IU Indianapolis ScholarWorks :: Browsing by Subject "IoT"

Browsing by Subject "IoT"

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Deep Reinforcement Learning of IoT System Dynamics for Optimal Orchestration and Boosted Efficiency
(2023-08) Shi, Haowei; Zhang, Qingxue; King, Brian; Fang, Shiaofen
This thesis targets the orchestration challenge of the Wearable Internet of Things (IoT) systems, for optimal configurations of the system in terms of energy efficiency, computing, and data transmission activities. We have firstly investigated the reinforcement learning on the simulated IoT environments to demonstrate its effectiveness, and afterwards studied the algorithm on the real-world wearable motion data to show the practical promise. More specifically, firstly, challenge arises in the complex massive-device orchestration, meaning that it is essential to configure and manage the massive devices and the gateway/server. The complexity on the massive wearable IoT devices, lies in the diverse energy budget, computing efficiency, etc. On the phone or server side, it lies in how global diversity can be analyzed and how the system configuration can be optimized. We therefore propose a new reinforcement learning architecture, called boosted deep deterministic policy gradient, with enhanced actor-critic co-learning and multi-view state transformation. The proposed actor-critic co-learning allows for enhanced dynamics abstraction through the shared neural network component. Evaluated on a simulated massive-device task, the proposed deep reinforcement learning framework has achieved much more efficient system configurations with enhanced computing capabilities and improved energy efficiency. Secondly, we have leveraged the real-world motion data to demonstrate the potential of leveraging reinforcement learning to optimally configure the motion sensors. We used paradigms in sequential data estimation to obtain estimated data for some sensors, allowing energy savings since these sensors no longer need to be activated to collect data for estimation intervals. We then introduced the Deep Deterministic Policy Gradient algorithm to learn to control the estimation timing. This study will provide a real-world demonstration of maximizing energy efficiency wearable IoT applications while maintaining data accuracy. Overall, this thesis will greatly advance the wearable IoT system orchestration for optimal system configurations.
Enhanced Data Transportation in Remote Locations Using UAV Aided Edge Computing
(ASTES, 2021) Ravi, Niranjan; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
In recent years, the applications in the field of Unmanned Aerial Vehicle (UAV) systems has procured research interests among various communities. One of the primary factors being, thinking beyond the box of what could UAV system bring to the table other than military applications? Evidence to any answer for this question is the current day scenarios. We could see numerous applications of UAV starting from commercial applications of delivering consumer goods to life saving medical applications such as delievery of medical products. Using UAVs in for data transportation in remote locations or locations with no internet is a trivial challenge. In-order to perform the tasks and satisfy the requirement, the UAVs should be equipped with sensors and transmitters. Addition of hardware devices increases the number of connections in hardware design, leading to exposure during flight operation. This research proposes an advanced UAV system enabling wireless data transfer ability and secure data transmission with reduced wiring in comparison to a traditional design of UAV. The applications of this research idea targets using edge computing devices to acquire data in areas where internet connectivity is poor and regions where secured data transmission can be used along with UAV system for secure data transport.
High sensitivity nanotechnology gas sensing device
(2016-12) Tanu, Tanu; Rizkalla, Maher. E.
