On the scalability and message count of Trickle-based broadcasting schemes

Abstract

As the use of wireless sensor networks increases, the need for 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 analyze the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. We show that the broadcasting process of a network using Trickle can be modeled by a Markov chain and that this chain falls under a class of Markov chains, closely related to residual lifetime distributions. It is then shown that this class of Markov chains admits a stationary distribution of a special form. These results are used to analyze the Trickle algorithm and its message count. Our results prove conjectures made in the literature concerning the effect of a listen-only period. 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.

Degree-dependent threshold-based random sequential adsorption on random trees

“…The reasoning behind this is that if many of your neighbors have already sent out their data, your broadcast is probably redundant and should be suppressed. These simple rules lead to scalable broadcasting schemes, where the number of broadcasts per time unit remains bounded, even when the network becomes more and more dense [14].…”

Trickle-D: High Fairness and Low Transmission Load With Dynamic Redundancy

“…The unfairness of Trickle has been mentioned in some works, in particular [15] and [19]. The effect of desynchronization on fairness has been analyzed in [18], in a more general case.…”

Adaptive broadcast suppression for Trickle-based protocols

“…Simulation results have shown that Trickle scales well with network density, suppressing many redundant broadcasts [1], [8]. In [7] the authors provide analytical results on Trickle's message overhead and broadcasting rate and show how they depend on k and the network size. These results prove Trickle's scalability and show that its message overhead scales linearly in k/I max .…”

“…For α < 1, the solution to (7) can be written recursively by defining π α (x) on distinct intervals as follows…”