Clustering Of Vehicles Research Articles

Clustering of Commercial Vehicles Based on Fuel Type Using Machine Learning Technique

Clustering of Commercial Vehicles Based on Fuel Type Using Machine Learning Technique

Vehicle Classification using BiLSTM for Predictive Maintenance and Digital Twins

The service life of infrastructure is directly related to the traffic load. In the past, this traffic load has often been underestimated, resulting in significant damage to structures, especially steel bridges. In order to establish the paradigm of predictive maintenance in bridge engineering, prediction models for the state of damage and service life are necessary. In particular, the proportion of heavy trucks leads to a significant decrease in remaining service life. Therefore, knowledge of the composition and frequency of traffic is important. New methods from communications technology based on recurrent neural networks allow rapid identification of specific signals in monitoring data.A bidirectional Long Short-Term Memory Network is used to identify up to nine different vehicle types from the drive-by vehicle signals. The method was applied to monitoring drive-by data from a German wide span steel bridge with an orthotropic deck (Rhine bridge Duisburg-Neuenkamp). Validation was performed based on weight-in-motion data in combination with a finite element analysis. The extracted data allows a detailed analysis of the classification of vehicle types, sequence effects and clustering of vehicles. The data obtained can later be used in digital twin applications.

Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

Novel machine learning based authentication technique in VANET system for secure data transmission

<p>Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges in vehicular ad hoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system (IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology which attempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combined technology, the source metadata information collected from the inter planetary file system is stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralized vehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.</p>

Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

Vehicular networks are known for their dynamic topology, high mobility, and frequent disconnections. Unmanned aerial vehicles (UAVs) have been recently used as instant communication relays to bridge the communication gaps between terrestrial vehicles to improve connectivity in vehicular networks and overcome the aforementioned problems. Despite the existing work in the literature where each vehicle connects directly to UAVs, this work studies how clustering and different densities of vehicles affect delay and energy efficiency in a UAV-based vehicular network integrated with 5G technology. Consequently, this work addresses the problem of UAV enabling vehicular ad-hoc networks (VANETs) in a highway scenario, where UAVs serve as an effective complement to forward data packets between vehicles in the absence of sufficient fixed infrastructures in an emergency situation. The main objective of this work is to minimize the delay while maximizing the energy efficiency by minimizing the power consumption and maximizing the total data rate under realistic conditions, while nonorthogonal multiple access (NOMA) is also adopted as an alternative answer for the effective utilization of limited bandwidth. The free-flowing traffic follows a Poisson stochastic process where each vehicle is assigned a random speed selected from a truncated Gaussian distribution. To this end, a novel modification of fast global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means is adopted to partition vehicles, allowing communication between clusters by vehicle-to-vehicle links, while data packets between clusters are relayed through UAVs. By computing the convex approximation of the objective function and the constraints, the original problem with the mixed-integer, nonconvex, and nonlinear form is solved by the proposed iterative inner penalty function algorithm. Finally, extensive simulations are conducted to validate the superiority of the proposed method in terms of various metrics. The results indicate that the relaying task in the proposed UAV-assisted VANET based on 5G technology is perfectly suited to enhance the network connectivity.

TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks

On account of the highly dynamic topology of vehicular networks, network congestion and energy utilization are greatly increased, which directly affects the performance of VANETs. So, managing traffic and reducing energy consumption in the network becomes a challenging task in such huge mobility-based VANET networks. Thus, in this paper a new traffic and cluster-based network method is introduced, namely, Traffic-Aware Clustering based Routing Protocol (TACRP). The main aim of the approach is to improve traffic management in the network as well as to reduce energy consumption in it. In the constructed network, a Traffic Management Unit (TMU) is introduced to control the entire network traffic with the help of RSUs. Vehicles with similar speed and direction are grouped into a cluster to increase the network stability and help to reduce the energy consumption of the network. The clustering model provides principles associated with vehicles leaving the clusters, joining the clusters, cluster updates and inter-cluster communication, which makes the network more stable and reliable. For instance, in the proposed work the CH selection is based on centralization, weight, distance, and energy calculation. Such network settings facilitate successfully clustering of vehicles on the road. Simulation experimental analysis showed that the proposed TACRP routing protocol achieved better results in terms of energy efficiency, throughput, packet delivery ratio, and end to end delay of the network when compared with earlier methods, such as ECHS and NRHCS.