The nanotechnology materials have been used for high sensitivity sensing devices due to their ability to alter their properties in response to the environmental parameters such as temperature, pressure, gas, electromagnetic, and chemicals. The features of employing nanoparticles on top of graphene thin film have driven the hypothesis of achieving high sensing nanotechnology devices. This study demonstrates a novel approach for designing a low noise nanoparticle based gas sensing device with internet of things (IoT) capability. The system is capable of minimizing cross-talk between multiple channels of amplifiers arranged on one chip using guard rings. Graphene mono-layer is utilized as sensing material with the sensitivity catalyzed by addition of gold nano-particles on its surface. The signal from the sensing unit is received by an offset cancellation amplifying system using a system on chip (SoC) approach. IoT capability of the sensing device is developed using FRDM K64f micro-controller board which sends messages on IoT platform when a gas is sensed. The message is received by an application created and sent as an email or message to the user. This study details the mathematical models of the graphene based gas sensing devices, and the interface circuitry that drives the differential potentials, resulting from the sensing unit. The study presents the simulation and practical model of the device, detailing the design approach of the processing unit within the SoC system and wireless implementation of it. The sensing device was capable of sensing gas concentration from 5% to 100% using both the resistive and capacitive based models. The I-V characteristics of the FET sensing device was in agreeable with the other models. The SoC processing unit was designed using cadence tools, and simulation results showed very high CMRR that enable the amplifier to sense a very low signal received from the gas sensors. The cross talk noise was reduced by surrounding guard rings around the amplifier circuits. The layout was accomplished with 45nm technology and simulation showed an offset voltage of 17μV.
Instrumentation and Data Collection for Sheet Glass Production
(2019) Shaw, David; Cooney, Elaine
Kokomo Opalescent Glass (KOG) is a manufacturer of art glass located in Kokomo Indiana. KOG has high defect rates in their sheet glass production process that can vary greatly depending on operator experience and environmental factors. This project aimed to improve the repeatability of KOG’s sheet glass production process by enabling them to monitor the temperature at which glass sheets enter their annealing oven and to decrease their defect rate, which has historically been around 25%. Through integrating instrumentation and data collection into KOG’s production process, defects in sheet glass production were successfully decreased by approximately 10% in the weeks following the installation of the device created in this project.
Internet of Things Proof of Concept
(2018-12-04) Cucore, Travis; Lin, William; Freije, Elizabeth
The EdgeNet platform was conceived of to satisfy graduation requirements and demonstrate a breadth and depth of knowledge capable of working with embedded systems and supporting their integration with larger software platforms. The project is justified as a valid senior design supported by several senior level CpET courses described in the introduction of this report. The EdgeNet IoT platform provides a flexible and brandable platform for connecting consumers and business with devices they wish to monitor or manipulate remotely. Built using industry standard languages and frameworks, consumer facing applications share a common code-base, reducing costs associated with code maintenance and development. The platform is architected around the MQTT (Message Queuing and Telemetry Transport) protocol version 3.1.1. However, the discovery of “Doze Mode,” a feature of Android API versions 23 and above, has complicated the implementation of wireless communication for consumer facing applications. To get around this, the project must be rearchitected to comply with application service windows.
Internet of Things Security Using Proactive WPA/WPA2
(2016-04-05) Kamoona, Mustafa; El-Sharkawy, Mohamed A.; King, Brian; Rizkalla, Maher
The Internet of Things (IoT) is a natural evolution of the Internet and is becoming more and more ubiquitous in our everyday home, enterprise, healthcare, education, and many other aspects. The data gathered and processed by IoT networks might be sensitive and that calls for feasible and adequate security measures. The work in this thesis describes the use of the Wi-Fi technology in the IoT connectivity, then proposes a new approach, the Proactive Wireless Protected Access (PWPA), to protect the access networks. Then a new end to end (e2e) IoT security model is suggested to include the PWPA scheme. To evaluate the solutions security and performance, rstly, the cybersecurity triad: con dentiality, integrity, and availability aspects were discussed, secondly, the solutions performance was compared to a counterpart e2e security solution, the Secure Socket Layer security. A small e2e IoT network was set up to simulate a real environment that uses HTTP protocol. Packets were then collected and analyzed. Data analysis showed a bandwidth e ciency increase by 2% (Internet links) and 12% (access network), and by 344% (Internet links) and 373% (access network) when using persistent and non-persistent HTTP respectively. On the other hand, the analysis showed a reduction in the average request-response delay of 25% and 53% when using persistent and non-persistent HTTP respectively. This scheme is possibly a simple and feasible solution that improves the IoT network security performance by reducing the redundancy in the TCP/IP layers security implementation.
