Network Coding for Energy-Efficient Distributed Storage System in Wireless Sensor Networks

TL;DR

This paper introduces a network coding-based scheme that enhances energy efficiency in wireless sensor network storage by using sparse generator matrices and iterative repair methods, reducing energy use and balancing network load.

Contribution

The paper presents a novel network coding scheme with a sparse generator matrix and an efficient repair method tailored for energy-efficient distributed storage in WSNs.

Findings

Reduces energy consumption in distributed storage systems

Balances energy load across the network

Works over very small finite fields

Abstract

A network coding-based scheme is proposed to improve the energy efficiency of distributed storage systems in WSNs (wireless sensor networks), which mainly focuses on two problems: firstly, consideration is given to effective distributed storage technology in WSNs; secondly, we address how to repair the data in failed storage nodes with less resource. For the first problem, we propose a method to obtain a sparse generator matrix to construct network codes, and this sparse generator matrix is proven to be the sparsest. Benefiting from the sparse generator matrix, the energy consumption required to implement distributed storage is reduced. For the second problem, we designed a network coding-based iterative repair method, which adequately utilizes the idea of re-encoding at intermediate nodes from network coding theory. Benefiting from the re-encoding, the energy consumption required by data repair is significantly reduced. Moreover, we provide an explicit lower bound of field size required by this scheme, which implies that this scheme can work over a very small field and the required computation overhead of coding is very low. The simulation result verifies that by using our scheme, the total energy consumption required to implement distributed storage system in WSNs can be reduced on the one hand, and on the other hand, this method can also balance energy consumption of the networks.