Гибридная модель эффективного представления данных в беспроводных транспортных сетях с динамической топологией VANET

At the moment, traffic information systems require the aggregation of big data to provide recommendations to vehicles in the current conditions, which leads to an increase in user comfort. The main tool for improving the level of safety was timely informing traffic participants about the current situation on the road, weather conditions, etc. In this case, if the network object is subjected to an attack and the data is replaced during transmission, then the disclosure of confidential information, the creation of emergency si¬tuations, etc. is possible throughout the visibility zone of the VANET segment. In this regard, the most urgent issue is ensuring security, including when transmitting traffic, and conducting an additional analysis of big data about anomalies and ongoing unauthorized actions. Aim. To develop a hybrid model for the efficient placement of source and intermediate data in wireless transport networks with a dynamic VANET topology, which represents a structural representation of a software-configurable network and edge computing tools, with the ability to optimally analyze data from network nodes and identify anomalies. Methods. The considered Edge computing approach consists in locating computing capacities in geogra¬phically distributed computing devices closer to end users. Software-configurable SDN networks transfer part of the control and physical transmission functions from routing and switches, reducing the load. Within the framework of this study, an RD algorithm has been developed – a protocol for transmitting and processing intermediate data. To carry out clustering of vehicles on a network segment, the DBSCAN unsupervised learning method was used. Preliminary analysis of abnormal traffic was carried out on the basis of RNN neural network models with short-term memory. Results. The developed hybrid model of efficient placement of initial and intermediate data makes it possible to react faster to unauthorized actions. Conclusion. The results obtained in the course of the study confirm the need to implement and scale a hybrid model with boundary calculations in practice.

Optical Tracking System for Multi-UAV Clustering

This paper proposes an optical tracking system to realize the clustering of multiple unmanned aerial vehicles (UAVs). The system uses a laser spot as a visual recognition target to realize cluster flight operations in a complex environment. To reduce the interference of sunlight in the optical visual navigation and obtain the optimal combination of laser receiving devices, we selected lasers in a variety of spectrum bands and filter films with different light transmittances to conduct a comprehensive test. By performing a fluid simulation of the UAVs with the related modules mounted in different positions, the optimal installation position was selected to enhance the effect of the UAV wind field. An online learning tracking framework based on the multi-feature extraction of convolutional networks was developed to realize robust long-term visual tracking. In the tracking process, the tracking algorithm could train the detector online according to the status of the target and adaptively initialize the target. Finally, to verify the practical application performance of the clustering system, a group of UAVs was tested in flight. We verified that the proposed method and device exhibit a high feasibility and reliability.

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

Cooperative monitoring and dissemination of urban events supported by dynamic clustering of vehicles

Critical urban events take places at a random way and they need to be dealt with by public authorities quickly to maintain the proper operation of cities. The main challenges for an efficient handling fall in the random nature of the event, and in the speed and accuracy of its notification to the authority. The pervasiveness of vehicles in the cities, and their communication and monitoring capabilities, allow VANETs to support the dissemination and handling of such events. Thus, this work proposes MINUET, a dynamic system to support the monitoring and dissemination of urban events, which operates in a VANET. It uses a cooperative-based strategy where vehicles dynamically coordinate the monitoring and dissemination of events in real time by the means of clusters. A performance comparative analysis in NS3 with a target tracking approach shows the efficiency of MINUET regarding the real-time monitoring, the dissemination of urban event data and data availability at the destination in dense and sparse networks.

