To serve us well, emerging mobile computing applications will need to know the physical location of things so that they can record them and report them to us: What lab bench was I standing by when I prepared these tissue samples? How should our search-and-rescue team move to quickly locate all the avalanche victims? Can I automatically display this stock devaluation chart on the large screen I am standing next to? Researchers are working to meet these and similar needs by developing systems and technologies that automatically locate people, equipment, and other tangibles. Indeed, many systems over the years have addressed the problem of automatic location-sensing. Because each approach solves a slightly different problem or supports different applications, they vary in many parameters, such as the physical phenomena used for location determination, the form factor of the sensing apparatus, power requirements, infrastructure versus portable elements, and resolution in time and space. To make sense of this domain, we have developed a taxonomy to help developers of location-aware applications better evaluate their options when choosing a location-sensing system. The taxonomy may also aid researchers in identifying opportunities for new location-sensing techniques. 1 Location System Properties A broad set of issues arises when we discuss and classify location system implementations. These issues are generally independent of the technologies or techniques a system uses. Although certainly not all orthogonal, nor equally applicable to every system, the classification axes we present do form a reasonable approach to characterizing or evaluating location systems.

Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning-a requirement for wide-scale adoption of location-aware computing-has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons' positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-40 meter median accuracy with nearly 100% coverage measured by availability in people's daily lives.

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Abstract. We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.

We analyze an architecture based on mobility to address the problem of energy efficient data collection in a sensor network. Our approach exploits mobile nodes present in the sensor field as forwarding agents. As a mobile node moves in close proximity to sensors, data is transferred to the mobile node for later depositing at the destination. We present an analytical model to understand the key performance metrics such as data transfer, latency to the destination, and power. Parameters for our model include: sensor buffer size, data generation rate, radio characteristics, and mobility patterns of mobile nodes. Through simulation we verify our model and show that our approach can provide substantial savings in energy as compared to the traditional ad-hoc network approach.

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