Roadside Units Deployment Research Articles

AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers’ and passengers’ comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.

A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios

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.

Roadside Unit Deployment of Cooperative Vehicle-Infrastructure System Based on Digital Measurable Image Method

As the trend of the new generation of intelligent transportation systems, the application of the cooperative vehicle-infrastructure system (CVIS) is important to reduce traffic congestion and accidents. For the cooperative vehicle-infrastructure system, the roadside unit (RSU) is a new application without enough deployment experience. Meanwhile the deployment of traditional mechanical and electrical infrastructure is generally based on experience and is easy to change the design. This paper analyzes the key technical parameters of RSU and the Digital Measurable Image (DMI) method in context of the CVIS. After collecting field data from a smart highway pilot project (the Deqing-Fuyang expanded Huzhou section in Chang-Shen Highway) and matching the DMI method with highway mileage pile numbers, an RSU deployment scheme based on DMI data was completed. It provides significant support for smart highway demonstration projects with cooperative vehicle-infrastructure intelligent management and control applications.

Optimal Exploitation of On-Street Parked Vehicles as Roadside Gateways for Social IoV—A Case of Kigali City

Vehicular Ad Hoc Network (VANET) is a subclass of Mobile Ad Hoc Network that mainly consists of moving and/or stationary vehicles, connected through wireless protocols such as IEEE 802.11p and wireless access in vehicular environments (WAVE). With the evolution of the Internet of Things (IoT), ordinary VANET has turned to the Internet of Vehicles (IoV), with additional social aspects, a novel extension themed SIoV has become common in urban areas. However vehicular wireless communication paradigms exhibit short radio communication. This problem has always been approached by supplementing moving vehicles with stationary Road Side Infrastructures, commonly known as roadside units (RSUs). The penetration of such RSUs on the global market is very low; furthermore, their procurement, deployment, and maintenance costs are prohibitively very high. All mentioned challenges have discouraged the widespread deployment of roadside infrastructure especially within large urban scenarios. With this research, we leverage on-street parked vehicles to allow them to exist as temporal gateways in the case study area. A novel modeling technique is introduced to enable a specific Percentage of parked vehicles to take up the role of roadside gateways for a certain percentage of their parking time. A mobile application is implemented that manages parking duration of the vehicle, based on the arrival, and departure time frames. Two more existing strategies were discussed (road-intersection RSUs deployment approach and Inter-vehicle scheme) to validate our proposed method through comparative studies. To evaluate the network performance evaluation, we compare two performance metrics, that is, Packets success delivery rate, and overall packets throughput under numerous vehicle densities. Using parked vehicles as temporal roadside gateways has demonstrated better results in comparison to intersection based RSUs deployment approach, and free vehicle to vehicle communication approach.

Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic

In the foreseeable future, the traffic stream will be likely mixed with connected automated vehicles (CAVs) and regular vehicles (RVs). In the mixed traffic environment, when following a RV, due to the lack of vehicle-to-vehicle communications, it may take longer time for a CAV to sense and react than a human driver, which results in longer time headway and the loss of highway throughput. To address such a connectivity gap, this paper investigates an infrastructure-based solution, i.e., the deployment of roadside units to help CAVs in the heterogeneous traffic stream. Specifically, it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage. In doing so, we first derive an analytical link performance function to capture their impact on the link capacity and travel time, and then develop a network equilibrium model to gauge their effect on travelers’ route choices and thus the flow distribution of both RVs and CAVs across the whole network. This modeling development will allow us to conduct a cost-benefit analysis for a given deployment plan of roadside units. For fair analyses, we further develop an optimization model to determine the optimal deployment plan for a given budget, while focusing on the worst case of its impact, because the flow distribution resulting from our network equilibrium model is not unique. Such a model provides a conservative estimate of the benefit brought by roadside units. Lastly, we offer case studies to demonstrate the models and unveil the potential of such an infrastructure-based solution.

