Driver's Smartphone Research Articles

Car e-Talk: An IoT-Enabled Cloud-Assisted Smart Fleet Maintenance System

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affect fleet productivity and performance. In this article, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle's health. Data from sensors are received on the driver's smartphone through a microcontroller and, after processing, useful information is displayed on the driver's mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.

Location Privacy Violation via GPS-agnostic Smart Phone Car Tracking

Smart phones nowadays are equipped with global positioning systems (GPS) chips to enable navigation and location-based services. A malicious app with the access to GPS data can easily track the person who carries the smart phone. People may disable the GPS module and turn it on only when necessary to protect their location privacy. However, in this paper, we demonstrate that an attacker is still able to track a person by using the embedded magnetometer sensor in victim's smart phone, even when the GPS module is disabled all the time. Moreover, this attack neither requests user permissions related to locations for installation, nor does its operation rely on wireless signals like WiFi positioning or suffer from signal propagation loss. Only the angles of a car's turning measured by the magnetometer sensor of a driver's smart phone are utilized. Without loss of generality, we focus on car tracking, since cars are popular transportation tools in developed countries, where smart phones are commonly used. Inspired by the intuition that a car may exhibit different turning angles at different road intersections, we find that an attacker can match car turning angles to a map to infer the actual path that the driver takes. We address technical challenges about car turn angle extraction, map database construction, and path matching algorithm design to make this attack practical and efficient. We also perform an evaluation using real-world driving paths to verify the relationship between the numbers of turns and the time cost of the matching algorithm. The results show that it is possible for attacker to precisely pinpoint the actual path when the driving path includes 11 turns or more. More simulations are performed to demonstrate the attack with lager selected local areas.

Analysis of Accident Risks from Driving Behaviors

Detecting aggressive driving behavior is essential for safe transport systems as it leads to the awareness of risks of accidents. Using smartphone-equipped sensors would be promising approach considering the penetration ratio to the consumers. In this paper, we have used a large dataset of accelerometer readings collected by smartphones of drivers. The experiment was performed to explore the accident risk indexes which statistically separate the safe drivers and risky drivers. By the statistical analysis, it is found that the frequency of acceleration exceeding 2.4 m/s2, that of deceleration exceeding 1.4 m/s2, and that of left acceleration exceeding 1.1 m/s2 separate the safe drivers and risky drivers. The classifier using these three criteria achieves 70 % classification accuracy and 83 % detection accuracy of risky drivers.

The Development of a Multi-port Slow Charger for Electric Vehicles

Background/Objectives: The EV charging infrastructure consists largely of the power supply facilities, charger, interface, and information system. Charger that supply electricity from an electricity provider to EV batteries can be classified as direct charging, noncontact charging, and battery exchange. Methods/Statistical Analysis: Direct charging is divided into slow charging and fast charging based on the charging time. At present, 90% of the charger market is slow chargers. The points to be solved of slow charger is the user discomfort and installation cost due to limitation of space and time. Findings: To solve this point, proposed the multi-port slow-charging system in this study. It reduced the installation and maintenance costs in comparison to existing chargers and improved user convenience by providing a multi-port charging feature, which enables multiple EVs to be charged simultaneously with one charger, and by providing a user interface through the driver's smart phone to implement the V2G standard. A prototype of the proposed multi-port slow-charging system was implemented, and performance tests were conducted in situations similar to the actual environment to verify the performance of the proposed system. An analysis of the performance test results reveals that the proposed system was above the KC(Korea Certification) standard values. Furthermore, the proposed multi-port slow-charging system can reduce costs by about 50%, compared to the existing charging system, based on the manufacturing and maintenance costs of the charger. Application/Improvements: The user interface of the charger was interfaced with the driver's smart phone, implementing a communication system that is similar to the V2G protocol in order to construct an ICT charging system that can be monitored remotely in real time. It is expected to contribute to the expansion of the EV charging infrastructure.

Sensor Integrated Antenna Design for Applications in Cold Chain Logistic Services

A planar antenna design is developed for integration with a temperature sensor to monitor frozen goods condition during transportation. Antenna geometries as well as radiation patterns are configured to address the distinctive propagation environment, which is the cargo space filled with stacks of thermal insulated boxes. The sensor, a Bluetooth module, periodically relays temperature data to a dedicated reader or the driver's smart phone. With these data, quality of service can be ensured for cold chain logistics. The sensor antenna, which is mounted on the surface of the insulated box, has a sideways pattern that is aimed to propagate along the gap between boxes and avoid attenuation from perishable goods within. Numerical simulations of this distinctive propagation environment are conducted to determine the required antenna polarization. To demonstrate advantages of the proposed temperature monitoring system, several field tests have been devised. Received signal strength indicators are measured for stacked containers in both closed and open truck environments to validate the superior performance of the developed sensor. Stable connections to sensors on boxes filled with frozen goods can be made successfully in delivery trucks.

Bus Information System based on Smart-Phone Apps using GPS Information

This paper provides Bus Information System(BIS) implementation based on the Smart-phone APPs using GPS information. Nearly all of recent smart-phones are equipped with the GPS feature. Bus driver's smart-phone can send the position information using its GPS information periodically and on events. On receiving those information, servers can process those into the information that can be used by individual users and bus stop information panel. The proposed BIS based on smart-phone APPs is expected to be used publicly with the special process that enables the smart-phone APPs only in the specific bus.