  LPWAN is an enabling technology for long-range, low-power, and low-cost
IoT/CPS applications. Recently, multiple LPWAN technologies have been developed
that operate in the licensed (e.g., 5G) and ISM (e.g., LoRa) bands. To avoid
the crowd in the ISM band and the cost of the licensed band, we propose a novel
LPWAN called Sensor Network Over White Spaces (SNOW) by utilizing the TV white
spaces. Specifically, we design, develop, and experiment SNOW, which is highly
scalable, energy-efficient, and has a long communication range. SNOW achieves
scalability and energy efficiency by enabling concurrent packets reception at a
BS using a single radio from numerous sensors and concurrent packets
transmission to numerous sensors from the BS using a single radio,
simultaneously, which we achieve by proposing a distributed implementation of
OFDM. To enable the low-cost and scalable SNOW deployment in practical
applications, we implement SNOW using the low-cost and small form-factored COTS
devices, where we address multiple practical challenges including the high
peak-to-average power ratio, channel state estimation, and carrier offset
estimation. Also, we propose an adaptive transmission power protocol to handle
the near-far power problem. 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. We address the intra-
and inter-SNOW interferences 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.
