imprecise representation of the external datum; second, some of the devices may be maliciously faulty mis-reporting the external datum. In such a setting, replicating the devices and voting on the environment data collected by them enhances the trust-worthiness of data reported to the application. The paper describes a cooperation mechanism that embodies two complementary functions: replication of devices and voting among devices. Device-level cooperation is realized using a tree-structured overlay set up over a vehicular network, in which the root node is attached to a data dissemination station and the leaf nodes are attached to the data collection devices in different geographic regions. Voting among the data collected is carried out at the leaf nodes whereupon a sanitized data propagates up the tree towards the root. An intermediate node may also carry out aggregation functions on the data arriving from its downstream tree segments and then forward the aggregated data upstream. The paper describes the voting-based data collection and dissemination as a middleware service.

We consider the problem of data dissemination in vehicular networks. Our main goal is to compare the application-level performance of fully distributed and centralized data dissemination approaches in the context of traffic advisory systems. Vehicular networks are emerging as a new distributed system environment with myriad promising applications. Wirelessly-connected, GPS-equipped vehicles can be used, for instance, as probes for traffic advisory or pavement condition information services with significant improvements in cost, coverage and accuracy. There is an ongoing discussion on the pros and cons of alternative approaches to data distribution for these applications. Proposed centralized, or infrastructure-based, models rely on road-side equipment to upload information to a central location for later use. Distributed approaches take advantage of the direct exchanges between participating vehicles to achieve higher scalability at the potential cost of data consistency. While distributed solutions can significantly reduce infrastructures' deployment and maintenance costs, it is unclear what the impact of "imprecise" information is to an application or what level of adoption is needed for this model to be effective. This paper investigates the inherent trade-offs in the adoption of distributed or centralized approaches to a traffic advisory service, a commonly proposed application. We based our analysis on a measurements study of signal propagation in urban settings and an extensive simulation-based experimentation in the Chicago road network.

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

Reinforcement learning based on routing with infrastructure nodes for data dissemination in vehicular networks (RRIN)

Vehicular Ad Hoc Networks (VANETs) have emerged as an exciting research and application area. The envisioned applications, as well as some inherent VANET characteristics such as highly dynamic topology, frequently disconnected network, and different and dynamic network density, make data dissemination a challenging task in these networks. Several approaches for data dissemination in VANETs have been recently proposed in the literature. However, more work needs to be done since most of the proposed solutions do not effectively address some or all of the main challenges in these scenarios such as the broadcast storm, network partition and temporal network fragmentation. In this paper, we provide a comprehensive and critical review of proposed techniques for data dissemination in VANETs and identify the main challenges in this area. For this, we first define suitable criteria to classify and organize some of the existing solutions. Next we identify and discuss some open issues and conclude with possible future research directions.

The prosperity of advanced applications in vehicular networks leads to frequent data requests of vehicles, necessitating an increased emphasis on data dissemination capabilities. Multicast transmission, enabling the simultaneous delivery of data to multiple receivers, stands out as a spectrally efficient approach for data dissemination. However, existing approaches in multicast transmission strategy for vehicular networks largely ignore the interference that can arise between multicast links, which results in a degradation of system performance. In this paper, we investigate the issue of joint multicast transmission and bandwidth allocation strategy for enhancing data dissemination in vehicular networks and formulate a problem to maximize the number of packets effectively transmitted by optimizing multicast link establishment, transmitted packets and bandwidth allocation. Because of the coupling of variables in the formulated problem, we decompose the problem into two subproblems and propose the conflict graph based multicast transmission strategy and the graph coloring algorithm based bandwidth allocation strategy for each subproblem. Then, an iterative algorithm is designed for joint optimization. Extensive simulations demonstrate the advantages of proposed strategy.

Main applications in vehicular networks are primarily looking for road safety improvement. To achieve this goal, positiondata information of all interfering road users must be conveyed effectively. This proposal examines the performance made possible using WAVE/DSRC standard when including pedestrians/two-wheels. First, we propose a specific message format for encapsulating pedestrian and two-wheel position-data information. Second, we propose a complete system for collision avoidance at intersection level. Subsequently, we perform tests to analyze the system performance in terms of packets reception rate and transmission delay using WAVE/DSRC standard in two modes of operation. Namely, we perform a comparison between using only the control channel to announce and forward messages as a safety service, and using one of the dedicated channels 172 or 184 after announcing the service on the control channel. Taking into account different levels of network densities, we investigate the modes of operation that provide lower congestion and channel saturation. The findings in this work are relevant to the design of data transmission patterns for pedestrian collision avoidance systems at intersections when using VANETs.

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.

Recent studies have shown that in realistic wireless sensor network environments links are extremely unreliable. To recover from corrupted packets, most routing schemes with an assumption of ideal radio environments use a retransmission mechanism, which may cause unnecessary retransmissions. Therefore, guaranteeing energy-efficient reliable data transmission is a fundamental routing issue in wireless sensor networks. However, it is not encouraged to propose a new reliable routing scheme in the sense that every existing routing scheme cannot be replaced with the new one. This paper proposes a Distributed and Reliable Data Transmission (DRDT) scheme with a goal to efficiently guarantee reliable data transmission. In particular, this is based on a pluggable modular approach so that it can be extended to existing routing schemes. DRDT offers reliable data transmission using neighbor nodes, i.e., helper nodes. A helper node is selected among the neighbor nodes of the receiver node which overhear the data packet in a distributed manner. DRDT effectively reduces the number of retransmissions by delegating the retransmission task from the sender node to the helper node that has higher link quality to the receiver node when the data packet reception fails due to the low link quality between the sender and the receiver nodes. Comprehensive simulation results show that DRDT improves end-to-end transmission cost by up to about 45% and reduces its delay by about 40% compared to existing schemes.