Minimizing transmission delays in vehicular ad hoc networks by optimized placement of road-side unit

In a vehicular ad hoc network (VANET), a road side unit (RSU) is a network traffic transmitter statically placed along the route to facilitate communication between vehicles and network infrastructure. The transmission and propagation delays, and the capacity of a VANET greatly depend upon the relative positions of RSUs along the road. In this paper, we investigate the problem of RSU placement on a highway-like roadway and propose a scheme that reduces network latency while ensuring good network capacity. In this regard, an integer linear programming model with the objective of minimizing network latency has been developed that depicts the network under consideration. Optimization techniques have then been applied to determine the RSU deployment that provides the minimum network latency. The proposed scheme was validated by generating traffic mobility patterns by using VanetMobiSim and performing simulations in NS2. In-depth comparative analyses of the proposed scheme with uniform distribution scheme and cost-effective strategy showed a reduction of 25% and 10% in network latency, respectively and thus established the superiority of the proposed solution.

An efficient roadside unit deployment method for vehicular ad-hoc networks

Abstract In big cities, with heavy traffic, tracking vehicle movements undoubtedly generates big data, which can be cumbersome for real time analysis. In the other hand, the process of roadside unit (RSUs) deployment in Vehicular Ad-hoc Networks (VANETs) mainly relies on the analysis of vehicles trajectories, which is considered as big sequential data. The complexity comes from (i) the number of transactions recorded at each time and (ii) the redundancy of transactions in the database. In this respect, several research works focusing on RSUs deployment have been proposed. The thriving challenge would be to (i) reduce the deployment cost by minimizing as far as possible the number of used RSUs; and (ii) to maximize the coverage ratio. In this paper, we introduce a spatio-temporal RSU deployment method consisting of two main parts namely ”finding representative transactions from the sequential database” and ”computing coverage”. The first one identifies the representative transactions, a.k.a transactions having a high utility, from a large sequential database. The second part computes a minimal set of junctions, where RSUs have to be ideally placed. The performed experiments show that our scheme outperforms its competitors in terms of cost, time performance as well as coverage ratio.

基于改进蝙蝠算法的车联网路侧单元部署优化研究

基于改进蝙蝠算法的车联网路侧单元部署优化研究

A roadside unit deployment framework for enhancing transportation services in Maghrebian cities

SummaryRoadside units (RSUs) have a crucial role in maintaining vehicular ad hoc networks (VANETs) connectivity and coverage, especially, for applications gathering or disseminating nonsafety information. In big cities with complex road network topology, a huge number of costly RSUs must be deployed to collect data gathered by all moving vehicles. In this respect, several research works focusing on RSUs deployment have been proposed. The thriving challenge would be to (1) reduce the deployment cost by minimizing as far as possible the number of used RSUs and (2) to maximize the coverage ratio. In this paper, we introduce a spatiotemporal RSU deployment framework including three methods, namely, SPaCov, SPaCov+, and HeSPic. SPaCov starts by mining frequent mobility patterns of moving vehicles from their trajectories; then, it computes the best RSU locations that cover the extracted patterns. Nonetheless, SPaCov+ extracts the frequent mobility patterns as well as the rare ones to enhance the coverage ratio. HeSPiC is a budget‐constrained spatiotemporal coverage method that aims to maximize the coverage ratio subject to a budget constraint, which is defined in terms of RSU number. Performed simulations highlight the efficiency and the effectiveness of the proposed RSU deployment framework in terms of coverage ratio, deployment cost, network latency and overhead.

Augmented Powell-Based Krill Herd Optimization for Roadside Unit Deployment in Vehicular Ad Hoc Networks

Abstract 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.

Gamma-Reload Deployment: Planning the communication infrastructure for serving streaming for connected vehicles

Intelligent Transportation Systems may highly benefit from the development of communication networks integrating vehicles. In this work, we study the deployment of roadside units for delivering streaming for connected vehicles. We consider that the proper dissemination of streaming requires a given share of vehicles periodically meeting the communication infrastructure located at the roadside in time intervals smaller than a given threshold. By considering the share of vehicles and time threshold, we propose the Gamma Deployment strategy for defining the location and number of roadside units required for providing the specific coverage. After that, we extend the proposed strategy to also consider the data transfer rate that vehicles receive data from roadside units, and also the data consumption rate of streaming inside vehicles, and so, deriving the Gamma-Reload Deployment strategy. We compare the proposed strategies to the intuitive strategy of locating roadside units at the densest locations of the road network, and the results reveal that the proposed strategies may achieve significant economy on the costs for setting up the communication network, while providing similar QoS.

