Access Type

Open Access Dissertation

Date of Award

January 2021

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Abusayeed Saifullah

Second Advisor

Sanjay Madria

Abstract

Low-Power Wide-Area Network (LPWAN) is regarded as the leading communication technology for wide-area Internet-of-Things (IoT) applications. It offers low-power, long-range, and low-cost communication. With different communication requirements for varying IoT applications, many competing LPWAN technologies operating in both licensed (e.g., NB-IoT, LTE-M, and 5G) and unlicensed (e.g., LoRa and SigFox) bands have emerged. LPWANs are designed to support applications with low-power and low data rate operations. They are not well-designed to host applications that involve high mobility, high traffic, or real-time communication (e.g., volcano monitoring and control applications).With the increasing number of mobile devices in many IoT domains (e.g., agricultural IoT and smart city), mobility support is not well-addressed in LPWAN. Cellular-based/licensed LPWAN relies on the wired infrastructure to enable mobility. On the other hand, most unlicensed LPWANs operate on the crowded ISM band or are required to duty cycle, making handling mobility a challenge.

In this dissertation, we first identify the key opportunities of LPWAN, highlight the challenges, and show potential directions for future research. We then enable the versatility of LPWAN applications first by enabling applications involving mobility over LPWAN. Specifically, we propose to handle mobility in LPWAN over white space considering Sensor Network Over White Space (SNOW). SNOW is a highly scalable and energy-efficient LPWAN operating over the TV white spaces. TV white spaces are the allocated but locally unused available TV channels (54 - 698 MHz in the US). We proposed a dynamic Carrier Frequency Offset (CFO) estimation and compensation technique that considers the impact of the Doppler shift due to mobility. Also, we design energy-efficient and fast BS discovery and association approaches. Finally, we demonstrate the feasibility of our approach through experiments in different deployments.

Finally, we present a collision detection and recovery technique called RnR (Reverse & Replace Decoding) that applies to LPWANs. Additionally, we discuss future work to enable handling burst transmission over LPWAN and localization in mobile LPWAN.

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