  Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology
for Internet-of-Things (IoT) due to their capability of communicating over long
distances at very low transmission power. Existing LPWAN technologies, however,
face limitations in meeting scalability and covering very wide areas which make
their adoption challenging for future IoT applications, especially in
infrastructure-limited rural areas. To address this limitation, in this paper,
we consider achieving scal-ability and extended coverage by integrating
multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed
LPWAN architecture over the TV white spaces, has demonstrated its advantages
over existing LPWANs in performance and energy-efficiency. In this paper, we
propose to scale up LPWANs through a seamless integration of multiple SNOWs
which enables concurrent inter-SNOW and intra-SNOW communications. We then
formulate the tradeoff between scalability and inter-SNOW interference as a
constrained optimization problem whose objective is to maximize scalability by
managing white space spectrum sharing across multiple SNOWs. We also prove the
NP-hardness of this problem. To this extent, We propose an intuitive
polynomial-time heuristic algorithm for solving the scalability optimization
problem which is highly efficient in practice. For the sake of theoretical
bound, we also propose a simple polynomial-time 1/2-approximation algorithm for
the scalability optimization problem. Hardware experiments through deployment
in an area of (25x15)sq. km as well as large scale simulations demonstrate the
effectiveness of our algorithms and feasibility of achieving scalability
through seamless integration of SNOWs with high reliability, low latency, and
energy efficiency.
