Monitoring Humans and Improving Wireless Network Performance with Heterogenous IoT Devices
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Date
2018-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
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Abstract
According to Gartner, the number of the internet of things (IoT) devices is growing exponentially to reach 26 billion by 2020. IoT devices are being designed to be used in smart building for vastly different applications such as automation, security, industrial controls, and life-saving health monitor. To be cost-effective and utilize existing infrastructure, these IoT devices must utilize the vast amount of existing radio frequency (RF) for human monitoring, movement tracking, and identification information. Moreover, these IoT devices use different radios and protocols to communicate due to different cost, data-rate, communication-range, frequency occupancy, and energy consumption requirements. Thus, these devices cannot directly communicate with each other while occupying the same frequency bands. Therefore, IoT networks face two challenges: privacy-preserving monitoring, tracking, and identification and efficient heterogeneous radio coexistence. These challenges raise fundamental questions: 1) How can we use these IoT signals to monitor, track, and identify people in a privacy-preserving manner? 2) How can IoT devices that use different radios, frequencies, and modulation mechanisms (e.g., WiFi and ZigBee) communicate efficiently (increase throughput, lower energy usage, and lower latency) with each other? The promising techniques to address these questions are to 1) perform channel state measurements between transmitter and receivers, 2) create hybrid WiFi-ZigBee subcarriers on the overlapped channel, and 3) recycle signals by leveraging low power consumption backscatter radios. By sensing channel state information (CSI), WiFi radios can produce human monitoring and tracking signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Moreover, we determine that combination of the CSI and hybrid WiFi-ZigBee subcarriers allow for concurrent bidirectional ZigBee and WiFi communication. Leveraging the same WiFi-ZigBee hybrid subcarriers, devices can produce signals allowing for ultra-low power backscatter radios. This thesis addresses these challenges by making the following contributions: Wobly allows for privacy-preserving tracking and positioning based on human gait using Wi-Fi CSI. Moreover, we can identify specific human body movements. Chiron enables concurrently transmitting (or receiving) 1 stream of WiFi data and up to 4 streams of ZigBee data to (or from) commodity WiFi and ZigBee devices as if there is no interference between these simultaneous connections due to CSI sensing. Passive-ZigBee demonstrates we can transform an existing productive WiFi signal into a ZigBee packet for a CoTS low-power consumption receiver. Moreover, this low power backscatter radio can bridge between the ZigBee and WiFi devices by relaying data allowing heterogenous radios to communicate with each other. Our empirical evaluations show that i) Wobly correctly identifies at a rate of 87% and localizes rate of 90%. ii) Chiron's concurrent WiFi and ZigBee communication can achieve similar throughput as the sole WiFi or ZigBee communication. Chiron's spectrum utilization is more than 16 times better than a traditional IoT gateway. iii) Passive-ZigBee consumes 1,440 times lower power compared to traditional ZigBee while able to maintain maximum ZigBee standard network throughput. Passive-ZigBee also can relay data between WiFi and ZigBee networks.