Jeffrey Hightower

Location systems for ubiquitous computing

This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field. 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.

Bayesian filtering for location estimation

Bayesian-filter techniques provide a powerful statistical tool to help manage measurement uncertainty and perform multisensor fusion and identity estimation. The authors survey Bayes filter implementations and show their application to real-world location-estimation tasks common in pervasive computing.

The Mobile Sensing Platform: An Embedded Activity Recognition System

Activity-aware systems have inspired novel user interfaces and new applications in smart environments, surveillance, emergency response, and military missions. Systems that recognize human activities from body-worn sensors can further open the door to a world of healthcare applications, such as fitness monitoring, eldercare support, long-term preventive and chronic care, and cognitive assistance. Wearable systems have the advantage of being with the user continuously. So, for example, a fitness application could use real-time activity information to encourage users to perform opportunistic activities. Furthermore, the general public is more likely to accept such activity recognition systems because they are usually easy to turn off or remove.

Learning and recognizing the places we go

Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someones personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.

Mobility detection using everyday GSM traces

Recognition of everyday physical activities is difficult due to the challenges of building informative, yet unobtrusive sensors. The most widely deployed and used mobile computing device today is the mobile phone, which presents an obvious candidate for recognizing activities. This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors over a period of one month, yielding an overall average accuracy of 85%, and a daily step count number that reasonably approximates the numbers determined by several commercial pedometers.

The location stack: a layered model for location in ubiquitous computing

Based on five design principles extracted from a survey of location systems, we present the location stack, a layered software engineering model for location in ubiquitous computing. Our model is similar in spirit to the seven-layer Open System Interconnect (OSI) model for computer networks. We map two existing ubiquitous computing systems to the model to illustrate the leverage the location stack provides. By encouraging system designers to think of their applications in this way, we hope to drive location-based computing toward a common vocabulary and standard infrastructure, permitting members of the ubiquitous computing community to easily evaluate and build on each others work.

Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study

Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has shown to be effective for tracking robots’ positions. This paper presents a case study of applying particle filters to location estimation for ubiquitous computing. Using trace logs from a deployed multi-sensor location system, we show that particle filters can be as accurate as common deterministic algorithms. We also present performance results showing it is practical to run particle filters on devices ranging from high-end servers to handhelds. Finally, we discuss the general advantages of using probabilistic methods in location systems for ubiquitous computing, including the ability to fuse data from different sensor types and to provide probability distributions to higher-level services and applications. Based on this case study, we conclude that particle filters are a good choice to implement location estimation for ubiquitous computing.

GSM indoor localization

Accurate indoor localization has long been an objective of the ubiquitous computing research community, and numerous indoor localization solutions based on 802.11, Bluetooth, ultrasound and infrared technologies have been proposed. This paper presents the first accurate GSM indoor localization system that achieves median within floor accuracy of 4 m in large buildings and is able to identify the floor correctly in up to 60% of the cases and is within 2 floors in up to 98% of the cases in tall multi-floor buildings. We report evaluation results of two case studies conducted over a course of several years, with data collected from 6 buildings in 3 cities across North America. The key idea that makes accurate GSM-based indoor localization possible is the use of wide signal-strength fingerprints. In addition to the 6-strongest cells traditionally used in the GSM standard, the wide fingerprint includes readings from additional cells that are strong enough to be detected, but are too weak to be used for efficient communication. We further show that selecting a subset of highly relevant channels for fingerprinting matching out of all available channels, further improves the localization accuracy.

Next century challenges: data-centric networking for invisible computing: the Portolano project at the University of Washington

Computing and telecommunications are maturing, and the next century promises a shift away from technology-driven general-purpose devices. Instead, we will focus on the needs of consumers: easy-to-use, low-maintenance, portable, ubiquitous, and ultra-reliable task-specific devices. Such devices, although not as limited by computational speed or communication bandwidth, will instead be constrained by new limits on size, form-factor, and power consumption. Data that they generate will need to be injected into the Internet and find its way to the services to which the user has subscribed. This is not simply a problem of ad-hoc networking, but one that requires re-thinking our basic assumptions regarding network transactions and challenges us to develop entirely new models for distributed services. Network topologies will be intermittent and services will have to be discovered independently of user guidance. In fact, data transfers from user interfaces to services and back, will need to become invisible to the user and guided by the task rather than explicit commands. This paper outlines a vision of this future and identifies research problems that will require our attention in the areas of user interfaces, distributed services, and networking infrastructure.

Are GSM Phones THE Solution for Localization

In this paper, we argue that localization solution based on cellular phone technology, specifically GSM phones, is a sufficient and attractive option in terms of coverage and accuracy for a wide range of indoor, outdoor, and placebased location-aware applications. We present preliminary results that indicate that GSM-based localization systems have the potential to detect the places that people visit in their everyday lives, and can achieve median localization accuracies of 5 and 75 meters for indoor and outdoor environments, respectively.