Censoring Diffusion for Harvesting WSNs

In this paper, we analyze energy-harvesting adaptive diffusion networks for a distributed estimation problem. In order to wisely manage the available energy resources, we propose a scheme where a censoring algorithm is jointly applied over the diffusion strategy. An energy-aware variation of a diffusion algorithm is used, and a new way of measuring the relevance of the estimates in diffusion networks is proposed in order to apply a subsequent censoring mechanism. Simulation results show the potential benefit of integrating censoring schemes in energy-constrained diffusion networks.Comment: Accepted in 2015 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015

A Sampling Algorithm for Diffusion Networks

In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.Comment: Change from previous version: included a header in the first page regarding the paper's acceptance at EUSIPC

A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks

Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sampling and censoring techniques have been topics of intense research, since the cost associated with measuring and/or transmitting data throughout the entire network may be prohibitive in certain applications. In this paper, we propose a low-cost adaptive mechanism for sampling and censoring over diffusion networks that uses information from more nodes when the error in the network is high and from less nodes otherwise. It presents fast convergence during transient and a significant reduction in computational cost and energy consumption in steady state. As a censoring technique, we show that it is able to noticeably outperform other solutions. We also present a theoretical analysis to give insights about its operation, and to help the choice of suitable values for its parameters.Comment: arXiv admin note: text overlap with arXiv:2007.0645