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- Creators: Arizona State University
- Creators: Simonson, Mark
- Creators: Berger, Nicholas
Description
With the new age Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this thesis, a wireless system is proposed which is targeted towards very low power, delay insensitive IoT applications with low throughput requirements. The low cost receivers for such devices will have very low complexity, consume very less power and hence will run for several years.
Long Term Evolution (LTE) is a standard developed and administered by 3rd Generation Partnership Project (3GPP) for high speed wireless communications for mobile devices. As a part of Release 13, another standard called narrowband IoT (NB-IoT) was introduced by 3GPP to serve the needs of IoT applications with low throughput requirements. Working along similar lines, this thesis proposes yet another LTE based solution called very narrowband IoT (VNB-IoT), which further reduces the complexity and power consumption of the user equipment (UE) while maintaining the base station (BS) architecture as defined in NB-IoT.
In the downlink operation, the transmitter of the proposed system uses the NB-IoT resource block with each subcarrier modulated with data symbols intended for a different user. On the receiver side, each UE locks to a particular subcarrier frequency instead of the entire resource block and operates as a single carrier receiver. On the uplink, the system uses a single-tone transmission as specified in the NB-IoT standard.
Performance of the proposed system is analyzed in an additive white Gaussian noise (AWGN) channel followed by an analysis of the inter carrier interference (ICI). Relationship between the overall filter bandwidth and ICI is established towards the end.
Long Term Evolution (LTE) is a standard developed and administered by 3rd Generation Partnership Project (3GPP) for high speed wireless communications for mobile devices. As a part of Release 13, another standard called narrowband IoT (NB-IoT) was introduced by 3GPP to serve the needs of IoT applications with low throughput requirements. Working along similar lines, this thesis proposes yet another LTE based solution called very narrowband IoT (VNB-IoT), which further reduces the complexity and power consumption of the user equipment (UE) while maintaining the base station (BS) architecture as defined in NB-IoT.
In the downlink operation, the transmitter of the proposed system uses the NB-IoT resource block with each subcarrier modulated with data symbols intended for a different user. On the receiver side, each UE locks to a particular subcarrier frequency instead of the entire resource block and operates as a single carrier receiver. On the uplink, the system uses a single-tone transmission as specified in the NB-IoT standard.
Performance of the proposed system is analyzed in an additive white Gaussian noise (AWGN) channel followed by an analysis of the inter carrier interference (ICI). Relationship between the overall filter bandwidth and ICI is established towards the end.
ContributorsSharma, Prashant (Author) / Bliss, Daniel (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / McGiffen, Thomas (Committee member) / Arizona State University (Publisher)
Created2017
Description
The Internet of Things (IoT) has become a more pervasive part of everyday life. IoT networks such as wireless sensor networks, depend greatly on the limiting unnecessary power consumption. As such, providing low-power, adaptable software can greatly improve network design. For streaming live video content, Wireless Video Sensor Network Platform compatible Dynamic Adaptive Streaming over HTTP (WVSNP-DASH) aims to revolutionize wireless segmented video streaming by providing a low-power, adaptable framework to compete with modern DASH players such as Moving Picture Experts Group (MPEG-DASH) and Apple’s Hypertext Transfer Protocol (HTTP) Live Streaming (HLS). Each segment is independently playable, and does not depend on a manifest file, resulting in greatly improved power performance. My work was to show that WVSNP-DASH is capable of further power savings at the level of the wireless sensor node itself if a native capture program is implemented at the camera sensor node. I created a native capture program in the C language that fulfills the name-based segmentation requirements of WVSNP-DASH. I present this program with intent to measure its power consumption on a hardware test-bed in future. To my knowledge, this is the first program to generate WVSNP-DASH playable video segments. The results show that our program could be utilized by WVSNP-DASH, but there are issues with the efficiency, so provided are an additional outline for further improvements.
