Multisensor Data Fusion
Multi-Sensor Data Fusion: An Introduction
Multi-Sensor Data Fusion: An Introduction
Introduction to Information Quality
Introduction to Information Quality
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Smart environments refer to buildings or locations equipped with a multitude of sensors and processing mechanisms for improved security, efficiency or functionality. Often, these sensors serve distinct purposes and their data may be processed separately by entirely separate systems. We argue that integrated processing of data available from multiple types of sensors can benefit a variety of decision making processes. For example, smart building sensors such as occupancy or temperature sensors used for lighting or heating efficiency can benefit the security system, or vice versa. Recent industry standards in sensor networks such as ZigBee make it possible to collect and aggregate data from multiple, heterogeneous sensors efficiently. However, integrated information processing with a diverse set of sensor data is still a challenge. We provide an information processing scheme that offers data fusion for multiple sensors such as temperature sensors or motion detectors and visual sensors such as security cameras. The broader goal of multi-sensor data fusion in this context is to enhance security systems, improve energy efficiency by supporting the decision making process based on relevant and accurate information gathered from different sensors. In particular, we investigate a major data fusion technique, Bayesian network, and present a simulation tool for a "smart environment". In addition, we discuss the potential impact of data fusion on the processes of decision or detection, estimation, association, and uncertainty management.