Effect of security and trustworthiness for a fuzzy cluster management system in VANETs

Recently, smart cities and Internet of Things (IoT) applications, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. However, these kinds of wireless networks have security problems. Also, the vehicles can be not trustworthy, which brings different communication problems. In this work, we present a Fuzzy Cluster Management System (FCMS) for VANETs. We present and compare two fuzzy-based system models: FCMS1 and FCMS2 for clustering of vehicles in VANETs. For FCMS1, we use three input parameters: Vehicle Relative Speed with Vehicle Cluster (VRSVC), Vehicle Degree of Centrality (VDC) and Vehicle Security (VS). The output parameter is Vehicle Remain or Leave Cluster (VRLC). For FCMS2, we consider four input parameters by adding Vehicle Trustworthiness (VT) as a new parameter. We evaluate both systems by simulations. The simulation results show that vehicles with the same VRSVC and with high VDC, VS and VT values have higher possibility to remain in the cluster. By comparing FCMS1 and FCMS2, we found that the FCMS2 can manage better the vehicles in the cluster than FCMS1.

A fuzzy-based approach for cluster management in VANETs: Performance evaluation for two fuzzy-based systems

In recent years, due to the growing interest in smart cities and Internet of Things (IoT) applications, several innovation types of networks, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. Moreover, inter-vehicle communication has attracted attention because it can be applicable not only to alternative networks but also to various communication systems. In this paper, we present a Fuzzy-Based Cluster-Management System (FBCMS) for VANETs. We present and compare two fuzzy-based system models: FBCMS1 and FBCMS2 for clustering of vehicles in VANETs. We evaluate both systems by simulations. The simulation results show that by selecting vehicles with high GS, RA, SC and DC values, the vehicles have high connectivity with other vehicles and are more secure, so they will be selected as the cluster head of the cluster. By comparing FBCMS1 and FBCMS2, we found that the FBCMS2 can manage better the vehicles in the cluster than FBCMS1.

Clustering Algorithm for Urban Taxi Carpooling Vehicle Based on Data Field Energy

A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is proposed to solve the clustering problem of taxi carpooling on urban roads. The data field energy function is used to calculate the field energy of each data point in the passenger taxi offpoint dataset. To realize the clustering of taxis, the central point, outlier, and data points of each cluster subset are discriminated according to the threshold value determined by the product of each data point field values and point spacing. The classical algorithm and proposed algorithm are compared and analyzed by using the compactness, separation, and Dunn validity index. The clustering results of the proposed algorithm are better than those of the classical clustering algorithm. In the case of cluster numbers 25, 249, 409, and 599, the algorithm has good clustering results for the taxi trajectory dataset with certain regularity in space distribution and irregular distribution in time distribution. This algorithm is suitable for the clustering of vehicles in urban traffic roads, which can provide new ideas and methods for the cluster study of urban traffic vehicles.

Analytical model for clustered vehicular ad hoc network analysis

Abstract Clustering of vehicles is an important technique to reduce the high mobility effect of vehicles. This paper proposes an analytical model to evaluate the performance of a clustered vehicular ad hoc network (VANET). The analytical model is developed to evaluate three important parameters, namely packet delivery ratio, throughput, and delay. The results obtained from the analytical model are also accompanied by simulation results. This model can be further extended by researchers working on clustered VANET scenarios and will be helpful in modeling their protocols or algorithms. Furthermore, this model can verify the simulation results obtained from any network simulator.

A bandwidth-efficient and fair CSMA/TDMA based multichannel MAC scheme for V2V communications

