Non-GPS Data Dissemination for VANET

Fast, reliable, and efficient data dissemination in VANET is a key of success for intelligent transportation system. This requires a broadcasting protocol which has efficient forwarder nodes and an efficient broadcasting mechanism. In this paper, we propose a self-decision algorithm that allows a node to know that it belongs to a member of connected dominating set or not. The algorithm is a combination of density based algorithm and topology based algorithm, called “DTA.” The algorithm does not require any geographical knowledge. Therefore, it can avoid violating a privacy issue. Moreover, the algorithm can resist inaccurate data than position-base algorithms that need high frequent beaconing for accurate data. The simulation results show that our algorithm provides the highest coverage results compared to existing solutions. We also propose a new broadcasting protocol, called “NoG.” NoG consists of a broadcasting mechanism, a waiting timeout mechanism, and a beaconing mechanism. The proposed protocol operates without any geographical knowledge and provides reliable and efficient data dissemination. The performance is evaluated with a realistic network simulator (NS-3). Simulation results show that NoG with DTA outperforms other existing protocols in terms of reliability and data dissemination speed

Blind Corner Propagation Model for IEEE 802.11p Communication in Network Simulators

Vehicular Ad Hoc Network (VANET) has been developed to enhance quality of road transportation. The development of safety applications could reduce number of road accidents. IEEE 802.11p is a promising standard for intervehicular communication, which would enable the connected-vehicle applications. However, in the well-known network simulators such as NS3 and Omnet, there is no propagation model that can simulate the IEEE 802.11p communication at blind corner realistically. Thus, in this paper, we conducted the real-world experiments of IEEE 802.11p in order to construct the model to describe the characteristics of the IEEE 802.11p communication at the blind corners. According to the experimental results, we observe that the minimum distance between the vehicle and the corner can effectively be represented as the key parameter in the model. Moreover, we have a variable parameter for adjusting the impact of the obstruction which could be different at each type of blind corners. The simulation results using our proposed model are compared with those using the existing obstacle model. The results showed that our proposed model is much more closely aligned with the real experimental results

Public Transport Driver Identification System Using Histogram of Acceleration Data

This paper introduces a driver identification system architecture for public transport which utilizes only acceleration sensor data. The system architecture consists of three main modules which are the data collection, data preprocessing, and driver identification module. Data were collected from real operation of campus shuttle buses. In the data preprocessing module, a filtering module is proposed to remove the inactive period of the public transport data. To extract the unique behavior of the driver, a histogram of acceleration sensor data is proposed as a main feature of driver identification. The performance of our system is evaluated in many important aspects, considering axis of acceleration, sliding window size, number of drivers, classifier algorithms, and driving period. Additionally, the case study of impostor detection is implemented by modifying the driver identification module to identify a car thief or carjacking. Our driver identification system can achieve up to 99% accuracy and the impostor detection system can achieve the F1 score of 0.87. As a result, our system architecture can be used as a guideline for implementing the real driver identification system and further driver identification researches

FloorLoc-SL: Floor Localization System with Fingerprint Self-Learning Mechanism

Nowadays, a mobile phone plays an important role in daily life. There are many applications developed for mobile phones. Location service application is one kind of mobile application that serves location information. GPS receiver is embedded on a mobile phone for localization. However, GPS cannot provide localization service over indoor scenario efficiently. This is because obstacles and structures of building block GPS signal from the satellites. Many indoor localization systems have been proposed but most of them are developed over single-floor scenario only. The dimension of altitudes in localization results will be missed. In this paper, we propose floor localization system. The proposed system does not need any site survey and any support from back-end server. It has a self-learning algorithm for creating fingerprint in each floor. The self-learning algorithm utilizes sensors on the mobile phone for detecting trace of mobile phone user. This algorithm is low computation complexity, which can be operated on any mobile phones. Moreover, the mobile phone can exchange fingerprints with others via virtual ad hoc network instead of learning all floor fingerprints by themselves only. Our proposed floor localization system achieves 87% of accuracy

Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand

A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week