Low-Power Wide-Area Network Design

TL;DR

This paper introduces SNOW, a scalable, energy-efficient LPWAN technology utilizing TV white spaces, with a novel distributed OFDM implementation and a hierarchical SNOW-tree architecture for large-scale IoT deployments.

Contribution

The paper presents SNOW, a new LPWAN design using TV white spaces, including a distributed OFDM implementation and a scalable SNOW-tree architecture, addressing practical deployment challenges.

Findings

SNOW achieves high scalability and energy efficiency.

The proposed algorithms effectively optimize spectrum sharing.

SNOW can connect tens of thousands of nodes over hundreds of kilometers.

Abstract

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.