Road traffic management based on self-load-balancing approach

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant.

This paper considers the downlink data synchronization from the digital twin (DT) to the vehicle, in which a vehicle drives through consecutive roadside units (RSUs) along its trip, and the DT on the cloud transmits the data to the vehicle through the relay of RSUs. To this goal, the DT first chops the data into blocks and cache them in the RSUs along the driving path of the vehicle. The vehicle can then retrieve the blocks when driving into the RSU's coverage to recover the data. Since RSUs have different cache capacities and communication costs, the DT needs to determine how to optimally distribute the data blocks at RSUs so that vehicles can finish downloading all the data before the deadline yet with the minimal cost. To determine the optimal delivery strategy of DT, we model the problem as an optimization framework subject to the time-varying wireless channel of RSUs, their service load and the communication cost. We then resort to the Lyapunov optimization to derive a distributed solution. Using extensive simulation results, we demonstrate that our scheme can effectively reduce the cost of data synchronization and improve the network load performance.

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.

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.

Vehicular Ad-Hoc Network (VANET) is a form of Peer-to-Peer (P2P) wireless communication between vehicles, which is characterized by the high mobility. In practice, VANET can be utilized to cater connections via multi-hop communication between vehicles to provide traffic information seamlessly, such as traffic jam and traffic accident, without the need of dedicated centralized infrastructure. Although dedicated infrastructures may also be involved in VANET, such as Road Side Units (RSUs), most of the time VANET relies solely on Vehicle-to-Vehicle (V2V) communication, which makes it vulnerable to several potential attacks in P2P based communication, as there are no trusted authorities that provide authentication and security. One of the potential threats is a Sybil attack, wherein an adversary uses a considerable number of forged identities to illegitimately infuse false or biased information which may mislead a system into making decisions benefiting the adversary. Avoiding Sybil attacks in VANET is a difficult problem, as there are typically no trusted authorities that provide cryptographic assurance of Sybil resilience. This paper presents a technique to detect and mitigate Sybil attacks, which requires no dedicated infrastructure, by utilizing just V2V communication. The proposed method work based on underlying assumption that says the mobility of vehicles in high vehicle density and the limited transmission power of the adversary creates unique groups of vehicle neighbors at a certain time point, which can be calculated in a statistical fashion providing a temporal and spatial analysis to verify real and impersonated vehicle identities. The proposed method also covers the mitigation procedures to create a trust model and announce neighboring vehicles regarding the detected tempered identities in a secure way utilizing Diffie-Hellman key distribution. This paper also presents discussions concerning the proposed approach with regard to benefits and drawbacks of sparse road condition and other potential threats.

Vehicular communication is a crucial component of intelligent transportation systems (ITS) upon which many services are based starting with relaying traffic conditions up to enabling autonomous driving. Using a backhaul link, a road side unit (RSU) is assumed to fetch content from the Internet to serve the requests of incoming vehicles while they are within its range. Unless an effective caching mechanism is employed at the RSU, popular content might be unnecessarily retrieved from the network backhaul repeatedly especially in scenarios with large number of vehicles. This incurs significant capital and operating expenditures to RSU operators due to backhaul leasing and utilization. In this work, we explore cost-effective solutions for RSU operators by leveraging the content that is readily available on vehicles within the RSU's coverage range to intelligently cache popular content and serve newly arriving vehicles. However, due to the limited RSUs storage capacity and continuous flow of contents, the problem of establishing an RSU caching strategy to maximize the serving rate of incoming vehicles in an energy-efficient manner is challenging. Thus, we propose a technique based on deep reinforcement learning and demonstrate its effectiveness compared to the state-of-the-art.