ContributorsKhan, Zarah (Author) / Reisslein, Martin (Thesis advisor) / Seema, Adolph (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2018
Description
Video capture, storage, and distribution in wireless video sensor networks
(WVSNs) critically depends on the resources of the nodes forming the sensor
networks. In the era of big data, Internet of Things (IoT), and distributed
demand and solutions, there is a need for multi-dimensional data to be part of
the Sensor Network data that is easily accessible and consumable by humanity as
well as machinery. Images and video are expected to become as ubiquitous as is
the scalar data in traditional sensor networks. The inception of video-streaming
over the Internet, heralded a relentless research for effective ways of
distributing video in a scalable and cost effective way. There has been novel
implementation attempts across several network layers. Due to the inherent
complications of backward compatibility and need for standardization across
network layers, there has been a refocused attention to address most of the
video distribution over the application layer. As a result, a few video
streaming solutions over the Hypertext Transfer Protocol (HTTP) have been
proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion
Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These
frameworks, do not address the typical and future WVSN use cases. A highly
flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)
are introduced. The platform's goal is to usher video as a data element that
can be integrated into traditional and non-Internet networks. A low cost,
scalable node is built from the ground up to be fully compatible with the
Internet of Things Machine to Machine (M2M) concept, as well as the ability to
be easily re-targeted to new applications in a short time. Flexi-WVSNP design
includes a multi-radio node, a middle-ware for sensor operation and
communication, a cross platform client facing data retriever/player framework,
scalable security as well as a cohesive but decoupled hardware and software
design.
(WVSNs) critically depends on the resources of the nodes forming the sensor
networks. In the era of big data, Internet of Things (IoT), and distributed
demand and solutions, there is a need for multi-dimensional data to be part of
the Sensor Network data that is easily accessible and consumable by humanity as
well as machinery. Images and video are expected to become as ubiquitous as is
the scalar data in traditional sensor networks. The inception of video-streaming
over the Internet, heralded a relentless research for effective ways of
distributing video in a scalable and cost effective way. There has been novel
implementation attempts across several network layers. Due to the inherent
complications of backward compatibility and need for standardization across
network layers, there has been a refocused attention to address most of the
video distribution over the application layer. As a result, a few video
streaming solutions over the Hypertext Transfer Protocol (HTTP) have been
proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion
Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These
frameworks, do not address the typical and future WVSN use cases. A highly
flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)
are introduced. The platform's goal is to usher video as a data element that
can be integrated into traditional and non-Internet networks. A low cost,
scalable node is built from the ground up to be fully compatible with the
Internet of Things Machine to Machine (M2M) concept, as well as the ability to
be easily re-targeted to new applications in a short time. Flexi-WVSNP design
includes a multi-radio node, a middle-ware for sensor operation and
communication, a cross platform client facing data retriever/player framework,
scalable security as well as a cohesive but decoupled hardware and software
design.
ContributorsSeema, Adolph (Author) / Reisslein, Martin (Thesis advisor) / Kitchen, Jennifer (Committee member) / Seeling, Patrick (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2017
Description
This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation is then depicted through a modified pitch book that contains a financial model and pro forma.
ContributorsLarrea, Justin (Co-author) / Berger, Nicholas (Co-author) / Peters, Matthew (Co-author) / Simonson, Mark (Thesis director) / Gray, William (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation is then depicted through a modified pitch book that contains a financial model and pro forma.
ContributorsBerger, Nicholas James (Co-author) / Larrea, Justin (Co-author) / Peters, Matthew (Co-author) / Simonson, Mark (Thesis director) / Gray, William (Committee member) / School of Accountancy (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation is then depicted through a modified pitch book that contains a financial model and pro forma.
ContributorsPeters, Matthew Scott (Co-author) / Larrea, Justin (Co-author) / Berger, Nicholas (Co-author) / Simonson, Mark (Thesis director) / Gray, William (Committee member) / Department of Finance (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT
technology, smart wearables can handle diverse operations, including networking and
recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying
smartphones now have data processing capability. Edge computing on such devices
enables real-time feedback and in turn preemptive identification of reactions to stress.
This can provide an opportunity to prevent more severe consequences that might
result if stress is unaddressed. From a system perspective, leveraging edge computing
allows saving energy such as network bandwidth and latency since it processes data in
proximity to the data source. It can also strengthen privacy by implementing stress
prediction at local devices without transferring personal information to the public
cloud.
