Fuzzy Logic Decision Making by Localization and Recursive Algorithm in Vehicular AdHoc Network

315–317Vehicular AdHoc Networks are the most prominent and efficient technology nowadays. It achieves a high gain by using intelligent transportation systems due to their advanced version. Generally, all vehicle users expect low traffic jams and high degree of safety and security during their travel. It can be achieved by technique called Prediction Based Authentication. We present a fuzzy logic based decision making process by localization and recursion algorithm. It satisfies all the above user expectations like availability of alternative path when the travel route is busy and time minimization with low packet loss. In this algorithm we use two metrics namely position and distance metric for the purpose of easy data transmission. In this approach we do not generate any keys for transmitting the data packets. It will be done by using binary values. By creating adversary nodes the information can easily be transmitted to the neighbour nodes so that data loss can be minimized. Here, the system can operate in both online and off line modes

Mobility prediction method for vehicular network using Markov chain

This paper proposes mobility prediction technique via Markov Chains with an input of user’s mobile data traces to predict the user’s movement in wireless network. The main advantage of this method is prediction will give knowledge of user’s movement in advance even in fast moving vehicle. Furthermore, the information from prediction result will be use to assist handover procedure by reserve resource allocation in advance in vehicular network. This algorithm is simple and can be computed within short time, thus the implementation of this technique will give the significant impact especially on higher speed vehicle. Finally, an experiment is performed using real mobile user data traces as input for Markov chain to predict next user movement. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out with several users under same location zone. The results show that the proposed method predicts have good performance which is 30 of mobile users achieved 100 of prediction accuracy

Fuzzy Logic Decision Making by Localization and Recursive Algorithm in Vehicular AdHoc Network

Vehicular AdHoc Networks are the most prominent and efficient technology nowadays. It achieves a high gain by using intelligent transportation systems due to their advanced version. Generally, all vehicle users expect low traffic jams and high degree of safety and security during their travel. It can be achieved by technique called Prediction Based Authentication. We present a fuzzy logic based decision making process by localization and recursion algorithm. It satisfies all the above user expectations like availability of alternative path when the travel route is busy and time minimization with low packet loss. In this algorithm we use two metrics namely position and distance metric for the purpose of easy data transmission. In this approach we do not generate any keys for transmitting the data packets. It will be done by using binary values. By creating adversary nodes the information can easily be transmitted to the neighbour nodes so that data loss can be minimized. Here, the system can operate in both online and off line modes

VANET-Based Traffic Monitoring and Incident Detection System: A Review

As a component of intelligent transport systems (ITS), vehicular ad hoc network (VANET), which is a subform of manet, has been identified. It is established on the roads based on available vehicles and supporting road infrastructure, such as base stations. An accident can be defined as any activity in the environment that may be harmful to human life or dangerous to human life. In terms of early detection, and broadcast delay. VANET has shown various problems. The available technologies for incident detection and the corresponding algorithms for processing. The present problem and challenges of incident detection in VANET technology are discussed in this paper. The paper also reviews the recently proposed methods for early incident techniques and studies them

Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.</p

Smart vehicle navigation system using hidden Markov model and RFID technology

The road transport of dangerous goods has been the subject of research with increasing frequency in recent years. Global positioning system (GPS) based vehicle location devices are used to track vehicles in transit. However, this tracking technology suffers from inaccuracy and other limitations. In addition, real-time tracking of vehicles through areas shielded from GPS satellites is difficult. In this paper, the authors have addressed the implementation of a smart vehicle navigation system capable of using radio frequency identification based on information about navigation paths. For prediction of paths and accurate determination of navigation paths in advance, predictive algorithms have been used based on the hidden Markov model. At the core of the system there is an existing field programmable gate array board and hardware for collection of navigation data. A communication protocol and a database to store the driver’s habit data have been designed. From the experimental results obtained, an accurate navigation path prediction is consistently achieved by the system. In addition, once-off disturbances to the driver habits have been filtered out successfully.http://link.springer.com/journal/112772017-10-31hb2017Electrical, Electronic and Computer Engineerin

