This survey and taxonomy of location systems for mobile-computing applications describes...

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality. 1.

The popularity of wireless networks has increased in recent years and is becoming a common addition to LANs. In this paper we investigate a novel use for a wireless network based on the IEEE 802.11 standard: inferring the location of a wireless client from signal quality measures. Similar work has been limited to prototype systems that rely on nearest-neighbor techniques to infer location. In this paper, we describe Nibble, a Wi-Fi location service that uses Bayesian networks to infer the location of a device. We explain the general theory behind the system and how to use the system, along with describing our experiences at a university campus building and at a research lab. We also discuss how probabilistic modeling can be applied to a diverse range of applications that use sensor data.

We present a WLAN location determination technique, the Joint Clustering technique, that uses (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.

Abstract. In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget ” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25 % to 89 % depending on the evaluation criteria used 1. 1

The popularity of wireless networks has increased in recent years and is becoming a common addition to LANs. In this paper we investigate a novel use for a wireless network based on the IEEE 802.11 standard: inferring the location of a wireless client from signal quality measures. Similar work has been limited to prototype systems that rely on nearest-neighbor techniques to infer location. In this paper, we describe Nibble, a Wi-Fi location service that uses Bayesian networks to infer the location of a device. We explain the general theory behind the system and how to use the system, along with describing our experiences at a university campus building and at a research lab. We also discuss how probabilistic modeling can be applied to a diverse range of applications that use sensor data.

The location of equipment, people, and other physical things is essential data to many emerging applications. Unfortunately, location data is often not easy to obtain. We have created SpotON to investigate ad-hoc location sensing, a flexible alternative to infrastructure-centric location systems. SpotON tags use received radio signal strength information as an inter-tag distance estimator. In this paper, we describe ad-hoc location sensing and the SpotON approach. We present specific results regarding calibration of the SpotON radios and suggest directions for further research.

Load sensing is a mature and robust technology widely applied in process control. In this paper we consider the use of load sensing in everyday environments as an approach to acquisition of contextual information in ubiquitous computing applications. Since weight is an intrinsic property of all physical objects, load sensing is an intriguing concept on the physical-virtual boundary, enabling the inclusive use of arbitrary objects in ubiquitous applications. In this paper we aim to demonstrate that load sensing is a versatile source of contextual information. Using a series of illustrative experiments we show that using load sensing techniques we can obtain not just weight information, but object position and interaction events on a given surface. We describe the incorporation of load-sensing in the furniture and the floor of a living laboratory environment, and report on a number of applications that use context information derived from load sensing.

To limit the radio map size and the time required to build the radio map, current WLAN location determination systems do not handle small-scale variations. This contributes to most of the estimation errors in the current systems. We propose a general technique, the perturbation technique, to handle the small-scale variations problem. The system uses user history to detect small-scale variations and then perturbs the signal strength vector entries to overcome it. The results obtained show that the accuracy can be increased by more than 8%. Moreover, the worst-case error is enhanced by more than 60%. We also show that the perturbation technique can help in enhancing the accuracy due to temporal variations in case of change in the environment conditions, thus increasing the accuracy of the current WLAN location determination systems beyond their limits.

this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et al. developed goal recognition algorithms using inductive logic programming [90] and version-space algebra [89, 168, 88] in the context of programming by demonstration