Radio Frequency Identification (RFID) middleware is a new class of software which facilitates data and information communication between automatic identification physical layer and enterprise applications. It provides a distributed environment to process the data from tags read by the readers, translates the data where necessary, and routes it to a variety of backend applications using suitable technologies such as Web, Remote and Windows Services. This paper reports different challenges and the corresponding research approach in developing a RFID middleware to provide a seamless environment from the edge of the enterprise network; moving data from the point of transaction to the enterprise systems. Key features of the RFID middleware architecture are encapsulation of communication details, large-scale network management, intelligent data processing and routing, hardware and software interoperability, system integration and system extendibility.To deal with high volume data, WinRFID middleware is supported by novel algorithms and data representation schemes capable of processing large amounts of data, rectifying errors in real-time, identifying patterns, correlating events, reorganizing and scrubbing data and recovering from faults and exceptions.Interoperability involves simultaneous distributed working of receivers/readers and transponders/tags at different frequencies using different protocols, with read/write capabilities, different read rates, and other characteristics as a layer transparent to the applications.Network management involves deployment, initialization and control of receivers and transponders, which can be organized into a hierarchical structure with operational syntax and semantics attached to each or a group of receivers, transponders and concentrators or even the edge computers.
Characterizing mobility or contact patterns in a campus environment is of interest for a variety of reasons. Existing studies of these patterns can be classified into two basic approaches -model based and measurement based. The model based approach involves constructing a mathematical model to generate movement patterns while the measurement based approach measures locations and proximity of wireless devices to infer mobility patterns. In this paper, we take a completely different approach. First we obtain the class schedules and class rosters from a university-wide Intranet learning portal, and use this information to infer contacts made between students. The value of our approach is in the population size involved in the study, where contact patterns among 22341 students are analyzed. This paper presents the characteristics of these contact patterns, and explores how these patterns affect three scenarios. We first look at the characteristics from the DTN perspective, where we study inter-contact time and time distance between pairs of students. Next, we present how these characteristics impact the spread of mobile computer viruses, and show that viruses can spread to virtually the entire student population within a day. Finally, we consider aggregation of information from a large number of mobile, distributed sources, and demonstrate that the contact patterns can be exploited to design efficient aggregation algorithms, in which only a small number of nodes (less than 0.5%) is needed to aggregate a large fraction (over 90%) of the data.
Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.
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