Iot Batch MixingSkid
(2023-05-05) Thang, Siang; Weissbach, Robert; Goodman, David Wayne
This report is a summary of the IoT Batch Mixing Skid Upgrade team’s senior design project to meet the requirements provided by Dr. Elizabeth Freije and Dr. Phil Pash. The request was to upgrade an existing tank instrumentation system that measures flow, temperature, level, and pressure within the system to an IoT capable instrumentation system. The team planned to replace current Endress and Hauser components with components that could communicate wirelessly. The report consists of the objective of the project, the design of the project, and components/devices that will be replaced. The focus of the report is to reveal our design, the new process of the system, how it functions, and the new devices and components that we replaced and tested for this project.
A multi-domain trust management model for supporting RFID applications of IoT
(PLOS, 2017-07-14) Wu, Xu; Li, Feng; Engineering Technology, School of Engineering and Technology
The use of RFID technology in complex and distributed environments often leads to a multi-domain RFID system, in which trust establishment among entities from heterogeneous domains without past interaction or prior agreed policy, is a challenge. The current trust management mechanisms in the literature do not meet the specific requirements in multi-domain RFID systems. Therefore, this paper analyzes the special challenges on trust management in multi-domain RFID systems, and identifies the implications and the requirements of the challenges on the solutions to the trust management of multi-domain RFID systems. A multi-domain trust management model is proposed, which provides a hierarchical trust management framework include a diversity of trust evaluation and establishment approaches. The simulation results and analysis show that the proposed method has excellent ability to deal with the trust relationships, better security, and higher accuracy rate.
Plant Level IIoT Based Energy Management Framework
(2023-05) Koshy, Liya Elizabeth; Chien, Stanley Yung-Ping; Chen, Jie; King, Brian
The Energy Monitoring Framework, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology. The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely. The main objectives of the project include the following: • Set up a wireless network using sensors and smart implants with a base station/ controller. • Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency. • Set up a generalized interface to collect and process the sensor data values and store the data in a database. • Design and develop a generic database compatible with various companies irrespective of the type and size. • Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment. The General Structure of the project constitutes the following components: • A wireless sensor network with a base station. • An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators. • A cloud that hosts a database and an API to collect and store information. • A web application hosted in the cloud to provide an interactive platform for users to analyze the data. The project was demonstrated in: • Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/). • Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/). • A company in Indiana. The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.
Towards Sustainable Water Supply: Schematic Development of Big Data Collection Using Internet of Things (IoT)
(Elsevier, 2015) Koo, Dan; Piratla, Kalyan; Matthews, John C.; Department of Engineering Technology, School of Engineering and Technology
Water supply systems in the United States connect raw water sources to hundreds of millions of water consumers through humongous infrastructure that include approximately one million miles of buried water mains and service connections and thousands of treatment facilities and appurtenances. This enormous set-up is currently operated by more than 170,000 public water systems. Sustainability of the water supply system faces several imminent challenges such as: 1) increasing water main breaks, 2) decreasing fresh water resources, 3) untraceable non-revenue water use, and 4) increasing water demands. However, current water supply management practices are not capable of providing fundamental solutions to the issues identified above. Big Data is a new technical concept to collect massive amounts of relevant data from sensors installed to monitor structural condition, usage, and system performance. This Big Data concept can be realized by deploying Internet of Things (IoT) technology throughout the water supply infrastructure and consumers’ usage. This paper presents a schematic development of IoT application for Big Data collection through a myriad of water clients. The scheme consists of downstream and upstream data collection using Wireless Sensor Network (WSN) technologies connecting to IoT. Downstream data shall provide water usage and performance data to clients and upstream data is similar to traditional SCADA and Automated Meter Reading (AMR) systems. Ultimately, all data will be converged to build a Big Data collection system where data mining identifies 1) local and system performances including pressure and flow, 2) non-revenue and illegitimate water consumption, and 3) locations and quantity of water breaks and water losses. The goal of this development is to enable both utilities and consumers to proactively manage their water usage and achieve higher levels of sustainability in water supply.

Browsing by Subject "IoT"

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