Roadside Units Deployment in Hybrid VANETs with Synchronous Communication

DIVINE: Data offloading in vehicular networks with QoS provisioning

The focus of this chapter is twofold: information dissemination from infrastructure nodes deployed along the roads, the so-called Road-Side Units (RSUs), to passing-by vehicles, and content downloading by vehicular users through nearby RSUs. In particular, in order to ensure good performance for both content dissemination and downloading, the presented study addresses the problem of RSU deployment and reviews previous work that has dealt with such an issue. The RSU deployment problem is then formulated as an optimization problem, where the number of vehicles that come in contact with any RSU is maximized, possibly considering a minimum contact time to be guaranteed. Since such optimization problems turn out to be NP-hard, heuristics are proposed to efficiently approximate the optimal solution. The RSU deployment obtained through such heuristics is then used to investigate the performance of content dissemination and downloading through ns2 simulations. Simulation tests are carried out under various real-world vehicular environments, including a realistic mobility model, and considering that the IEEE 802.11p standard is used at the physical and medium access control layers. The performance obtained in realistic conditions is discussed with respect to the results obtained under the same RSU deployment, but in ideal conditions and protocol message exchange. Based on the obtained results, some useful hints on the network system design are provided.

With the adoption of sensing technology, vehicles are able to gather information about driver's environment, like speed, temperature, moisture and road condition, etc. The state-of-the-art wireless technologies also provide vehicles with the ability of communicating to their neighboring vehicles on the road, as well as the static frameworks on the road side. Vehicular Sensor Network (VSN) is an emerging paradigm in vehicular networks. In this paper, we propose a new VSN scenario, Road Probing. The Road Side Unit (RSU) initiates the probing process and selects the passing by vehicles as probes to collect traffic and environment information. The selected vehicles sense the desired data and forward it back to the RSU in a multi-hop fashion. A dynamically growing one-dimension probing chain originated from the RSU is formed by participated vehicles as the probing process going on. A mobility profile based probe selection method is developed to improve this chain's connectivity as it's the key issue to assure the quality of data collection. The performance of Road Probing is demonstrated by simulation experiments.

As the number of smart vehicles with advanced communication capabilities increases, the deployment of Roadside Unit (RSU) becomes particularly important in the vehicular ad hoc networks (VANETs).In this work, we tackle the challenging problem of placing RSUs at road intersections to achieve maximum coverage in urban areas while minimizing signal overlap, an NP-hard combinatorial optimization problem due to the exponential solution space.To dealing with limitations of the exhaustive search algorithms, a novel metaheuristic method, called the Coverage-Oriented Multi-Zone Crossover Genetic Algorithm (COMZC-GA) is proposed in this paper.This smart GA has been engineered to optimally determine the minimum number of RSUs and their possible locations.This study extends beyond simple coverage optimization by integrating an analysis of antenna characteristics and radio wave propagation phenomena, clarifying how physical layer constraints fundamentally interact with network topology.The performance of the proposed algorithm is evaluated in terms of performance metrics, including Packet delivery ratio (PDR) and end-to-end delay, its capacity of achieving high coverage rates with less overlapping zones.By investigating a complex problem model, considering chromosome structure and a multi-objective fitness function, the optimization algorithm shows its potential on attaining higher network performance and cost-effectiveness in comparison with traditional deployment schemes.

Due to its short latency, low transmission cost, and benefit in data security, the vehicle to roadside-units (V2R) technology is growing in importance in the VANET. Roadside unit (RSU) complicated location, however, has an impact on the RSU network in terms of time delay, transmission efficiency, etc., making it challenging to use large-scale RSU networks. In view of this, a cooperative transmission framework is devised for data transmission in VANET. The number of RSU and the time delay are used as the metrics for measuring the economy and network transmission performance, respectively, in order to create the RSU deployment optimization model in this article, which addresses the issue. A multi-objective evolutionary algorithm is then used to carry out the RSU deployment’s optimization. The results of experiments are based on taxi data from ShenZhen. The findings show that the suggested technique can reduce the number of RSU while enhancing the RSU network’s transmission capabilities.

The reliability of communication in vehicular networks depends mostly on the density of DSRC-enabled vehicles that form the network. In highway scenarios, and depending on the time of day, the probability of having a disconnected vehicular network can be very high, which hinders communication reliability. To improve communication in these scenarios, infrastructure points known as Road Side Units (RSU) may be used. RSUs, however, have an associated cost, and therefore the number of RSUs needs to be minimized while still providing a significant improvement on communications. In this paper we study the effect of including RSUs as relay nodes to improve communication in highway scenarios. We model the average time taken to propagate a packet to disconnected nodes (denoted as re-healing time) when considering both scenarios of connected and disconnected RSUs. We then compare the results of both these models and of a model with no RSUs. Results show significant improvements with RSU deployments, both connected and disconnected, particularly in multi-cluster communication scenarios.

Internet of vehicles (IoV) promises to provide ubiquitous information exchange among moving vehicles and reliable connectivity to the internet. Therefore, IoV is becoming more and more popular as the number of connected vehicles is increasing. However, the existing vehicular communication infrastructure cannot guarantee reliable connectivity because all-time information exchange for every travelling vehicle is not assured due to lack of required number of roadside units (RSUs) especially along the intercity highways. This study is aimed towards exploring the use of cost-effective dynamic deployment of RSUs based upon the road traffic density and by ensuring the Line of Sight (LOS) among RSUs and Cellular Network Antennas. The unmanned aerial vehicles (UAVs) have a potential to serve as economical dynamic RSUs. Therefore, the use of UAVs along the roadside for providing reliable and ubiquitous information exchange among vehicles is proposed. The UAVs will be deployed along the roadside and their respective placement will be changed dynamically based upon the current traffic density in order to ensure the all-time connectivity with the travelling vehicles and the other UAVs/Cellular Network antennas. The reliability of the proposed network will be tested in terms of signal strength and packet delivery ratio (PDR) using the simulation.

Following an investigation undertaken by the publisher, we have determined that this paper was accepted on the basis of a compromised peer review process. We hereby retract the paper. The corresponding author has been notified of the retraction. The retraction statement can be found here: https://doi.org/10.1520/JTE20259998. This article focuses on roadside unit (RSU) deployment based on the analysis of aggregation delay causes between the accident-occurring location and the nearby RSU. Information aggregation in vehicular ad hoc networks using RSU is one of the phenomenal concepts that look ahead in today’s recent advancements, since the consistency in aggregation and dissemination of data in a volatile network is high when compared with vehicle to vehicle communications. Deploying the RSU as appropriate in rural areas is cost-sensitive since the collection of information is not up to the mark when compared with urban regions. A mathematical model called Powell’s Method and a bioinspired algorithm, namely krill herd algorithm, have been hybridized and proposed in this research article for effective RSU deployment. The proposed algorithm has been tested under three different road maps, and the consistency of the algorithm has been evaluated under appropriate performance measures.

With the development of the vehicular communications technology, roadside units (RSUs) placement problem has recently received considerable attentions in vehicular ad hoc networks (VANETs). This paper addresses the roadside units (RSUs) placement problem for traffic flow coverage requirement number in vehicular ad hoc networks (VANETs). Given a set of road segments, some traffic flows and a coverage requirement number n, the problem is how to find the optimal placement of RSUs, such that each traffic flow is covered by at least n RSUs. We first investigate full RSUs placement problem, which place all the RSUs on the given road segments totally. We prove that this problem is NP-hard. Then, we propose a greedy algorithm to solve the problem and theoretical analysis is given. We also study incremental RSUs placement problem, which place the partial RSUs based on the deployed RSUs on the given road segments. Based on the proposed greedy algorithm, we also propose a algorithm to solve the incremental RSUs placement problem. Extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms.

Roadside Units Deployment (RSUD) is of great importance to smart transportation with the Internet of Things (IoT). It is believed to be not feasible for RSUD to cover and perceive the whole area due to the high installation and maintenance costs. The candidate locations set of RSUD may be huge for a future urban area with vehicle-to-everything (V2X) networks. Most of the previous studies tried to maximize the Roadside Units (RSU) coverage only and made few reports on emergency scenarios, such as accidents happening. We tried to find better candidate locations of RSUD in some grid road networks with equal length streets, and then chose some of these locations for final installation with a given budget to minimize the average reporting time of emergency messages in V2X networks. Firstly, we analyzed candidate locations of RSUD for different cases of RSUs and vehicles. Then we proposed a message dissemination model for RSUD with the V2X network, and a center-rule-based neighborhood search algorithm (CNSA for short). In this algorithm, we generated initial solutions with the center rule and then obtained better neighbor solutions. Numerical simulation results from small-scale urban streets showed that the proposed algorithm performs well on execution time. Simulation results with Veins and Simulation of Urban Mobility) (SUMO) verified the proposed model and CNSA for evaluating the RSUD scheme by distance instead of accident reporting time in urban areas with large-scale traffic flow.

