ON THE STOCHASTIC MODELING OF DESYNCHRONIZATION CONVERGENCE IN WIRELESS SENSOR NETWORKS

Desynchronization is a fundamental approach in wireless sensor networks that allows for convergence to time-division multiple access (TDMA) of the medium without the need for clock synchronization and centralized coordination. The method is based on the concept of reactive listening of periodic fire message broadcasts between nodes sharing the given spectrum. We propose a novel framework to estimate the required iterations for convergence to fair TDMA scheduling. Unlike previous conjectures or bounds found in the literature, our estimation framework is based on a stochastic modeling approach. Experiments via imote2 TinyOS nodes and simulations demonstrate that the proposed estimates characterize the experimental desynchronization convergence iterations significantly better than existing conjectures or bounds.