Joint Roadside Unit Deployment and Service Task Assignment for Internet of Vehicles (IoV)

Internet of Vehicles (IoV) is a promising Internet of Things application, where roadside unit (RSU) plays an important role for network service provisioning. How to select the number and locations of RSUs to deploy and allocate the traffic load to them is a critical and practical open problem. Most of the existing work focused on 1-D scenarios assuming unlimited RSU capacity, while a more practical 2-D case with limited RSU capacity has not been fully considered yet. In this paper, we investigate an RSU deployment problem for 2-D IoV networks considering the expected delivery delay requirements and task assignment. We formulate a novel utility-based maximization problem to solve the RSU deployment problem, where the utility function indicates the total benefit from the RSU deployment. We observe that each RSU has an irregular service area, which makes the problem much more difficult than the traditional facility location problem. Then, we design a utility-based RSU deployment algorithm (URDA), a linear programming-based clustering algorithm, to solve the problem. The gap between URDA and the optimal solution has been analyzed, which proved that the proposed URDA is near optimal if the deployment cost is low. Extensive simulations have been conducted to demonstrate the effectiveness and superiority of the proposed solution for IoV network service guarantee over other approaches.

Efficient Traffic Information Dissemination and Vehicle Navigation for Lower Travel Time in Urban Scenario Using Vehicular Networks

This article presents peer to peer systems for efficient traffic information dissemination and discovery as well as re-routing vehicles to lower travel time in urban environment. Both navigating the vehicles from the sparsest path and sending traffic packets from the paths with the highest density will turn a sparse path to one with congestion and, considering the limited capacity of the network, these packets will be dropped by the intermediate nodes. On the other hand, selecting multiple paths by means of peer to peer layer along with appropriately distributing the vehicles among the selected paths prevents vehicle congestion in various zones and appropriate distribution of the packets among zones with proper density instead of the highest one will stop their dropping by intermediate vehicles. All the same, establishing a peer to peer network on mobile nodes due to their high mobility will increase the overhead of creation and maintenance for the peer to peer network. Roadside unit (RSU) placement in appropriate locations as well as the creation of peer to peer networks on these nodes reduces peer to peer overheads; however, due to the considerable costs of RSU deployment and maintenance, creation of a peer to peer network on RSU would not be affordable. Therefore, this article tries to present a system that deploys a limited number of RSUs in the most appropriate places and uses them as super peer nodes, using vehicle nodes as peer to peer ones in other key intersections, not covered by the RSUs. Such a system reduces the high costs of RSUs’ deployment and maintenance; also lowering the high overhead of creation and maintenance of the peer to peer networks. Simulation results show the appropriate efficiency of the presented systems over the other related methods.

Message coverage maximization in infrastructure-based urban vehicular networks

The success of vehicular networks is highly dependent on the coverage of messages, which refers to the trajectory of messages over time. Many of the existing works primarily performed in 1-D environments (i.e., straight highways) and/or merely focused on vehicle-to-vehicle (V2V) communications to enhance the coverage in a given road network. Consequently, there still lacks a clear comprehension of using road infrastructures (e.g., roadside units), to improve the message coverage in 2-D environments (i.e., urban areas). In this paper, we study the problem of optimal utilization of roadside units in 2-D environments. Specifically, we develop a message coverage maximization algorithm (MCMA) that carefully deploys the roadside units to achieve the maximum message coverage. Considering that the vehicle density is heterogeneous across the road networks, we study the message coverage for V2V networks by deriving analytical lower bounds of message dissemination distance for areas with different vehicle densities. The MCMA then utilizes the derived lower bounds to estimate the minimum spacing allowed between neighbor roadside units based on the prevailing traffic stream and delay constraint of applications. In addition, we propose a disseminator selection algorithm for infrastructure-based urban vehicular networks to further improve the message coverage. By selecting desired types of applications (i.e., safety and non-safety), we obtain two different roadside unit deployment sets from MCMA for the evaluation purpose. Extensive simulation studies show that MCMA outperforms the alternative algorithms in terms of the message coverage and message dissemination speed. The results also demonstrate that MCMA improves traffic efficiency in a post-crash scenario.

