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Accelerometer is the predominant sensor used for lowpower context detection on smartphones. Although lowpower, accelerometer is orientation and position-dependent, requires a high sampling rate, and subsequently complex processing and training to achieve good accuracy. We present an alternative approach for context detection using only the smartphone's barometer, a relatively new sensor now present in an increasing number of devices. The barometer is independent of phone position and orientation. Using a low sampling rate of 1 Hz, and simple processing based on intuitive logic, we demonstrate that it is possible to use the barometer for detecting the basic user activities of IDLE, WALKING, and VEHICLE at extremely lowpower. We evaluate our approach using 47 hours of realworld transportation traces from 3 countries and 13 individuals, as well as more than 900 km of elevation data pulled from Google Maps from 5 cities, comparing power and accuracy to Google's accelerometer-based Activity Recognition algorithm, and to Future Urban Mobility Survey's (FMS) GPS-accelerometer server-based application. Our barometer-based approach uses 32 mW lower power compared to Google, and has comparable accuracy to both Google and FMS. This is the first paper that uses only the barometer for context detection.

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.

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With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the crowd. Ideally, a crowd counting solution should be easy to deploy, scalable, energy efficient, be minimally intrusive to the user and reasonably accurate. Existing solutions using data communication or RFID do not meet these criteria. In this paper, we propose a crowd counting solution based on audio tones, leveraging the microphones and speaker phones that are commonly available on most phones, tackling all the above criteria. We have implemented our solution on 25 Android phones and run several experiments at a bus stop, aboard a bus, within a cafeteria and a classroom. Experimental evaluations show that we are able to achieve up to 90% accuracy and consume 81% less energy than the WiFi interface in idle mode.