Information diffusion algorithms over WSNs for non-asymptotic confidence region evaluation

Getting confidence regions for parameter estimates obtained from data collected by a wireless sensor network (WSN) is very important to assess the performance of the estimator. The sign perturbed sums (SPS) approach has been proposed recently to defined exact confidence regions in a centralized setting even if only few measurements are available. SPS may be distributed to get confidence regions at each node of a WSN. This paper investigates a data dissemination strategy called Tagged and Aggregated Sums (TAS), exploiting the particularities of SPS, to efficiently provide each node with the information necessary to evaluate locally the confidence region. TAS and flooding (FL) algorithms have been investigated through simulations and then implemented on commercial sensor nodes. The impact of collision avoidance mechanisms at the medium access control (MAC) layer is also experimentally assessed. Performance comparisons show that TAS outperforms FL in structured networks.