OptDynLim: An Optimal Algorithm for the One-Dimensional RSU Deployment Problem With Nonuniform Profit Density

Proper deployment of roadside units (RSUs) is of crucial importance to vehicular ad hoc networks (VANETs). However, our understandings to the simple one-dimensional RSU Deployment (D1RD) problem with nonuniform profit density is still seriously limited. In this paper, we analyze the D1RD problem and try to design optimal algorithms for it. We first analyze the properties of the optimal solutions of the D1RD problem involving a single RSU, and then extend to multiple RSUs. Next, we propose an efficient technique named Dynamic Limiting (DynLim), which reduces the solution search space size considerably by adjusting search space limits dynamically. Finally, an optimal algorithm named OptDynLim is proposed based on the DynLim technique, and its optimality is proved. Numerical simulations validate the correctness of our analyzes and show that DynLim can usually reduce solution search space size by more than 99%.

Connectivity Analysis of a Highway With One Entry/Exit and Multiple Roadside Units

This paper studies the connectivity problem of vehicles moving on a highway road and considers a one-way road scenario where there exist one entry and exit, and multiple roadside units (RSUs) equidistantly deployed along the road. A connectivity analytical model is derived for analyzing the connectivity probability, taking into account the existence of the entry and exit and the deployment of multiple RSUs. In the considered road scenario, the entry and exit is uniformly distributed on the highway road. At the entry and exit, a vehicle may enter or exit from the road, or drive through the entry and exit with a certain probability. In deriving the connectivity analytical model, the highway road is divided into several segments and the connectivity probability of each segment is derived based on probability distribution theory. The connectivity probability of the highway road is then derived based on the connectivity probability on each sub-segment. Moreover, several parameters that may affect the connectivity of the road are taken into account, including a vehicle's arrival rate and moving speed, the number of RSUs deployed on the road, and a vehicle's driving-through probability at the entry and exit. The derived analytical model is justified based on simulation results. It can be used to investigate the effects of major parameters on the connectivity probability of a highway road, which is useful for the deployment of RSUs in such scenarios.

Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks.

As an auxiliary facility, roadside units (RSUs) can well improve the shortcomings incurred by ad hoc networks and promote network performance in a vehicular ad hoc network (VANET). However, deploying a large number of RSUs will lead to high installation and maintenance costs. Therefore, trying to find the best locations is a key issue when deploying RSUs with the set delay and budget. In this paper, we study the delay-bounded and cost-limited RSU deployment (DBCL) problem in urban VANET. We prove it is non-deterministic polynomial-time hard (NP-hard), and a binary differential evolution scheme is proposed to maximize the number of roads covered by deploying RSUs. Opposite-based learning is introduced to initialize the first generation, and a binary differential mutation operator is designed to obtain binary coding. A random variable is added to the traditional crossover operator to increase population diversity. Also, a greedy-based individual reparation and promotion algorithm is adopted to repair infeasible solutions violating given constraints, and to gain optimal feasible solutions with the compromise of given limits. Moreover, after selection, a solution promotion algorithm is executed to promote the best solution found in generation. Simulation is performed on analog trajectories sets, and results show that our proposed algorithm has a higher road coverage ratio and lower packet loss compared with other schemes.

