Wireless fault tolerances decision using artificial intelligence technique

Wireless techniques utilized in industrial applications face significant challenges in preventing noise, collision, and data fusion, particularly when wireless sensors are used to identify and classify fault in real time for protection. This study will focus on the design of integrated wireless fault diagnosis system, which is protecting the induction motor (IM) from the vibration via decrease the speed. The filtering, signal processing, and Artificial Intelligent (AI) techniques are applied to improve the reliability and flexibility to prevent vibration increases on the IM. Wireless sensors of speed and vibration and card decision are designed based on the wireless application via the C++ related to the microcontroller, also, MATLAB coding was utilized to design the signal processing and the AI steps. The system was successful to identify the misalignment fault and dropping the speed when vibrations rising for preventing the damage may be happen on the IM. The vibration value reduced via the system producing response signal proportional with fault values based on modify the main speed signal to dropping the speed of IM

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

A mobile sensing solution for indoor and outdoor state detection

Abstract. One important research challenge in ubiquitous computing is determining a device’s indoor/outdoor environmental state. Particularly with modern smartphones, environmental information is important for enabling of new types of services and optimizing already existing functionalities. This thesis presents a tool for Android-powered smartphones called ContextIO for detecting the device’s indoor/outdoor state by combining different onboard sensors of the device itself. To develop ContextIO, we developed a plugin to AWARE mobile sensing framework. Together the plugin and its separate controller component collect rich environmental sensor data. The data analysis and ContextIO’s design considers collected data particularly about magnetometer, ambient light and GSM cellular signal strength. We manually derive thresholds in the data that can be used in combination to infer whether a device is indoor or outdoor. ContextIO uses the same thresholds to infer the state in real time. This thesis contributes an Android tool for inferring the device’s indoor/outdoor status, an open dataset that other researchers can use in their work and an analysis of the collected sensor data for environmental indoor/outdoor state detection.Tiivistelmä. Yksi jokapaikan tietotekniikan tutkimuskysymyksistä keskittyy selvittämään onko laitteen sijainti sisä- vai ulkotilassa. Etenkin uudet älypuhelimet pystyvät hyödyntämään tätä tietoa uudenlaisten palveluiden ja sovellusten kehittämisessä sekä vanhojen toiminnallisuuksien optimoinnissa. Tämä diplomityö esittelee Android-käyttöjärjestelmällä toimiville puhelimille suunnatun työkalun nimeltään ContextIO. Työkalu yhdistelee älypuhelimen sensorien tuottamaa tietoa ja havaitsee laitteen siirtymisen eri sijaintiin sisä- ja ulkotilojen suhteen. ContextIO:n suunnittelu ja kehitystyö perustuvat data-analyysiin, jonka data kerättiin AWARE-sensorialustan liitännäisellä sekä erillisellä nimeämistyökalulla. Data-analyysi keskittyy magnetometrin, valosensorin sekä GSM-kentän voimakkuuden hyödyntämiseen paikantamisessa. Kerätystä datasta määriteltiin raja-arvot, joita yhdistelemällä voidaan varsin luotettavasti todeta laitteen sijainti sisä- ja ulkotilojen suhteen. Nämä raja-arvot luovat perustan ContextIO:n reaaliaikaiselle laitteen sijainnin määrittämiselle. Tämän diplomityön pääasialliset tulokset ovat työkalu Android-pohjaisten älypuhelinten sijainnin määrittämiseen sisä- ja ulkotilojen suhteen, avoin datasetti, jota muut tutkijat voivat käyttää sekä sijainnin määrittämiseen keskittyvä data-analyysi

Master of Science

thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Internet of things for disaster management: state-of-the-art and prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE