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.

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In this paper, we present an efficient routing algorithm, Plankton, for Delay/Disruptive Tolerant Network (DTN). Plankton utilizes replica control to reduce overhead and contact probability estimates to improve performance. Plankton has two major features. First, it uses a combination of both short-term bursty contacts and long-term association based statistics for contact prediction. Second, it dynamically adjusts replication quotas based on estimated contact probabilities and delivery probabilities.Our evaluation on extensive traces shows that Plankton achieves significantly better prediction accuracy than existing algorithms for contact probability prediction. In addition, we show that while Plankton incurs much lower communication overhead compared to Spray-and-Wait, MaxProp and RAPID with savings from 14% to 88%, it can also achieve similar if not better delivery ratios and latencies.Index Terms-delay/disruptive tolerant network (DTN) routing, contact prediction, replication control