Computer communications

The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road.

Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones

The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classification of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months.

/pdf/drivingstyles-a-smartphone-application-to-assess-driver-2ens5tjr32.pdf

DrivingStyles: A smartphone application to assess driver behavior

Constructing an intelligent traffic monitoring system firstly depends on automatic identification for vehicles. At present, automatic identification technology based on image and vehicle license plate is going to fall in the trap due to its low recognition rate and affection by adverse weather. Thus it is necessary to apply new technologies to solve this problem, and technologies based on Internet of Things provide a new approach for it. In this paper, we explored this issue and proposed a feasible scheme. At first, we took global unique EPC code as identity identification of vehicles in stead of vehicle license plate and utilized RFID reader to read EPC code by RF electromagnetic wave, which completely solve d the problem of no all-weather operations. Secondly, we obtained positioning information of vehicles by using GPS technology. Thirdly, because GPRS provides high-speed wireless IP services for mobile users, fully supports the TCP/IP, we t ook wireless GPRS scheme to transmit data of mobile objects. The realization of automatic detection and transmission of data provided a fundamental guarantee for cons tructing an intelligent traffic monitoring system. And then, we designed and discussed in turn its network architecture, data flow analysis, hardware logic structure , software flow, as well as its intelligent decision-making module. Research and design show that it is feasible and inexpensive to construct an intelligent traffic monitoring system based on Internet of Things, and the intelligent traffic monitoring system based on Internet of Things has a number of advantages such low cost, high reliability, never affected by adverse weather, all weather operations etc. Therefore, it will have a broad applying perspective.

Internet of Things: a New Application for Intelligent Traffic Monitoring System

https://ir.nctu.edu.tw:443/bitstream/11536/14903/1/000298027700001.pdf

Research challenges towards the Future Internet

Android/OSGi-based vehicular network management system

Workshops are a very common mode of interaction for team learning, and depending on the type of workshop, there are different ways of interaction and questions. In digital media workshops, design and computation coexist, but the logical conflict between them is always difficult for designers to understand and master. In the research team's previous study, we found that designers often had difficulty combining their imagination with the computational tools we were given at the time of the workshop, or not sure what tools could help them. In a multi-person workshop, it is difficult for the facilitator to take care of both the activity and the participants, resulting in an increased burden on the facilitator and a decrease in the participants’ willingness and self-confidence to engage in computation. In the previous study, we tested and adjusted the interaction process of the workshop and developed a new workshop process. In this study, we took the role of the facilitator as the main part, the scenario as the computing workshop, and the user as the designer. The process of the previous study was used as the base concept for the next iteration of the system, for which we designed a model of the workshop support system. After the participants have gone through the design thinking process, they put their design ideas into the system, and after a set of natural language processing, the system can suggest the participants’ computational tools based on the data, and provide them with the direction to choose the right tools for their ideas. The system should also be able to make iterative corrections and learn from the iterative corrections in the original prescribed route to generate new answers. Through machine learning, the system can automatically learn and update, and can switch between different computational workshops more freely, so that the results can be more flexible. 

Automated Tools Recommendation System for Computing Workshops

In factories, energy conservation is a crucial issue. The co-fabrication space is a modern-day equivalent of a new factory type, and it makes use of Internet of Things (IoT) devices, such as sensors, software, and online connectivity, to keep track of various building features, analyze data, and produce reports on usage patterns and trends that can be used to improve building operations and the environment. The co-fabrication user requires dynamic and flexible space, which is different from the conventional user’s usage. Because the user composition in a co-fabrication space is dynamic and unstable, we cannot use the conventional approach to assess their usage and rentals. Prototyping necessitates a specifically designed energy-saving strategy. The research uses a “seeing–moving–seeing” design thinking framework, which enables designers to more easily convey their ideas to others through direct observation of the outcomes of their intuitive designs and the representation of their works through design media. The three components of human behavior, physical manufacture, and digital interaction are primarily the focus of this work. The computing system that connects the physical machine is created through communication between the designer and the digital interface, giving the designer control over the physical machine. It is an interactive fabrication process formed by behavior. The Sensible Energy System+ is an interactive fabrication process of virtual and real coexistence created by combining the already-existing technology, the prototype fabrication machine, and SENS. This process analyzes each step of the fabrication process and energy, fits it into the computing system mode to control the prototype fabrication machine, and reduces the problem between virtual and physical fabrication and energy consumption.

/pdf/sens-a-co-existing-fabrication-system-for-a-smart-dfa-3bhcpmf6.pdf

Teng-Wen Chang

Papers

Android/OSGi-based vehicular network management system

Automated Tools Recommendation System for Computing Workshops

SENS+: A Co-Existing Fabrication System for a Smart DFA Environment Based on Energy Fusion Information