Experimental evaluation of mobile phone sensors

Smart phone has become an important part of people's daily life. Most of current smart phone are equipped with a rich set of built-in sensors. The mobile applications such as geo-location based video annotation and indoor positioning require precise measurements from sensors. In addition, understanding the sensing performance of a smart phone device is helpful for implementing a mobile application that needs sensor data. This paper presents an experimental evaluation of key sensors in a state of the art smart phone - Google Nexus 4. The sensors chosen in the paper are accelerometer, gyroscope, magnetometer and GPS. Substantial tests have been executed to evaluate the sensors' accuracy, precision, maximum sampling frequency, sampling period jitter, energy consumption

It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications

Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smart-phones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications. In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no prior-configuration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of $15^\circ$ averaged over a wide variety of phone positions. This is $558\%$ better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.Comment: Accepted for publication in the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2014

Ambient Home Server: Location Aware Home Automation

Currently in the realm of home automation, there are two primary approaches. One approach creates independent devices which can be controlled by a smart phone or send notifications when its task is complete, such as door locks and washing machines. The other approach has devices with sensors on them which attempts to determine if people are around before performing an action, such as the Nest thermostat and the WeMo motion sensors. The goal of this project is to merge both of these approaches together by using a smart phone to create a location aware device which can inform the home of where people are located. This project requires a Java server and MariaDB to handle interactions between various sensors and controllers, sensors built with off the shelf parts to monitor the home and an Android application to inform the server of its location, as well as view and control sensors inside of the home

Toward Introduction of Immunity-based Model to Continuous Behavior-based User Authentication on Smart Phone

AbstractSmart phone stores a lot of important private information, so that user authentication is increasingly necessary to prevent attacks by illegal users who are not the owner of the smart phone. Password authentication or biometrics can be generally applied only on login. After the authentication is passed, not only the legal owner but also illegal users freely use the smart phone. Therefore we are trying to develop a behavior-based user authentication system to continuously check the user activities after login. The developing system can extract many operational and behavioral features characteristic of user by multiple sensors; for example, touch screen, accelerometer, microphone, and GPS sensor. And then it can combine the authentication results from the multiple sensors because a single sensor may produce poor authentication accuracy. In this paper, we report the ongoing results of our system, that is, the experimental results from user authentication using touch operational features, and some features extracted from accelerometer. We also discuss the introduction of immunity-based model to our system to integrate the authentication results from the multiple sensors

Improved battery life for context awareness application in smart-phones

The new smart-phones with new operating system and portable sensors support the basis for context awareness systems and applications for handling user activity and user privacy. Nowadays, individuals need new services and real time information anywhere and anytime. Context awareness is an emerging service, which could be able to improve the user experiences in current situation. Context awareness can be considered as location, calendar, user activity and etc. The review of the literature proves that context awareness in mobile phone can be useful and studied as unavoidable service in next generation of smart-phone applications. In this paper, a short review about context awareness in mobile phone is studied, furthermore, we critically analyzed related works of context awareness in smart-phones. The review shows that the most important context in mobile phone is location, which is mostly obtained by using Global Positioning System (GPS) sensor in mobile phones but GPS can significantly increases battery consumption in mobile phones. In this regard, a framework as Improved Battery life in Context Awareness System (IBCS) is proposed to improve battery life and reduce cost of using GPS in context awareness applications based on smart-phones. The review argues the weakness and strength of these studies, and aims to (a) indicate the most important context in mobile phone, (b) reduce the battery consumption of GPS sensor in mobile phone

Smart Cities for Real People

Accelerating urbanization of the population and the emergence of new smart sensors (the Internet of Things) are combining in the phenomenon of the smart city. This movement is leading to improved quality of life and public safety, helping cities to enjoy economies that help remedy some budget overruns, better health care, and is resulting in increased productivity. The following report summarizes evolving digital technology trends, including smart phone applications, mapping software, big data and sensor miniaturization and broadband networking, that combine to create a technology toolkit available to smart city developers, managers and citizens. As noted above, the benefits of the smart city are already evident in some key areas as the technology sees actual implementation, 30 years after the creation of the broadband cable modem. The challenges of urbanization require urgent action and intelligent strategies. The applications and tools that truly benefit the people who live in cities will depend not on just the tools, but their intelligent application given current systemic obstacles, some of which are highlighted in the article. Of course, all the emerging technologies mentioned are dependent on ubiquitous, economical, reliable, safe and secure networks (wired and wireless) and network service providers

Posture Recognition with G-Sensors on Smart Phones

[[abstract]]With the popularity of smart phones in recent years, various sensors on smart phones can be utilized to detect the movement or intention of the smart phone users. In this research, we aim at using the signals collected from the G-sensor in the smart phone to recognize the posture of the user. Signals for sit, stand, walk and run are collected to train an offline neural network as the classifier. After the neural network learns the four postures, we then implement a neural network with the learned connection weights in a smart phone app. The app can record the postures of the user for the whole day and estimate the burned calories accordingly. This app can replace the pedometer to have a more accurate estimate of calorie consumption. Details of the app are presented in this paper. The accuracy of neural networks on posture recognition with G-sensor signals is also verified by five-fold cross-validation.[[conferencetype]]國際[[conferencedate]]20120926~20120928[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Melbourne, Australi