Urban Vehicular Networks Research Articles

Performance Analysis of C-V2X Resource Allocation in an Urban Vehicular Network

This paper discusses the performance of a Cellular-Vehicle to Everything (C-V2X) in an urban vehicular network focusing on the Fifth Generation New Radio (5G NR). Urban areas with high volume of vehicles not only cause traffic jams and accidents but also affect the C-V2X communication. It requires a lot of resources and sometimes causes problems if resources are not well allocated. Limited resources need to be allocated efficiently to meet different needs and avoid congestion such as, Quality of Service (QoS), latency, and data rates. The main goal of this paper is to investigate the performance of C-V2X schedulers in the vehicular network. The objectives include developing a model of C-V2X network that includes Resource Allocation (RA) schedulers, assessing the schedulers in the model, and validating the performance of the RA schedulers. The system-level simulator has been deployed to evaluate the performance of the C-V2X communication with different schedulers for RA. The performance was analysed in terms of throughput and delay which were studied through simulation work conducted using OMNET++ with Simu5G framework. The results show that Deficit Round Robin (DRR) is better than Maximum Carrier to Interference Ratio (MAXCI) and Proportional Fair (PF) with 1.345% throughput and 0.033% delay. Therefore, it can be concluded that DRR scheduler has the best performance in terms of throughput and delay for VoIP traffic.

Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks

Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks

Estimating Quality-of-Service in Urban Vehicular Networks Through Machine Learning

Estimating Quality-of-Service in Urban Vehicular Networks Through Machine Learning

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

A new delay-based broadcast suppression mechanism for efficient emergency messages dissemination in CAVs environment

Network congestion is a major issue affecting communications in Connected and Autonomous Vehicles (CAVs) under high vehicle density scenarios. The common idea used by emergency message broadcast protocols to overcome this challenge is to reduce the number of retransmissions. This is achieved by suppressing redundant retransmissions while maintaining broadcast reliability. In this paper, we analyze the problem of inaccurate suppression of redundant retransmissions in delay-based broadcast protocols and propose uHBS (unHurried Broadcast Suppression), a new more efficient broadcast suppression mechanism. uHBS avoids inaccurate decisions to suppress or forward an emergency message by considering the occurrence of duplicate receptions of this message and using an indication about the channel’s busy status. Next, we use the proposed uHBS mechanism as a basis for designing uHBS-DP (uHBS based Dissemination Protocol), a novel delay-based protocol for broadcasting emergency messages in urban vehicular networks. The simulation results show that uHBS-DP, using the proposed broadcast suppression mechanism, significantly improves the efficiency of emergency message broadcasting by ensuring high reliability with low broadcasting overhead, compared to two other variants of uHBS-DP that use conventional suppression mechanisms. Furthermore, the results show a substantial improvement, compared to a well known protocol, in terms of reduced collision ratio (up to 36.57%), lower dissemination delay (up to 19.17%) and reduced broadcast overhead (up to 19.28%).

A Steiner Tree based efficient network infrastructure design in 5G urban vehicular networks

Autonomous or assisted driving under next-generation vehicular applications requires a very high data rate with ultra-reliable low latency. The design of a proper network infrastructure plays a primary role in supporting these demands. This necessitates the network infrastructure nodes, i.e., the edge nodes and 5G RSUs, to be efficiently placed and connected across a smart city in a cost-effective manner. Such planning under a restricted capital expenditure budget presents a significant research challenge. We address this issue by formulating it as a Steiner Tree with Profits, Budget and Hop constraint problem for designing cost-efficient network infrastructure in an urban vehicular scenario. The optimization maximizes the long-term profits under the budget and hops constraint. The problem under consideration is a known NP-hard problem, and therefore, we have employed a Breakout Local Search based heuristic to solve it. Performance evaluation with actual traffic in real cities with diverse setups reveals crucial insights for designing cost-efficient network infrastructure in 5G urban vehicular networks.