Renewable energy powered road side units(RSUs) in Internet of Vehicles(IoVs) are a desirable green alternative choice compared to the traditional electric grid, because it not only extends the service range of IoVs but also saves the cost on energy. However, the energy on RSUs is limited and usually influenced by communication policies. Therefore, the communication scheduling policy on RSUs plays an important role on the persistence of network, which is still an open topic to be solved. In this paper, we focus on the communication scheduling problem on RSUs, and propose an LSTM-based communication scheduling algorithm for RSUs in IoVs. The scheduling algorithm is composed of three parts - deep learning clustering, LSTM-based traffic prediction, and a vehicle access scheduling algorithm. At last, we conduct an extensive simulation, and the simulation results indicate that our algorithm can achieve a better performance than the no scheduling mechanism.

We present in this paper a new algorithm for urban traffic light control with mixed traffic (communicating and non communicating vehicles) and mixed infrastructure (equipped and unequipped junctions). We call equipped junction here a junction with a traffic light signal (TLS) controlled by a road side unit (RSU). On such a junction, the RSU manifests its connectedness to equipped vehicles by broadcasting its communication address and geographical coordinates. The RSU builds a map of connected vehicles approaching and leaving the junction. The algorithm allows the RSU to select a traffic phase, based on the built map. The selected traffic phase is applied by the TLS; and both equipped and unequipped vehicles must respect it. The traffic management is in feedback on the traffic demand of communicating vehicles. We simulated the vehicular traffic as well as the communications. The two simulations are combined in a closed loop with visualization and monitoring interfaces. Several indicators on vehicular traffic (mean travel time, ended vehicles) and IEEE 802.11p communication performances (end-to-end delay, throughput) are derived and illustrated in three dimension maps. We then extended the traffic control to a urban road network where we also varied the number of equipped junctions. Other indicators are shown for road traffic performances in the road network case, where high gains are experienced in the simulation results.

VANET is a next generation communication technology where vehicles create an autonomous network with assistance of RSU (Road Side Units). VANET provides legitimate information to the users on the road, in order to increase the road and user’s safety. It provides useful information to the vehicles about directions, location mapping, premises, etc. The fast propagation of emergency and local warning messages to the approaching vehicles will be helpful for preventing secondary accidents. If security of the network is not guaranteed, several attacks may occur, thereby alert messages may not reach to the RSUs on time or message may get spoof due to attack on it. Major attacks on emergency messages such as timing attack, spoofing attack, DoS attack, Sybil attack, etc. would be considered to mitigate and make the communication and infrastructure more secure. One of the major emergency message attack is Sybil, which is used by selfish message propagators to claim authenticity of their message by propagating with multiple identities. By deceiving other nodes with their false support using multiple identifies, they can achieve various purpose in network like vehicle re-routing, speed shift etc. This work proposes a RSU cooperative detection mechanism involving triangulation and fake propagation by RSU to identify the Sybil attacks in the VANET. The proposed methodology secures fast communication emergency messages to prevent secondary accidents by validating the message sources, nodes will accept only the messages from RSU which are digitally signed and source validated against Sybil nodes. The performance evaluation is made with existing methodology and effectiveness of the proposed methodology results demonstrated in article. The spatial location and trajectory of attacker is learnt effectively with RSU cooperation in the proposed work.

In this paper, we propose a secure data downloading protocol with privacy preservation in vehicular ad hoc networks (VANETs). In the data downloading application, vehicles send requests, such as where is the nearest gas station, at a road side unit (RSU) and receive the corresponding responses from application servers via current or the following RSUs. It would be easy for eavesdroppers to get vehicles' private information if semi-trust RSUs are compromised because both request and response messages are forwarded by them. Therefore, we develop a protocol which enables vehicles to download data securely from RSUs with their privacy under protection even when one or multiple RSUs are compromised. Our protocol guarantees vehicles exclusive access to their requested data while eavesdroppers can not obtain any private information of the vehicles. Possible attacks and the corresponding solutions as well as privacy evaluation are discussed to demonstrate the performance of the proposed protocol.

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.