From movement purpose to perceptive spatial mobility prediction

International audienceA major limiting factor for prediction algorithms is the forecast of new or never before-visited locations. Conventional personal models utterly relying on personal location data perform poorly when it comes to discoveries of new regions. The reason is explained by the prediction relying only on previously visited/seen (or known) locations. As a side effect, locations that were never visited before (or explorations) by a user cause disturbance to known location's prediction. Besides, such explorations cannot be accurately predicted. We claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. As recently demonstrated [1, 2], there exist regularities in returning and exploring visits. Moreover, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we infer an individual's next type of transition: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. We conduct extensive experiments on three real-world GPS mobility traces. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities. To the best of our knowledge, we are the first to propose a framework solely based on personal location data to tackle the prediction of visits to new places

From movement purpose to perceptive spatial mobility prediction

A major limiting factor for prediction algorithms is the forecast of new or never before-visited locations. Conventional personal models utterly relying on personal location data perform poorly when it comes to discoveries of new regions. The reason is explained by the prediction relying only on previously visited/seen (or known) locations. As a side effect, locations that were never visited before (or explorations) by a user cause disturbance to known location's prediction. Besides, such explorations cannot be accurately predicted. We claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. As recently demonstrated [3, 15], there exist regularities in returning and exploring visits. Moreover, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we infer an individual's next type of transition: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. We conduct extensive experiments on three real-world GPS mobility traces. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities. To the best of our knowledge, we are the first to propose a framework solely based on personal location data to tackle the prediction of visits to new places.Accepted manuscrip

Proposta de mecanismo para prevenção da congestão causada por envio excessivo de mensagens em redes veiculares

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.A prevenção de acidentes em vias e rodovias é um dos aspectos mais importantes das redes veiculares. Em uma Rede Ad Hoc Veicular (do Inglês, Vehicular Ad-Hoc Network) (VANET), veículos são equipados com um dispositivo que permite a comunicação sem fio entre eles. Neste tipo de rede, veículos geralmente constroem a topologia da rede através da troca periódica de mensagens conhecidas como beacons. Estas mensagens devem conter a posição do veículo, sua velocidade e direção. Algumas aplicações para prevenção de acidentes em VANETs definem requisitos rígidos para garantir sua operação correta, dentre eles, frequências de transmissão de beacons que podem variar de 10 a 50 Hz. Entretanto, altas taxas de transmissão de beacons podem causar um problema conhecido como broadcast storm, que prejudica a operação destas aplicações em cenários com alta densidade de veículos. Durante um broadcast storm, o número de mensagens sendo transmitidas simultaneamente aumenta a quantidade de pacotes perdidos por colisões e diminui a cobertura. Neste trabalho, propõe-se um novo protocolo para transmissão de beacons em VANETs chamado Density-based Congestion Avoidance Protocol (DCAP), com objetivo de minimizar o impacto do broadcast storm. Resultados experimentais demonstram que o protocolo DCAP consegue reduzir a taxa de pacotes perdidos por colisões para aproximadamente 5%, garantindo uma taxa de recepção de pacotes superior a 85% até em cenários propensos ao broadcast storm.Accident prevention in roads and highways is one of the most important aspects of vehicular networks. In a Vehicular Ad Hoc Network (VANET), vehicles are equipped with a device that enables wireless communication between them. In this type of network, vehicles can build the network topology by periodic exchange of messages known as beacons. These messages should include the vehicle’s position, speed and heading. Some accident prevention applications in VANETs define strict requirements to guarantee their operation, among them, beaconing rates that can vary between 10 to 50 Hz. However, high beacon transmission rates can lead to a problem known as broadcast storm, which impair the operation of these applications in high vehicle density scenarios. During a broadcast storm, the number of messages being transmitted simultaneously increases packet loss rate due to collisions and decreases coverage. In this work, a new protocol for beaconing in VANETs called Density-based Congestion Avoidance Protocol (DCAP) is proposed to minimize the impact caused by broadcast storm. Experimental results demonstrate that the DCAP protocol can reduce the packet loss rate caused by collisions to approximately 5%, guaranteeing a packet delivery ratio of up to 85% in scenarios prone to broadcast storm