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COMMUNICATION AND COMPUTATION ARCHITECTURES FOR DISTRIBUTED WIRELESS SENSOR NETWORKS AND INTERNET OF THINGS
Title
COMMUNICATION AND COMPUTATION ARCHITECTURES FOR DISTRIBUTED WIRELESS SENSOR NETWORKS AND INTERNET OF THINGS
Creator
Yi, Won-jae
Date
2017, 2017-07
Description
Real-time data communication has been viral since the era of the smartphone rose to prominence in this decade. All communications from human...
Show moreReal-time data communication has been viral since the era of the smartphone rose to prominence in this decade. All communications from human to human, from device to human, and from device to device are handled over the Internet connection either through a mobile Internet service provider or Wi-Fi, which enables information exchange including weather service, road traffic conditions, news alerts, package tracking notifications. By looking at different perspectives of the role of a smartphone, it reveals itself as an ideal device to mobilize critical user data to construct a real-time monitoring application such as in remote healthcare and home automation systems. Not only can the smartphone handle real-time data transmissions, but it can also handle real-time computations on the device itself by utilizing its embedded CPU. This dissertation is a comprehensive study of the investigation, exploration and experimentation on a real-time health monitoring system where quality of life can be improved when the conventional system may affect and hamper regular daily activities. The design flow of this system is based on the Internet connection where any device that is communicatively associated with the smartphone can be connected to the Internet. By utilizing the Android smartphone, not only does the system gain real-time data transmission capability, but it also obtains flexibility to communicate with different types of sensors and platforms through multiple wireless protocols. This system is highly adaptable to the currently trending Internet of Things (IoT) standards, where significantly increasing anticipation over its social impact, where it can assist populations in rural and distant areas for healthcare, day-to-day activity monitoring, and prevention against hazardous conditions for workers. The system architecture introduced in this research is focused on reconfigurability and compatibility of wireless sensors where they are independent from a certain platform in which sensors are not limited to medical devices but also detect movement, location, climate condition and any other sensor for analyzing the environment. Four major components are introduced in this research including wireless sensor nodes, a central sensor data processing and communication node, an Android application, and a central database server. They are discussed and explored to seek for solutions to improve and enhance features in the fundamental system design. Communication and computation processing capabilities are evaluated for all major components for practical usage of the system for different case studies. Also as a quantitative case study, a posture and fall detection system is presented which determines the patient's activities, medical conditions and the cause of an emergency event through the integration of all system architecture components. Adapting the IoT system is also explored in this dissertation by introducing a protocol standard to improve data transmission efficiency and to enable cross-platform compatibility of wireless devices. In addition to improving system efficiency, a study on data security issues and assessment on sensor data has been explored by implementing a proposed security scheme to each major component within the real-time mobile monitoring system. Also, a concept of Quality-of-Service (QoS) for mobile monitoring system using a wireless sensor network has been investigated to provide a solution to prioritize sensor data transmissions based on the results obtained from the sensor data assessment application. The proposed solutions can be either implemented on or under the application layer.
Ph.D. in Computer Engineering, July 2017
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Wireless Body Sensor Network for Tracking Human Mobility using Long Short-Term Memory Neural Network for Classification
Title
Wireless Body Sensor Network for Tracking Human Mobility using Long Short-Term Memory Neural Network for Classification
Creator
Gupta, Saumya
Date
2019
Description
A large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks...
Show moreA large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks explore how to capture a type or types of motion, but all their sensors are placed in different locations; making their algorithms very specific to that movement. In this research, we explore the enhancement of human activity classification algorithm using long short-term memory (LSTM) neural network and wearable sensor network. There are five identical nodes used in the body sensor network to collect data. Each node incorporates an ESP8266 Microcontroller with Wi-Fi which is connected to an inertial measurement unit consisting of triple axis accelerometer and gyroscope sensor board. An analysis on the accuracy that each sensor node provides separately and in different combinations has been conducted to allow future research to focus their positioning in optimal positions. A Robot Operating System (ROS) central node is used to illustrate the in-built multi-threading capability. For demonstration, the positions chosen are waist, ankles and wrists. The raw sensor data can be observed on screen while it is being labelled live to create fitting dataset for developing an artificial neural network. Expectation is that increasing the number of sensors should raise the overall accuracy of the output but that isn’t the case observed, positioning of the sensor is pertinent to improvement. These platforms can be further extended to understand different motions and different sensor positions, also expanded to include other sensors.
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