Roadside Units Deployment Research Articles

A multi‐objective optimization model for RSU deployment in intelligent expressways based on traffic adaptability

AbstractThe intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well‐planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability‐based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality.

Optimizing Roadside Unit Deployment in VANETs: A Study on Consideration of Failure

Optimizing Roadside Unit Deployment in VANETs: A Study on Consideration of Failure

Considering traffic characteristics: Roadside unit deployment optimization algorithm based on dynamic division of road network subareas

AbstractGiven that the overall coverage deployment method fails to meet information needs in important areas, there are redundancies and deficiencies in the information provided. To enhance communication stability for roadside units (RSUs), improve information coverage at critical intersections and optimize algorithm efficiency. Here, a method for deploying RSUs is proposed that aims to optimize revenue in road network subareas. The road network is divided into several subareas based on critical intersections, node similarity, road segment correlations, and characteristics of RSU information transmission. Then, a roadway accessibility algorithm is developed that accounts for channel fading. Considering the robustness of wire network deployment, an improved traveling salesman problem (TSP) problem is proposed that includes candidate locations and constructs a model for optimal RSU deployment that maximizes consolidated revenue. Finally, using the Sioux Falls network as an example, the RSU deployment strategy is evaluated for the overall network and the road network after being subdivided. The results indicate that subdividing the road network improves the efficiency of the optimization solution, the information coverage of critical intersections increases by 1.8 times. The deployment optimization scheme of RSUs is directly influenced by various parameters such as bandwidth capacity and cost coefficient. When deploying RSUs in road network subareas, variations in total demand have minimal impact on RSU deployment, ensuring a stable deployment scheme.

A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars

A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars

An efficient multi-objective UAV assisted RSU deployment (MOURD) scheme for VANET

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

Greta: Towards a General Roadside Unit Deployment Framework

<i>Greta:</i> Towards a General Roadside Unit Deployment Framework

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

A spectral clustering-based deployment strategy for roadside units in vehicular edge computing environments

Roadside units deployment strategy in vehicular edge computing (VEC) environments plays a crucial role in balancing deployment expenses with user experience. This paper tackles this challenge by formulating the communication and computation cost of offloading vehicle tasks to RSUs and, subsequently, optimizing the RSU deployment problem to minimize the average task completion time. We propose the spectral clustering-based deployment strategy (SCBDS), which determines the optimal number and precise locations of RSUs by identifying clusters of vehicular traffic demands. Performance evaluation through simulations in real-world traffic scenarios demonstrates that SCBDS outperforms existing algorithms in terms of task completion time, showcasing its effectiveness in enhancing VEC efficiency and user experience.

A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.

Cluster-based RSU deployment strategy for vehicular ad hoc networks with integration of communication, sensing and computing

The integration of communications, sensing and computing (I-CSC) has significant applications in vehicular ad hoc networks (VANETs). A roadside unit (RSU) plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication. Due to the constraints of limited resources, RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method. However, direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue. In this paper, we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks. The renormalization method is compared with two deployment schemes: genetic algorithm (GA) and memetic framework-based optimal RSU deployment (MFRD), to verify the improvement of communication performance. Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.

Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price Algorithm

Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price Algorithm

Signal-Vehicle Coordination Control Modeling and Roadside Unit Deployment Evaluation under V2I Communication Environment

Signal-vehicle coordinated control holds substantial promise for enhancing urban transportation efficiency. However, its development faces notable challenges: (1) most existing studies have been conducted based on the assumption of perfect communication conditions. This assumption overlooks the significant impact of vehicle-to-infrastructure (V2I) communication quality on control performance, which leads to poor applicability in practice. (2) The evaluation of roadside unit (RSU) deployment for optimizing signal-vehicle control has not been well studied. Hence, the modeling of signal-vehicle coordination control and RSU deployment evaluation under V2I environment are studied in this paper. First, we introduce a communication model that characterizes the imperfections in communication between RSUs and connected vehicles (CVs). Second, we propose a model for signal-vehicle coordination control within this connected environment. This model integrates strategies from both signal control optimization and the speed optimization of CV platoons. Finally, to assess the impact of the RSU deployment parameters on the performance of signal-vehicle coordination control, we introduce a systematic evaluation method. The reduction in vehicle delays is introduced as the evaluation indicator for control performance. Six other indicators—the number of vehicles in the RSU communication domain, connectivity probability between the CV and RSU, number of vehicles whose speeds are successfully optimized, number of speed adjustments, green extension time, and overlap rate of the communication domains of multiple RSUs—are introduced as the observation indicators. The simulation experiments verify the effectiveness of the proposed model in implementing signal-vehicle coordination control under imperfect communication and environments in low-traffic, medium-traffic, and high-traffic scenarios. Furthermore, these experiments show the quantitative impact of RSU deployment parameters (communication distance, command transmission cycle, installation position, and number of RSUs) on control performance.

