Radio Frequency Identification (RFID) is a wireless technology which can efficiently track various items within certain proximity. It has the potential to become a great asset across many applications such as a tracking inventory within a warehouse and the ability to track medical utensils within a hospital environment. Unfortutely, there are several problems that hinder the wide scale adoption of RFID technology including the serious threat of missed readings. Current state-of-the-art methodologies which attempt to solve the problem of false negatives can still not effectively restore the data set completely. In this paper, we propose an architecture that utilises a fusion of both intelligent data alysis of the observatiol records and a non-monotonic reasoning engine designed to determine the most likely values to restore. We then perform an alysis upon our methodology in which we discuss the adoption of our application.
Fifth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2009)
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