An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Hadiwardoyo, Seilendria A.; Patra, Subhadeep; Calafate, Carlos T.; Cano, Juan-Carlos; Manzoni, Pietro

doi:10.1007/s11390-018-1817-4

Cited by 29 publications

(14 citation statements)

References 31 publications

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“…It also provide powerful privacy guarantee with high quality services. The four factors we have recognized for abstaining the shortcomings of the previous location Anonymizer are; quality, accuracy, flexibility and efficiency [25][26][27][28][29][30]. Minimum spatial area requirements and k anonymity has been supported by Casper [33].…”

Section: Third Trusted Party Architecture (Fog Anonymizer)mentioning

confidence: 99%

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Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing

Arif¹,

Wang²,

Balas³

2019

STUD INFORM CONTROL

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In the Vehicular Ad-hoc Networks (VANETs), a vehicle or the vehicle driver could be recognized and tracked by eavesdropping its queries (e.g., beacons) by an adversary as these contains personal information like location, speed, and communication of the vehicles. This attack leads to threats to the vehicle`s location and leakage of personal information. The current solution, is to use the anonymizer as a third trusted party between the vehicles and the LBS. In this paper we refer to the use of a Fog server with Fog anonymizer to secure the communication among the vehicles and LBS. Our scheme consists of four phases. In first phase, the vehicle driver initiates the communication process and generate the encrypted messages. These messages may contain the sub-messages. In phase 2, the Fog server received the messages via different roots. The Fs combined the messages and decrypt the messages based on the PK received by the vehicle. All the Fog server perform the same task for encryption and decryption. If any of the Fog server was compromised, we still had the link for communication. In Phase 3, the Fog anonymizer receive the messages from the Fog node, anonymize them based on the anonymization process. Thereafter, the Fog anonymizer send these messages to LBS to achieve desired goals. The Fog anonymizer perform the same job for anonymization and de-anonymization, while sending and receiving the messages from the LBS. In the last phase, the LBS received the messages from the Fog anonymizer, understand the communication messages, compile the desired results, and sent them back to the Fog anonymizer. Our analysis shows that the proposed scheme preserved the location privacy based on the queries at low communication and computational cost.

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Section: Third Trusted Party Architecture (Fog Anonymizer)mentioning

confidence: 99%

“…Security and privacy are one of the main challenges in VANETs. Security messages must be approved to avoid security attacks [6][7][8][9][10][29][30], such as the addition of false data, the modification of published messages and the reproduction of attacks [7]. These attacks can cause serious damage to the VANETs system [7].…”

Section: Introductionmentioning

confidence: 99%

Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing

Arif¹,

Wang²,

Balas³

2019

STUD INFORM CONTROL

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“…Intelligent Transportation Systems (ITS) are able to provide efficient solutions for traffic-related issues, such as safety and efficiency [1]. When attempting to make roads safer, ITS can provide systems that reduce the number of accidents taking place [2], along with safety-related applications [3].…”

Section: Introductionmentioning

confidence: 99%

Experimental characterization of UAV-to-car communications

et al. 2018

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Unmanned Aerial Vehicles (UAVs), popularly known as drones, can be deployed in conjunction with a network of ground vehicles. In situations where no infrastructure is available, drones can be deployed as mobile infrastructure elements to offer all types of services. Examples of such services include safety in rural areas where, upon an emergency event, drones can be quickly deployed as information relays for distributing critical warning to vehicles. In this work, we analyze the communications performance on the link between cars and drones taking into account the altitude, the antenna orientation, and the relative distance. The presented results show that the communication between a drone and a car can reach up to three kilometers in a rural area, and achieves at least a fifty percent success ratio for the delivery rate at a 2.7 kilometer range. Finally, to allow integrating the communications link behaviour in different network simulators, the experimental results were also modeled with a modified Gaussian function that offers a suitable representation for this kind of communication.

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“…Along with the development of technologies such as cloud computing, big data and artificial intelligence, automotive technologies is being rapidly developed towards the new direction of “electronic, networking, intelligence and sharing” [ 1 , 2 ]. Telematics boxes (T-Boxes) [ 3 ] including functions such as positioning, long-distance communication and acquisition of vehicle status are being more popular.…”

Section: Introductionmentioning

confidence: 99%

An Extended Kalman Filter and Back Propagation Neural Network Algorithm Positioning Method Based on Anti-lock Brake Sensor and Global Navigation Satellite System Information

Qin

et al. 2018

Sensors

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Telematics box (T-Box) chip-level Global Navigation Satellite System (GNSS) receiver modules usually suffer from GNSS information failure or noise in urban environments. In order to resolve this issue, this paper presents a real-time positioning method for Extended Kalman Filter (EKF) and Back Propagation Neural Network (BPNN) algorithms based on Antilock Brake System (ABS) sensor and GNSS information. Experiments were performed using an assembly in the vehicle with a T-Box. The T-Box firstly use automotive kinematical Pre-EKF to fuse the four wheel speed, yaw rate and steering wheel angle data from the ABS sensor to obtain a more accurate vehicle speed and heading angle velocity. In order to reduce the noise of the GNSS information, After-EKF fusion vehicle speed, heading angle velocity and GNSS data were used and low-noise positioning data were obtained. The heading angle speed error is extracted as target and part of low-noise positioning data were used as input for training a BPNN model. When the positioning is invalid, the well-trained BPNN corrected heading angle velocity output and vehicle speed add the synthesized relative displacement to the previous absolute position to realize a new position. With the data of high-precision real-time kinematic differential positioning equipment as the reference, the use of the dual EKF can reduce the noise range of GNSS information and concentrate good-positioning signals of the road within 5 m (i.e. the positioning status is valid). When the GNSS information was shielded (making the positioning status invalid), and the previous data was regarded as a training sample, it is found that the vehicle achieved 15 minutes position without GNSS information on the recycling line. The results indicated this new position method can reduce the vehicle positioning noise when GNSS information is valid and determine the position during long periods of invalid GNSS information.

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An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

Cited by 29 publications

References 31 publications

Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing

Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing

Experimental characterization of UAV-to-car communications

An Extended Kalman Filter and Back Propagation Neural Network Algorithm Positioning Method Based on Anti-lock Brake Sensor and Global Navigation Satellite System Information

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