A Belief Propagation Solution for Beam Coordination in mmWave Vehicular Networks

Millimeter-wave communication is widely seen as a promising option to increase the capacity of vehicular networks, where it is expected that connected cars will soon need to transmit and receive large amounts of data. Due to harsh propagation conditions, mmWave systems resort to narrow beams to serve their users, and such beams need to be configured according to traffic demand and its spatial distribution, as well as interference. In this work, we address the beam management problem, considering an urban vehicular network composed of gNBs. We first build an accurate, yet tractable, system model and formulate an optimization problem aiming at maximizing the total network data rate while accounting for the stochastic nature of the network scenario. Then we develop a graph-based model capturing the main system characteristics and use it to develop a belief propagation algorithmic framework, called CRAB, that has low complexity and, hence, can effectively cope with large-scale scenarios. We assess the performance of our approach under real-world settings and show that, in comparison to state-of-the-art alternatives, CRAB provides on average a 50% improvement in the amount of data transferred by the single gNBs and up to 30% better user coverage.

A scalable time-division-based emergency messages broadcast scheme for connected and autonomous vehicles in urban environment

Connected and Autonomous Vehicles (CAVs) technology promises to revolutionize the transportation sector by solving many safety and traffic efficiency challenges while significantly enhancing passengers' and drivers' comfort. Most of these applications are built upon multi-hop broadcast of information among vehicles, leading to an increased contention and thus higher collision probability due to the limited bandwidth available for vehicular communications. In this paper, we propose a novel Time-Division-based Broadcast (TDB) scheme to mitigate interferences originating from hidden nodes (i.e., vehicles) to meet the stringent safety applications requirements. Then, we use this broadcast scheme as a basis for designing a new efficient and scalable protocol for emergency messages dissemination in urban vehicular networks (UV-TDB: an Urban, Vehicular and Time-Division-based Broadcast protocol). The simulation experiments show that UV-TDB significantly improves the performance of multi-hop broadcast and outperforms two state of the art protocols in terms of the achieved reduction in broadcast overhead (up to 77.9%), packet collisions ratio (up to 93.5%), interference rate (up to 43.5%) and increase in message reception rate (up to 337.7%). The results highlight also that UV-TDB adapts better to varying levels of vehicle density, making it more scalable.

Adaptive Resource Allocation for Diverse Safety Message Transmissions in Vehicular Networks

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception.

Efficient Model to Predict the Traffic Congestion through Deep Learning

Abstract: Traffic in urban centers has considered a worsening issue across the world, impacting development and people's daily lives. It also occurs in large and medium regions of India, posing a serious threat to the country's progress. Humans could gain knowledge from the administration and management of urban vehicular networks that road congestion can be ameliorated or diminished if we can anticipate road congestion which will occur within few moments or has already occurred in a few seconds and implement prompt, adequate traffic abatement methodologies. As a consequence, traffic congestion prediction is crucial for improving the transportation system's energy consumption and dependability. A variety of strategies to this problem have been investigated and discussed in this research article for this goal. This has enabled the successful implementation of our solution for a traffic congestion prediction system based on Artificial Neural Networks and Decision Making. The result analysis has been crucial in the realization of the improved accuracy of the prescribed approach. Keywords: Artificial Neural Network, Deep learning, Traffic Congestion prediction ,Preprocessing , Segarigation

Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and ~40% more than the state-of-the-art.

Dispatch of UAVs for Urban Vehicular Networks: A Deep Reinforcement Learning Approach

Due to the dynamic nature of connectivity in terrestrial vehicular networks, it is of great benefit to deploy unmanned aerial vehicles (UAVs) in these networks to act as relays. As a result, a remarkable number of studies have exploited UAVs to bridge the communication gaps between terrestrial vehicles, and sometimes despite their unoptimized mobility, their restricted communication coverage, and their limited energy resources. However, it was noted that for an intermittently connected vehicular network, UAVs could not cover all sparse areas all the time. Even worse, when deploying enough UAVs to cover all these areas, the probability of inter-UAV collisions increases, and it will be complex to control their movements efficiently. Consequently, it is required to dispatch an organized and intelligent group of UAVs to perform communication relays in the long term while keeping their connectivity, minimizing their average energy consumption, and providing an efficient coverage strategy. To meet these requirements, we propose a deep reinforcement learning (DRL) framework, called DISCOUNT (Dispatch of UAVs for Urban VANETs). Extensive simulations have been conducted to evaluate the performance of the proposed framework. It has been shown that the proposed framework significantly outperforms two commonly-used baseline techniques and some reinforcement learning methods in terms of energy consumption, coverage, and routing performances.