Content caching in vehicular networks is a promising technology to dramatically reduce the request-response time and transmission delay. The existing caching policies often suffer from high computation and communication overhead and fail to well capture the dynamics of the vehicular networks and content popularity. In this paper, we propose a novel Cooperative Caching algorithm for vehicular networks with multi-level federated Reinforcement Learning (named CoCaRL) to dynamically determine which contents should be replaced and where the content requests should be served. In CoCaRL, Deep Reinforcement Learning (DRL) is employed to optimize the cooperative caching policy between RoadSide Units (RSUs) of vehicular networks, while a federated learning framework applies to reduce the computation and communication overhead in a decentralized way. To speed-up the convergence rate, we also develop a two-level aggregation mechanism for federated learning, where the low-level aggregation is performed at the RSUs and the high-level aggregation is executed at a Global Aggregator (GA). Through extensive simulation experiments, we demonstrate that our algorithm can: 1) achieve a higher hit rate than four baseline algorithms, 2) converge faster than original federated reinforcement learning without multi-level aggregation, and 3) perform good adaptability to different cache capacities and content quantities.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the Internet. For the reliable data upload and download, TCP can be used for vehicular networks. However, TCP requires the connection initialization using three-way handshaking for the data exchange between two end systems over the Internet. Thus, the efficient operation of TCP is important for data services in the vehicular networks. This paper proposes a method of TCP Context Migration Scheme (TOMS) for the enhancement of data services in vehicular networks. TOMS provides vehicles with proactive TCP connection initialization using a moving TCP proxy as a cluster head, which will have the Internet connectivity with a Road-Side Unit (RSU). A cluster member can initiate its TCP connection toward its corresponding TCP end-system (e.g., server and peer) via the TCP proxy within its cluster. The TCP proxy performs the TCP connection set-up for the sake of other cluster member vehicles and acknowledges the received TCP segments toward these vehicles. When the TCP proxy moves out of the communication range of the RSU, it transfers the TCP contexts of other vehicles to another vehicle, which will play the role of a TCP proxy through the proposed TCP context migration scheme. Also, the RSU works as a fixed TCP proxy for handling the acknowledgement of TCP segments and TCP timer handling (e.g., persist timer and keepalive timer) when there happens the disconnection between the moving proxy and the RSU. Thus, it is shown that our TOMS outperforms the legacy TCP in vehicular networks.

This paper proposed a mobility-aware multimedia data transfer mechanism using Multipath TCP in Vehicular Network. Since high transmission rate and low latency are the two key factors for multimedia data transmission that could provide stable video streaming services, therefore, we first adopted Multipath Transport Control Protocol which can transfer data concurrently for improving transmission rate and designed Quality-aware Data Distribution to dynamically allocate the data to different subflows. Moreover, in Vehicular Network, the mobile terminal, which means the vehicle, can communicate with remote server through roadside unit (RSU). However, the communication link between terminals and remote server will disrupt while the vehicle exceeding the communication range of roadside unit; and there also exists the situation that the vehicle is in the communication range of several RSU. Accordingly, we exploited a mobility-aware distance measurement for checking whether the vehicle has moved out of the communication range of any RSU or it is in multi-RSU's communication range. Afterwards, we designed a handover mechanism which transfer data to connected path (4G) for stable transmission using MPTCP when the vehicle exceeded the communication range of RSU; while one mobile can communicate with more than one RSU, we exploited a mechanism which can trigger new path for multipath data transmission and non-corporation Nash Equilibria was employed for solving the problem of fairness in the same kind of network technology multipath transmission. Simulation results show how mobility-aware multimedia data transfer mechanism improve the performance of transmission comparison with state-of-art solution.

Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical.

On the deployment of V2X roadside units for traffic prediction