Relevant opportunistic information extraction and scheduling in heterogeneous sensor networks

Abstract

Determining the output of the most relevant sensor is of crucial importance when heterogeneous sensors are available for measuring a given process in an environment. In this paper, we describe an IEEE 1451 TEDS (transducer electronic data sheets) compliant sensor model for heterogeneous sensor networks. The proposed model uses the relevance feedback method to understand the context of a sensor learning application. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D real-time robotics software player/gazebo on an autonomous mobile robot's navigation problem. The results show that the proposed model can be utilised in the real-time scenario and can help reduce the computational cost of a system