Solar Irradiance Reduction Using Optimized Green Infrastructure in Arid Hot Regions: A Case Study in El-Nozha District, Cairo, Egypt

In the Middle East and North Africa (MENA) region, studies focused on the relationship between urban planning practice and climatology are still lacking, despite the fact that the latter has nearly three decades of literature in the region and the former has much more. However, such an unfounded relationship that would consider urban sustainability measures is a serious challenge, especially considering the effects of climate change. The Greater Cairo Region (GCR) has recently witnessed numerous serious urban vehicular network re-development, leaving the city less green and in need of strategically re-thinking the plan regarding, and the role of, green infrastructure. Therefore, this study focuses on approaches to the optimization of the urban green infrastructure, in order to reduce solar irradiance in the city and, thus, its effects on the urban climatology. This is carried out by studying one of the East Cairo neighborhoods, named El-Nozha district, as a representative case of the most impacted neighborhoods. In an attempt to quantify these effects, using parametric simulation, the Air Temperature (Ta), Mean Radiant Temperature (Tmrt), Relative Humidity (RH), and Physiological Equivalent Temperature (PET) parameters were calculated before and after introducing urban trees, acting as green infrastructure types that mitigate climate change and the Urban Heat Island (UHI) effect. Our results indicate that an optimized percentage, spacing, location, and arrangement of urban tree canopies can reduce the irradiance flux at the ground surface, having positive implications in terms of mitigating the urban heat island effect.

A Credibility Score Algorithm for Malicious Data Detection in Urban Vehicular Networks

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control.

Distributed perception and model inference with intelligent connected vehicles in smart cities

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

BRT: Bus-Based Routing Technique in Urban Vehicular Networks

Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

Efficient data dissemination protocol based on complex networks\u2019 metrics for urban vehicular networks

Services that aim to make the current transportation system more secure, sustainable, and efficient constitute the Traffic Management Systems (TMS). Vehicular Ad hoc Networks (VANETs) exert a strong influence for TMS applications, due to TMS services require data, communication, and processing for operation. Besides, VANET allows direct communication between vehicles, and data are exchanged and processed between them. Several TMS services require disseminated information among decision-making vehicles. However, such dissemination is a challenging task, due to the specific characteristics of VANETs, such as short-range communication and high node mobility, resulting in several variations in their topology. In this article, we introduce an extensive analysis of our proposed data dissemination protocol based on complex networks’ metrics for urban VANET scenarios, called DDRX. Each vehicle must build a subgraph to identify the relay node to continue the dissemination process. Based on the local graph, it is possible to select the relay nodes based on complex networks’ metrics. Simulation results show that DDRX offers high efficiency in terms of coverage, number of transmitted packets, delay, and packet collisions compared to well-known data dissemination protocols. Also, DDRX provides significant improvements to a TMS that needs efficient data dissemination.

On the performance of social-based and location-aware forwarding strategies in urban vehicular networks

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work evaluates the performance of several location-based and social-aware forwarding schemes through emulations and in a real scenario. Gateway Location Awareness (GLA), a location-aware ranking classification, makes use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns. Aging Social-Aware Ranking (ASAR) exploits the social behavior of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles. To merge both location and social aforementioned algorithms, a HYBRID approach emerges, thus generating a more intelligent mechanism. For each strategy, we evaluate the influence of several parameters in the network performance, as well as we comparatively evaluate the strategies in different scenarios. Experiment results, obtained both in emulated (with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network) and real scenarios, show the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of mobile networks as a smart city network infrastructure.

Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks.

Remote monitoring applications in urban vehicular ad-hoc networks (VANETs) enable authorities to monitor data related to various activities of a moving vehicle from a static infrastructure. However, urban environment constraints along with various characteristics of remote monitoring applications give rise to significant hurdles while developing routing solutions in urban VANETs. Since the urban environment comprises several road intersections, using their geographic information can greatly assist in achieving efficient and reliable routing. With an aim to leverage this information, this article presents a receiver-based data forwarding protocol, termed Intersection-based Link-adaptive Beaconless Forwarding for City scenarios (ILBFC). ILBFC uses the position information of road intersections to effectively limit the duration for which a relay vehicle can stay as a default forwarder. In addition, a winner relay management scheme is employed to consider the drastic speed decay in vehicles. Furthermore, ILBFC is simulated in realistic urban traffic conditions, and its performance is compared with other existing state-of-the-art routing protocols in terms of packet delivery ratio, average end-to-end delay and packet redundancy coefficient. In particular, the results highlight the superior performance of ILBFC, thereby offering an efficient and reliable routing solution for remote monitoring applications.