Enhancing data persistence for energy constrained networks by network modulation

Maintaining data persistence in a scalable fashion for large-scale distributed systems has become critical and essential. It becomes more challenging when nodes have finite energy. In this work, we propose a novel approach called network modulation (NeMo) to significantly improve the data persistence. Built on algebraic number theory, NeMo operates at the level of modulated symbols. Its core notion is to mix data at intermediate network nodes and meanwhile guarantee the symbol recovery at the sink(s) without pre-storing or waiting for other symbols. The persistence performance of NeMo has been evaluated by simulations to show that the proposed approach is efficient to enhance the data persistence for energy-constrained networks.