Optimal and Greedy Algorithms for the One-Dimensional RSU Deployment Problem With New Model

Optimal road side unit (RSU) deployment is of crucial importance to vehicular ad hoc networks. The RSU deployment (RD) problem models in the literature usually lack the ability to describe curve-shaped roads and roads nonuniform statistics. We had proposed a more realistic RD problem model taking these features into consideration. However, our understanding of the RD problem with the new model is still seriously limited. In this paper, we try to deepen our understanding of the one-dimensional RD (D1RD) problem with the new model by analyzing its properties and designing optimal and efficient approximate algorithms for it. We first analyze the properties of optimal solutions of the D1RD problem. Next, suspecting that the D1RD problem with $n$ RSUs of different coverage radii is intractable, we propose two greedy-based algorithms (named as Greedy2P3 and Greedy2P3E) and show that Greedy2P3E's approximation ratio is at least $1-(\frac{n-1}{n})^2$ . Then, by exploiting the properties of the D1RD problem, we propose an optimal algorithm named OptGreDyn by combining greedy idea and dynamic programming, and prove its optimality. At last, we proved that, if applied to the RD problem with $n$ RSUs of identical radii, the approximation ratios of Greedy2P3 and Greedy2P3E are at least $\frac{2}{3}$ , and it is tight for Greedy2P3 when $n=3i$ , where $i$ is a positive integer. Simulation results verify the optimality of OptGreDyn, meanwhile show that Greedy2P3 and Greedy2P3E usually return near-optimal solutions with profit more than 98% of the optimal solutions, and they are preferable than the existing approximate algorithms.

RA-eV2V: relaying systems for LTE-V2V communications

Emerging Long Term Evolution-based vehicle-to-vehicle (LTE-V2V) communication will support exchange of safety-related broadcast messages among vehicles. Such messages are generated periodically to announce one's own current state to other neighboring vehicles. The message reception performance in LTE-V2V drops sharply as communication distance increases if the communication channel is in a non-line-of-sight (NLOS) situation. To alleviate the problem, we propose a relaying system, called relayassisted enhanced V2V (RA-eV2V). In RA-eV2V, Road side unit (RSU) is in charge of relaying messages, received from vehicular user equipments (V-UEs). The operation of RSU is fully independent from other entities, meaning that the deployment of RSU on the road has no constraint at all. Moreover, there is no change from the current 3GPP standard operation on V-UE side either. In our realistic simulation, RA-eV2V hugely outperforms the 3GPP baseline scheme, e.g., by up to 36.5% in terms of message reception ratio in NLOS situations. Moreover, the feasibility of RA-eV2V is proven through real-map based simulation, which realistically reflects the actual situations on the roads.

A constrained Delaunay Triangulation based RSUs deployment strategy to cover a convex region with obstacles for maximizing communications probability between V2I

In context of competent communication in Vehicular Ad-hoc Networks (VANets), an efficient algorithm for roadside units (RSUs) placement is considered to be a challenging task mainly due to the obstacles like buildings, trees, water-bodies and other constructions. The existing solutions available for this problem come with several limitations. While some of them consider only vehicle density and intersection popularity for RSUs position calculation (bypassing the global coverage), some others are unable to consider the obstacles present in the map to validate the model. In case of the hotspot based RSUs placement method, if the hotspot areas are changed for some reasons, the method does not work properly and even fails for real complex traffic scenarios. This paper presents a novel scheme for placing a given number of RSUs in a convex map and assigning transmission range to each of the RSUs in such a way that each and every position in the map can be covered certainly by at least one RSU despite several obstacles. The proposed method at first initial RSUs position is determined based on a Constrained Delaunay Triangulation (CDT) method to deploy the RSUs in obstacle free area, followed by an optimization method to find out the more significant position for RSUs deployment based on RSUs cost and end-to-end delay. And finally a multi-criteria decision making strategy for RSU selection is introduced for efficient communication between vehicle to infrastructure (V2I) or infrastructure to vehicle (I2V). The simulations have been performed in a real complex road traffic scenario of Ottawas downtown area considering all the obstacles. The simulation results show that the proposed method outperforms the existing methods with the improvement of about 7.7% in packet delivery rate, about 9% reduction in packet loss and about 22% reduction in end-to-end delay. Further, the impact has been analyzed by varying the vehicle density, vehicle flow and RSU cost. The proposed method is tested in different scenarios like Manhattan: Simple map, Erlangen: Medium map and Rome: Complex map to get more precise results.