Large-scale distributed systems such as sensor networks usually experience dynamic topology changes, data losses, and node failures in various catastrophic or emergent environments. As such, maintaining data persistence in a scalable fashion has become critical and essential for such systems. The existing major efforts such as coding, routing, and traditional modulation all have their own limitations. In this work, we propose a novel network modulation (NeMo) approach to significantly improve the data persistence. Built on algebraic number theory, NeMo operates at the level of modulated symbols (so-called "modulation over modulation"). Its core notion is to mix data at intermediate network nodes and meanwhile guarantee the symbol recovery at the sink(s) without prestoring or waiting for other symbols. In contrast to the traditional thought that n linearly independent equations are needed to solve for n unknowns, NeMo opens a new regime to boost the convergence speed of achieving persistence. Different performance criteria (e.g., modulation and demodulation complexity, convergence speed, finite-bit representation, and noise robustness) have been evaluated in the comprehensive simulations and real experiments to show that the proposed approach is efficient to enhance the network data persistence.
['Xiaoli Ma', 'Giwan Choi', 'Wei Zhang']
Network modulation: an algebraic approach to enhancing network data persistence