Designing QoS-aware medium access control (MAC) scheme is a challenging issue in vehicular ad hoc networks. Proportional fairness and bandwidth utilization are among the significant requirements that should be taken into account by a MAC scheme. In this paper, a bandwidth-efficient and fair multichannel MAC protocol is proposed to address these two requirements, specifically in vehicle-to-vehicle communications. The proposed scheme is based on clustering of vehicles and exploits time division multiple access (TDMA) method alongside the carrier sense multiple access with collision avoidance mechanism to allocate DSRC-based resources in a different manner from IEEE 802.11p/IEEE 1609.4 protocols. It divides each channel into aligned dynamic-sized time frames. In each time frame, in a fully TDMA-based period, transmission opportunities are assigned to vehicles letting them have dedicated transmissions on the service and control channels. The maximum number of transmission opportunities per each frame is determined by the cluster head (CH) based on a defined optimization problem which aims at maximizing both proportional fairness and bandwidth utilization. Furthermore, the bandwidth utilization is assumed to be enhanced more through reallocation of unused transmission opportunities in each time frame, using a proposed reallocation algorithm. The proposed MAC protocol is treated as a lightweight scheme such that various types of unicast, multicast and broadcast communications are possible within the cluster without involving the CH. Evaluation results show that the proposed scheme has more than 90 % achievement in terms of proportional fairness and bandwidth utilization simultaneously, and in this case, has a considerable superiority over TC-MAC. In addition, using the proposed scheme, the satisfaction level of vehicles is preserved appropriately.

Social Clustering of Vehicles Based on Semi-Markov Processes

The full text version attached to this record is the authors final peer reviewed version. The publisher's final version of record can be found by following the DOI link.

A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance

This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway.

An AdaptiveTDMA Slot Assignment Strategy in Vehicular Ad Hoc Networks

Improving road safety is among the main objectives of Vehicular Ad-hoc NETworks (VANETs) design. This objective would be achieved essentially by the use of efficient safety applications which should be able to wirelessly broadcast warning messages between neighbouring vehicles in order to inform drivers about a dangerous situation in a timely manner. To insure their efficiency, safety applications require reliable periodic data dissemination with low latency. Medium Access Control (MAC) protocols play a primary role to provide efficient delivery and avoid as much as possible data packet loss. In fact, in distributed MAC approaches, packet loss is a consequence of collisions resulted from well known situations of the exposed and hidden node situations. This paper introduces an Adaptive TDMA Slot Assignment Strategy (ASAS) for VANET based on clustering of vehicles. The main aim of this work is to provide a MAC layer protocol that can reduce inter-cluster interference under different traffic loading conditions without having to use expensive spectrum and complex mechanisms such as CDMA or OFDMA. An analysis and simulation results are presented to evaluate the performance of ASAS. Moreover, we compare its performance with two TDMA MAC protocols DMMAC and VeMAC

Criterion for condensation in kinetically constrained one-dimensional transport models

We study condensation in one-dimensional transport models with a kinetic constraint. The kinetic constraint results in clustering of immobile vehicles; these clusters can grow to macroscopic condensates, indicating the onset of dynamic phase separation between free-flowing and arrested traffic. We investigate analytically the conditions under which this occurs and derive a necessary and sufficient criterion for phase separation. This criterion is applied to the well-known Nagel-Schreckenberg model of traffic flow to analytically investigate the existence of dynamic condensates. We find that true condensates occur only when acceleration out of jammed traffic happens in a single time step, in the limit of strong overbraking. Our predictions are further verified with simulation results on the growth of arrested clusters. These results provide analytic understanding of dynamic arrest and dynamic phase separation in one-dimensional traffic and transport models.

Improving Real-Time Route Suggestions in Automotive Navigation Systems using Vehicle Cluster Behavior

Automotive navigation systems are widely used by drivers to help them reach a particular destination. Mostly, such systems are designed to suggest routes which will help the driver to reach destination by covering less distance or consuming less time. This paper presents a technique that will select least congested path by using a vehicular traffic prediction technique that utilizes Global positioning data paired with Speed and Accelerometer Telemetry provided by a GPS unit, mounted on a vehicle, to improve the results of existing graph search methodologies currently being implemented to provide navigation data to users. Clustering of vehicles is done, based on similar positional and directional behavior. These clusters will have similar congestion levels. Traffic data identified through clustering is used to manipulate the path cost of the corresponding road, on an existing road network graph. It tries to improve real-time route suggestions by selecting less congested routes dynamically.