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The user profile is one of the main context-information in a wide range of pervasive computing applications. Modern handheld devices provided with localization capabilities could automatically create a diary of user’s whereabouts and use that information as a surrogate (or a complement) of the user profile. The places we go, in fact, reveal also something about us, for example, two persons can be matched as compatible given the fact they visit the same places. Web-retrieved information, and the temporal patterns with which different places are visited, can be used to automatically define meaningful semantic labels to the visited places. In our work we used geocoding and white-pages Web-services to extract information about a place, and Bayesian networks to classify places on the basis of the time in which they have been visited. In this paper we describe the general idea at the basis of the whereabouts diary, discuss our implementation, and present experimental results. Finally, several applications that can exploit the diary are illustrated.

Adaptation of devices and applications based on contextual information has a great potential to enhance usability and mitigate the increasing complexity of mobile devices. An important topic in context-aware computing is to learn semantic locations and routes of mobile device users. Several batch methods have been proposed to learn these locations. However, such offline methods have very limited usefulness in practice. This paper describes an online adaptive approach to learn user’s semantic locations. The proposed method models user’s GPS data as a mixture of Gaussians, which is updated by an online estimation. The learned Gaussian mixture is then evaluated to determine which components most likely correspond to the important locations based on probabilities. With learned semantic locations, we also propose a minimax criterion to discover user’s frequent transportation routes, which are modeled as sequences of GPS data. Finally, we describe an application of the proposed methods in a cell phone based automatic traffic advisory system.

In cellular networks, the signal pattern reported by a mobile terminal has been the major source for localization. In this paper we show how the signal pattern is affected by the terminal movement such as the speed and the moving direction in CDMA/WCDMA networks. When the mobile terminal is moving, its signal pattern tends to contain more signals from base stations positioned opposite of the terminal’s moving direction than signals from base stations positioned in the forward. We call this phenomenon ”. If the signal dragging prevails, it naturally provides a useful hint for figuring out the movement of a terminal, e.g., direction. We also show that the accuracy of the localization algorithm based on pattern matching varies greatly depending on the terminal movement. Based on these experimental results in commercial networks we suggest the practical data collection procedure, e.g., the war-driving, should consider the terminal movement. Otherwise the use of war-driving data can be harmful.

One of the most important performance objectives for a localization system is positional accuracy. It is fundamental and essential to general location-aware services. The radio interferometric positioning (RIP) method [1] is an exciting approach which promises sub-meter positional accuracy. In this work, we would like to enhance the RIP method by dynamically selecting the optimal anchor nodes as beacon senders to further optimizing the positional accuracy when tracking targets. We have developed an estimation error model to predict positional error of the RIP algorithm given different combinations of beacon senders. Building upon this estimation error model, we further devise an adaptive RIP method that selects the optimal sender-pair combination (SPC) according to the locations of targets relative to anchor nodes. We have implemented the adaptive RIP method and conducted experiments in a real sensor network testbed. Experimental results have shown that our adaptive RIP method outperforms the static RIP method in both single-target and multi-target tracking, and improves the average positional accuracy by 47%~60% and reduces the 90% percentile error by 55%~61%.

Many context- and location-aware applications request high accuracy and availability of positioning systems. In reality however, knowledge about the current position may be incomplete or inaccurate as a result of, e.g., limited coverage. Often, position data is thus merged from a set of systems, each contributing a piece of position knowledge. Traditional sensor fusion approaches such as Kalman or Particle filters have certain demands concerning the statistical distribution and relation between position and sensor output. Negated position statements (“I’m at home”), cell-based information or external spatial data are difficult to incorporate into existing mechanisms. In this paper, we introduce a new approach to deal with different types of position data which typically appear in context- or location-aware application scenarios.

In this paper, we present a novel indoor location system, called Activity based Location Tracking and Identification (ALTI), which uses a combination of a camera-based tracking system in the environment and mobile devices with motion sensors. In the indoor environment, GPS-based location systems cannot offer precise location information as they fail to find the required number of GPS satellites. Therefore, many indoor location systems are proposed and developed. However, these systems still have the following two issues if they are applied for the public spaces, such as a shopping mall, an underground mall and a station where many general public visit: (1) many of location systems manage location information in centralized severs, but many people do not want to have their locations in public spaces managed by others (privacy issue) and (2) many of location systems need dedicated user devices, we can not expect that all the people in the public spaces carrying such devices (special device issue). ALTI is based on the combination of camera-based tracking system which generates anonymous user’s location information and mobile phone handsets as the user’s devices. ALTI solves the above issues by using mobile phone handsets as the user’s devices for the special device issue and by estimating the user’s location within his/her mobile phone handset assisted by anonymous location information from camera-based tracking system for the first issue. As a results, the ALTI offers fairly good user privacy. This paper describes the detailed mechanism of ALTI and shows the feasibility of it through a preliminary evaluation using the actual visual tracking system and the prototype terminal devices.

This paper presents algorithms, simulations, and empirical results of a system that finds relative tag positions in 3D space using a new approach called “mobile infrastructure.” Mobile infrastructure consists of one or more sensors in a fixed configuration on a mobile platform, and a set of tags affixed to objects or locations in the environment which the users want to localize. It is especially useful in cases where infrastructure is needed only temporarily, such as during installation, calibration, or maintenance. Mobile infrastructure can cover a much larger area than installed infrastructure with the same number of sensors, and is especially useful in cases where localization hardware costs are asymmetric, with expensive sensors and inexpensive tags. The data collected at various positions are combined by a simple “leapfrog” procedure, with constrained optimization to obtain better accuracy. Our system achieves about one foot (0.3 meter) accuracy with 90% confidence in indoor environments.