Empirical Model Development for Message Delay and Drop in Wireless Sensor Networks

Simulation of wireless sensor networks with very large number of motes poses significant challenges with respect to computational complexity. Application level code prototyping with reasonable accuracy and fidelity can however be accomplished through simulation that models only the effects of the wireless and distributed computations which materialize as delay and drop for the messages being exchanged among the motes. This study pursues that idea of empirical modelling of delay and drop and employs such a model to affect the reception times of wirelessly communicated messages. The delay and drop is therefore, modelled as random variables with probability distributions empirically approximated based on the data reported in the literature. The paper concludes with a case study that employs the proposed empirical delay and drop models for multilayer perceptron neural networks distributed across a wireless sensor network for a classification task on the Isolet dataset.