Distributed Data Storage in Large-Scale Sensor Networks Based on LT Codes

TL;DR

This paper introduces a scalable distributed data storage algorithm for large sensor networks using LT codes, enhancing data persistency and efficiency without global information exchange.

Contribution

It presents a novel LT code-based algorithm that avoids global parameter estimation, improves random walk mixing, and is scalable to various network topologies.

Findings

Enhanced data persistency demonstrated through simulations.

Reduced transmission overhead compared to previous algorithms.

Improved random walk mixing time with a new probabilistic forwarding scheme.

Abstract

This paper proposes an algorithm for increasing data persistency in large-scale sensor networks. In the scenario considered here, k out of n nodes sense the phenomenon and produced ? information packets. Due to usually hazardous environment and limited resources, e.g. energy, sensors in the network are vulnerable. Also due to the large size of the network, gathering information from a few central hopes is not feasible. Flooding is not a desired option either due to limited memory of each node. Therefore the best approach to increase data persistency is propagating data throughout the network by random walks. The algorithm proposed here is based on distributed LT (Luby Transform) codes and it benefits from the low complexity of encoding and decoding of LT codes. In previous algorithms the essential global information (e.g., n and k) are estimated based on graph statistics, which requires excessive transmissions. In our proposed algorithm, these values are obtained without additional transmissions. Also the mixing time of random walk is enhanced by proposing a new scheme for generating the probabilistic forwarding table of random walk. The proposed method uses only local information and it is scalable to any network topology. By simulations the improved performance of developed algorithm compared to previous ones has been verified.