This thesis presents a framework for real-time stress prediction using Fitbit and
machine learning with the support from cloud computing. Fitbit is a wearable tracker
that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate
fluctuations and corresponding stress levels entered by users. I also established the
dataset collected from police cadets during their academy training program. Machine
learning classifiers for stress prediction are built using classic models and TensorFlow
in the cloud. Lastly, the classifiers are optimized using model compression techniques
for deploying them on the smartphones and analyzed how efficiently stress prediction
can be performed on the edge.
ContributorsSim, Sang-Hun (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2022
Description
This project explores the potential for the accurate prediction of basketball shooting posture with machine learning (ML) prediction algorithms, using the data collected by an Internet of Things (IoT) based motion capture system. Specifically, this question is addressed in the research - Can I develop an ML model to generalize a decent basketball shot pattern? - by introducing a supervised learning paradigm, where the ML method takes acceleration attributes to predict the basketball shot efficiency. The solution presented in this study considers motion capture devices configuration on the right upper limb with a sole motion sensor made by BNO080 and ESP32 attached on the right wrist, right forearm, and right shoulder, respectively, By observing the rate of speed changing in the shooting movement and comparing their performance, ML models that apply K-Nearest Neighbor, and Decision Tree algorithm, conclude the best range of acceleration that different spots on the arm should implement.
ContributorsLiang, Chengxu (Author) / Ingalls, Todd (Thesis advisor) / Turaga, Pavan (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2023
Description
Realizing the applications of Internet of Things (IoT) with the goal of achieving a more efficient and automated world requires billions of connected smart devices and the minimization of hardware cost in these devices. As a result, many IoT devices do not have sufficient resources to support various protocols required in many IoT applications. Because of this, new protocols have been introduced to support the integration of these devices. One of these protocols is the increasingly popular routing protocol for low-power and lossy networks (RPL). However, this protocol is well known to attract blackhole and sinkhole attacks and cause serious difficulties when using more computationally intensive techniques to protect against these attacks, such as intrusion detection systems and rank authentication schemes. In this paper, an effective approach is presented to protect RPL networks against blackhole attacks. The approach does not address sinkhole attacks because they cause low damage and are often used along blackhole attacks and can be detected when blackhole attaches are detected. This approach uses the feature of multiple parents per node and a parent evaluation system enabling nodes to select more reliable routes. Simulations have been conducted, compared to existing approaches this approach would provide better protection against blackhole attacks with much lower overheads for small RPL networks.
ContributorsSanders, Kent (Author) / Yau, Stephen S (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2021
Description
Increased awareness and technological solutions will not solve the global ecological crises of climate change and mass extinction by themselves. A fundamental shift is needed in how we view ourselves and our relationships with all life to avoid further degradation of the biosphere and ensure a more equitable future. A crucial part of such a shift means expanding the range of species that fall under human consideration. Viewing non-human life, including plants, as intrinsically rather than instrumentally valuable can be transformative to how we, as a species, think about and enact practices that encourage sustainable development. By highlighting the intelligence and communication abilities of plant life through artistic work, a strong counter-narrative can be developed against the dominant utilitarian view of plants as merely a resource for human cultivation and consumption. This dissertation explores plant intelligence and communication as models for music composition and networked sound installations. It is comprised of two complementary components, a sound installation, Unheard Voices, and the following document that explores the relevant artistic precedents, ecological, philosophical, and practice-based research that was conducted to facilitate the creation of the installation project. Focusing this research are the questions: 1) How can plant intelligence in communication, as outlined by plant neurobiologists and ecologists, serve as a model for creating sound installations? 2) How can such art pieces help viewers reflect on humanity’s interconnection to nature and reconsider plants as sentient, communicative, and intrinsically rather than instrumentally valuable?
ContributorsArne, Devin (Author) / Paine, Garth (Thesis advisor) / Feisst, Sabine (Committee member) / Broglio, Ronald (Committee member) / Arizona State University (Publisher)
Created2021