Sensor Network-Based Nonlinear System Identification

Abstract

Abstract. In this paper, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm, that uses the distributed Karhunen-Loève transform, extends in a decentralized setting the KLT-based identification approach that have recently been proposed for a centralized setting. The effectiveness of the proposed methodology is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. The results in the identification of a system whose behavior is described by a partial differential equation in a 2-D domain with random excitation confirms the effectiveness of this technique.

Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

“…A strategy by which sensor nodes detect and estimate non-localized phenomena such as boundaries and edges (e.g., temperature gradients, variations in illumination or contamination levels) can be found in study [13]. A general class of boundaries, with mild regularity assumptions, is considered, and a theory on the achievable performance of sensor network based boundary estimation is established.…”

“…The theoretical study considers networks with dynamically changing communication topology. In the paper [13] an algorithm for the identification of distributed systems by large-scale collaborative sensor networks is initiated, based on the distributed KarhunenLoève transformation. The reduction of the distortions at the nodes and the low computational complexity of the fusion algorithm at the fusion centre are taken into consideration.…”