Distributed Flooding-based Storage Algorithms for Large-scale Sensor Networks

TL;DR

This paper introduces two distributed flooding-based storage algorithms for large-scale wireless sensor networks, focusing on efficient data dissemination and storage despite node failures and limited resources.

Contribution

The paper presents novel distributed flooding algorithms that adapt to unknown network size and optimize data dissemination and storage in sensor networks.

Findings

Algorithms are efficient in encoding and decoding operations.

Performance analysis matches simulation results.

Algorithms adapt to network size and node degree.

Abstract

In this paper we propose distributed flooding-based storage algorithms for large-scale wireless sensor networks. Assume a wireless sensor network with $n$ nodes that have limited power, memory, and bandwidth. Each node is capable of both sensing and storing data. Such sensor nodes might disappear from the network due to failures or battery depletion. Hence it is desired to design efficient schemes to collect data from these $n$ nodes. We propose two distributed storage algorithms (DSA's) that utilize network flooding to solve this problem. In the first algorithm, DSA-I, we assume that every node utilizes network flooding to disseminate its data throughout the network using a mixing time of approximately O(n). We show that this algorithm is efficient in terms of the encoding and decoding operations. In the second algorithm, DSA-II, we assume that the total number of nodes is not known to every sensor; hence dissemination of the data does not depend on $n$. The encoding operations in this case take $O(C\mu^2)$, where $\mu$ is the mean degree of the network graph and $C$ is a system parameter. We evaluate the performance of the proposed algorithms through analysis and simulation, and show that their performance matches the derived theoretical results.