Data Dissemination Performance in Large-Scale Sensor Networks

TL;DR

This paper analyzes the message dissemination performance of the Trickle algorithm in large-scale wireless sensor networks, providing a mathematical model, a generalized version with tunable parameters, and validation through simulations.

Contribution

It introduces a generalized Trickle algorithm with a listen-only period parameter and offers a mathematical analysis of message count and inter-broadcast times in large-scale networks.

Findings

Message count increases with network size and depends on Trickle parameters.

Derived distributions of inter-broadcasting times and their asymptotic behavior.

Validated analytical results with simulations.

Abstract

As the use of wireless sensor networks increases, the need for (energy-)efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper we develop a model describing the message count in large-scale wireless sensor networks. We focus our attention on the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. This generalization proves to be useful for optimizing the design and usage of the algorithm. For single-cell networks we show how the message count increases with the size of the network and how this depends on the Trickle parameters. Furthermore, we derive distributions of inter-broadcasting times and investigate their asymptotic behavior. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Additionally, we develop an approximation for the expected number of transmissions in multi-cell networks. All results are validated by simulations.