An RSU-Assisted Hybrid Emergency Message Broadcasting Protocol for VANETs

In vehicular ad hoc networks (VANETs), emergency messages broadcasting has been considered as one of the important parts for safety related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this paper, we propose an RSUs-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and roadside unit (RSU) deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

A Multi-Objective Roadside Unit Deployment Model for an Urban Vehicular Ad Hoc Network

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.

Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment

The rapid development of automated vehicle (AV) technologies enables us to explore how to update the urban road infrastructure to cater for the upcoming mixed-autonomy traffic with both AVs and human-driven vehicles (HVs). In this study, we aim to seek an optimal solution to integrate the deployment of AV-dedicated lane and roadside unit assisting automated driving subject to a limited budget by considering the route choice behaviors of AVs and HVs. An AV-dedicated lane segregates AVs from the mixed traffic to create a fully automated driving environment and eliminate disturbances from HVs. The deployment of roadside units assisting automated driving could overcome the connectivity gap for AVs and lower their headway when they follow an HV. We seek to develop a robust and optimal integrated deployment solution that accommodates the uncertain road capacity caused by the stochastic mixed AV and HV fleet sequence. We first establish the stochastic network equilibrium conditions for the mixed traffic, and formulate the integrated deployment problem as a mathematical program with complementarity constraints (MPCC). The MPCC is approximated by a mixed-integer linear programing (MILP) model, which allows existing algorithms for its global optimum. We further develop two interesting strategies, including domain reduction and breakpoint selection, to enhance the effectiveness of the MILP model. Numerical experiments are finally carried out to evaluate the feasibility of research methodology proposed in this study and find some valuable insights.

Fog-ROCL: A Fog based RSU Optimum Configuration and Localization in VANETs

Intelligent Transportation Systems (ITS) are one of the pillars of smart cities that enable smart traffic and smart mobility. Vehicular Ad-hoc Networks (VANETs) are utilized as platforms for ITS applications. In VANETs, vehicles collect relevant information from sensors and exchange information about road conditions and traffic status with each other and with roadside units (RSUs). Roadside units facilitate reliable communication among the vehicles and perform real-time processing for the sensed data before sending to the cloud. Although cloud computing offers high performance computational and storage resources, it does not conform to the real-time nature of the ITS applications and the massive amount of data exchange and generation rate due to its centralized nature and high communication latency. In this paper we propose a cost-effective strategy to solve the configuration and localization problem of fog-based RSU deployment in VANETs. The proposed strategy is able to assign the computational capacity of each fog node based on the amount of computational demand inside its coverage region. The problem is formulated as a Satisfiability Modulo Theories (SMT) problem. Our proposed strategy is more efficient than other strategies in terms of the total deployment cost and the overall satisfied computational demand.

An RSU Deployment Scheme for Vehicle-Infrastructure Cooperated Autonomous Driving

For autonomous driving vehicles, there are currently some issues, such as limited environmental awareness and locally optimal decision-making. To increase the capacity of autonomous cars’ environmental awareness, computation, decision-making, control, and execution, intelligent roads must be constructed, and vehicle–infrastructure cooperative technology must be used. The Roadside unit (RSU) deployment, a critical component of vehicle–infrastructure cooperative autonomous driving, has a direct impact on network performance, operation effects, and control accuracy. The current RSU deployment mostly uses the large-spacing and low-density concept because of the expensive installation and maintenance costs, which can accomplish the macroscopic and long-term communication functions but fall short of precision vehicle control. Given these challenges, this paper begins with the specific requirements to control intelligent vehicles in the cooperative vehicle–infrastructure environment. An RSU deployment scheme, based on the improved multi-objective quantum-behaved particle swarm optimization (MOQPSO) algorithm, is proposed. This RSU deployment scheme was based on the maximum coverage with time threshold problem (MCTTP), with the goal of minimizing the number of RSUs and maximizing vehicle coverage of communication and control services. Finally, utilizing the independently created open simulation platform (OSP) simulation system, the model and algorithm’s viability and effectiveness were assessed on the Nguyen–Dupuis road network. The findings demonstrate that the suggested RSU deployment scheme can enhance network performance and control the precision of vehicle–infrastructure coordination, and can serve as a general guide for the deployment of RSUs in the same application situation.