Learning Sensor Data Characteristics in Unknown Environments

Abstract

Ad hoc wireless sensor networks derive much of their promise from their potential for autonomously monitoring remote or physically inaccessible locations. As we begin to deploy sensor networks in real world applications, concerns are being raised about the fidelity and integrity of the sensor network data. In this paper, we motivate and propose an online algorithm that leverages competitive learning neural network for characterization of a dynamic, unknown environment. Based on the proposed characterization sensor networks can autonomously construct multimodal views of their environments and derive the conditions for verifying data integrity over time