Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach

TL;DR

This paper introduces a stochastic modeling framework to accurately estimate the convergence time of desynchronization algorithms in wireless sensor networks, validated through experiments and simulations.

Contribution

It provides the first stochastic-based convergence estimates for desynchronization algorithms, improving upon previous conjectures and bounds.

Findings

Stochastic model closely matches experimental convergence times.

Model applies to Desync and pulse-coupled oscillator algorithms.

Estimates outperform existing bounds in accuracy.

Abstract

Desynchronization approaches in wireless sensor networks converge to time-division multiple access (TDMA) of the shared medium without requiring clock synchronization amongst the wireless sensors, or indeed the presence of a central (coordinator) node. All such methods are based on the principle of reactive listening of periodic "fire" or "pulse" broadcasts: each node updates the time of its fire message broadcasts based on received fire messages from some of the remaining nodes sharing the given spectrum. In this paper, we present a novel framework to estimate the required iterations for convergence to fair TDMA scheduling. Our estimates are fundamentally different from previous conjectures or bounds found in the literature as, for the first time, convergence to TDMA is defined in a stochastic sense. Our analytic results apply to the Desync algorithm and to pulse-coupled oscillator algorithms with inhibitory coupling. The experimental evaluation via iMote2 TinyOS nodes (based on the IEEE 802.15.4 standard) as well as via computer simulations demonstrates that, for the vast majority of settings, our stochastic model is within one standard deviation from the experimentally-observed convergence iterations. The proposed estimates are thus shown to characterize the desynchronization convergence iterations significantly better than existing conjectures or bounds. Therefore, they contribute towards the analytic understanding of how a desynchronization-based system is expected to evolve from random initial conditions to the desynchronized steady state.