Access Type

Open Access Dissertation

Date of Award

January 2020

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Abusayeed Saifullah

Abstract

Low-Power Wide-Area Network (LPWAN) is an enabling technology for long-range, low-power, and low-cost Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications. Due to their escalating demand in the IoT/CPS applications, recently, multiple LPWAN technologies have been developed that operate in the cellular/licensed (e.g., 5G, LTE Cat M1, and NB-IoT) and unlicensed/ISM (e.g., LoRa and SigFox) bands. To avoid the crowd in the limited ISM band (where most LPWANs operate) and the cost of the licensed band, we propose a novel LPWAN technology called Sensor Network Over White Spaces (SNOW) by utilizing the TV white spaces. White spaces refer to the allocated but locally unused TV channels (54--698MHz in the US) and can be used by the unlicensed devices as secondary users. White spaces offer less crowded and much wider spectrum in both urban and rural areas, boasting an abundance in rural and suburbs and have excellent propagation and obstacle penetration characteristics that enable long-range communication.

In this thesis, we design, develop, and experiment SNOW that is highly scalable, energy-efficient, and can connect thousands of sensors over a single-hop distance of several kilometers. SNOW achieves scalability and energy efficiency by enabling concurrent packets reception (Rx) at a base station (BS) using a single radio from numerous sensors and concurrent packets transmission (Tx) to numerous sensors from the BS using a single radio, simultaneously, which we achieve by proposing a distributed implementation of Orthogonal Frequency Division Multiplexing. We also demonstrate the feasibility of SNOW design by implementing on a prototype hardware called Universal Software Radio Peripheral(USRP).

To enable the low-cost and scalable SNOW deployment in practical IoT/CPS applications, we then implement SNOW using the low-cost and small form-factored commercial off-the-shelf (COTS) devices, where we address multiple practical challenges including the peak-to-average power ratio (PARP) problem handling, channel state information estimation, and carrier frequency offset estimation. Additionally, we propose an adaptive transmission power protocol for the SNOW nodes to handle the near-far power problem in SNOW. To demonstrate the feasibility of COTS SNOW implementation, we use TI CC1310 and TI CC1350 devices as SNOW nodes and deploy in the city of Detroit, Michigan.

To enable connecting tens of thousands of nodes over hundreds of kilometers, we further propose a network architecture called SNOW-tree through a seamless integration of multiple SNOWs where they form a tree structure and are under the same management/control at the tree root. We address the intra-SNOW and inter-SNOW interferences in SNOW-tree by formulating a constrained optimization problem called the scalability optimization problem (SOP) whose objective is to maximize scalability by managing the spectrum sharing across the SNOWs. By proving the NP-hardness of SOP, we then propose two polynomial-time methods to solve it: a greedy heuristic algorithm and a 1/2-approximation algorithm. Our deployment covering approximately (25x15)sq. km in the Detroit metropolitan area demonstrates that both of our algorithms are highly efficient in practice.

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