Vehicular ad hoc network (VANET) is a wireless ad hoc network which is created among vehicles, road side units (RSU) and regional trusted authorities. It provides inter-vehicle communications as well as communication between vehicles and RSUs. An authentication framework has been proposed in order to establish secure communication in VANET environment. Privacy of the vehicles has to be ensured in the network, which avoids misuse of private data. Self-generated public key cryptography based pseudo IDs are used instead of original IDs of vehicles for privacy preservation. The proposed scheme uses id-based signature (IBS) and ID-based online/offline signature (IBOOS) scheme for authentication purpose. Communication between vehicles and RSUs are authenticated using IBS scheme and IBOOS scheme authenticates communication between vehicles. By using these schemes, communication among vehicles under different RSUs and RTAs are also authenticated. Security attacks based on authentication like impersonation attack, Sybil attack are also resolved and the malicious nodes are isolated from the network. Vehicle-to-vehicle authentication without RSUs is also carried out by using RSA algorithm. Finally, the performance of the system is compared with recent schemes. Simulation results show that the proposed scheme provides secure communication among vehicles in the network with less communication and computation overhead.

This paper investigates the joint service placement and model partitioning problem for accelerating DNN inference in edge intelligence empowered vehicle networks, considering the vehicle mobility and the caching capability of Road Side Unit (RSU). To avoid the degradation of DNN inference performance due to the cold start during the vehicle's service node switching, we propose three different service placement solutions, including processing locally, directly selecting one RSU as server node for collaborative offloading, and selecting one RSU as relay to maintain the connection with original server node. We model the RSU network as an undirected weighted graph and formulate an optimization problem to minimize the DNN inference latency by jointly optimizing the DNN service placement and DNN model partitioning. To solve this problem, we propose a Graph Neural Network Reinforcement learning-based service Placement and model Partitioning algorithm (GRPP). Initially, we extract the feature embedding of RSU nodes and obtain the DNN service placement solution based on Graph Neural Network (GNN). Furthermore, we design a Multilayer Perceptron (MLP) to generate the DNN model partitioning point. Specifically, the parameters of GNN and MLP are learned through an actor-critic based reinforcement learning structure. Finally, we generate the simulation model via traffic simulator Simulation for Urban MObility (SUMO) and give a comprehensive performance evaluation. Simulation results indicate that our proposed scheme can decrease the DNN inference latency by up to 64.51% compared with the benchmark.

In this paper, a distributed cyclic delay diversity (dCDD) is proposed to the cooperative infrastructure-to-vehicle (I2V) system comprising one road side unit (RSU). At a particular time, to connect a RSU and a target vehicle outside each other's transmission range, a multiple number of vehicles located within the transmission ranges of both the RSU and the target vehicle are configured to operate as the dCDD based decode-and-forward (DF) cooperative relays. By using dCDD in the I2V system, the transmission range of the RSU is extended without the need of full channel state information of the vehicles at the RSU, and the transmit diversity gain can also be achieved. For this new preliminary setting of the I2V system, we conduct performance analysis. The simulations are conducted to verify the outage probability. The asymptotic outage diversity gain is also investigated and justified by the link level simulations.

In this paper, we consider the problem of disseminating data in Infrastructure-to-Vehicular (I2V) networks. In particular, given a fixed Road Side Unit (RSU) we evaluate the maximum amount of data that can be reliably transferred from the RSU to the vehicles passing in its proximity through a recently proposed multihop probabilistic broadcasting protocol, namely Irresponsible Forwarding (IF). In particular, referring to highway-like Vehicular Ad-Hoc Networks (VANETs), we first characterize a mobile scenario in such a way to make a direct comparison with a static scenario meaningful, taking into account a physical characterization of the network (e.g., in terms of vehicle spatial density). Then, we carry out a comparative investigation considering mobile and static IEEE 802.11 networking scenarios. Our results show that from a single packet perspective, the vehicles' mobility does not affect the behavior of the IF protocol, at least in the considered highway scenarios. However, different conclusions are reached when an information flow (i.e., a series of consecutive packets) is considered. In this context, we determine the maximum amount of data that can be transferred from the RSU to the mobile vehicles passing through a certain region of interest around the RSU.