Issue No. 01 - January-March (2010 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2010.10
Eyal de Lara , University of Toronto
Nigel Davies , Lancaster University
Anind Dey , Carnegie Mellon University
Jeffrey Hightower , Intel Labs Seattle
Location sensing and awareness have been exciting research topics in pervasive computing for nearly two decades. In fact, location was arguably one of the formative topics in what has become the broad field of pervasive computing. Early location systems, such as Active Badge infrared location technology, showed the possibilities of using real-time sensing to automate office tasks and share state among colleagues. Several significant location research efforts followed, including RADAR (Wi-Fi location from Microsoft Research pioneering the idea of indoor fingerprinting), ultrasound location in MIT's Cricket project, and Intel's Place Lab project exploring wide-area beacon-based location. In parallel to this sensing work, researchers also explored location use in applications such as local search, tour guides, mobile games, advertising, and mobile commerce. Developers built toolkits to standardize, simplify, and abstract the process of building applications.
Today, location information is in the hands of the masses. Low-cost GPS devices bring computer-assisted navigation to everyone, and many people enjoy associated activities such as geocaching. High-speed cellular networks combined with location enable mobile versions of popular Internet mapping and local search products. Perhaps most important, many programmable mobile phones now offer location capabilities in a clean and functional way, and thus developers can integrate it into a wide variety of applications. Remember all the exciting location applications that pervasive computing researchers conjured up over the last two decades? Many of them are now in the general public's pocket, which is a wonderful success story for the pervasive computing community as a whole.
But location research isn't finished, and today's researchers are looking ahead to the next decade. The success of location has exposed a new set of exciting challenges and opportunities for industrial and academic researchers to sink their teeth into. First, there are always refinements and innovations that make location sensing more robust, accurate, deployable, and developer-friendly. Next, location is increasingly an enabling piece of much more complex activity- and context-aware applications. Using location can help us infer someone's activity (for example, being at a grocery store indicates shopping) and uncover social roles by studying colocation patterns. These activity-aware applications strive to provide complete long-lived services or experiences—for example, making public transit systems simple to use, aiding emergency responders, helping people reach long-term fitness goals, or enhancing social relationships. Finally, a continuing direction for location research is privacy and social implications. The Active Badge project led many observers to express concerns about the emergence of "Big Brother" technology, and this in turn has led to research programs studying privacy issues in capturing and sharing location data.
Motivated by these developments, this special issue of IEEE Pervasive Computing showcases four articles that describe recent research on location technology and services.
In "Location-Aware Tools for Improving Public Transit Usability," Brian Ferris, Kari Watkins, and Alan Borning describe a popular location-aware iPhone application for Seattle's OneBusAway system. It offers real-time bus stop, arrival, and routing information for the metropolitan Seattle transit system tailored to the user's current location. Their system tries to address some key challenges and frustrations everyday transit users face.
In "How to Mitigate Signal Dragging during Wardriving," Jinyoung Han, Jeongkeun Lee, Ted Kwon, Daehyung Jo, Taejoon Ha, and Yanghee Choi focus on a specific technological issue with modern wide-area radio location systems: the signal-dragging effect. Signal dragging occurs when the fingerprints collected by a moving vehicle include cached signal-strength values that don't correspond to the measurement location. This impacts localization systems that rely on Wi-Fi access point or cellular base station signal-strength fingerprints.
In "LOC8: A Location Model and Extensible Framework for Programming with Location," Graeme Stevenson, Juan Ye, Simon Dobson, and Paddy Nixon present a framework for simplifying the development of location-aware applications. The framework includes a space model that supports a range of geometric and relative spatial-positioning descriptions, a sensing model that incorporates uncertainty and allows sensor fusion, and a location-query language that enables positioning, range, spatial-relation, and navigation queries.
In our final article, "Location and Navigation Support for Emergency Responders: A Survey," Carl Fischer and Hans Gellersen survey location and navigation techniques in the context of emergency response in which harsh conditions, darkness, smoke, fire, power outages, water, and noise can prevent a location system from working, and heavy protective clothing, gloves, and facemasks limit standard mobile computer use.
All the articles in this issue were subject to the IEEE Computer Society's usual rigorous peer-review process and were approved by this group of guest editors as well as by Roy Want, as part of his duties as outgoing editor in chief. We hope you enjoy this issue and find the articles stimulating and motivating.
Selected CS articles and columns are also available for free at http://ComputingNow.computer.org.
Anind Dey is an associate professor at Carnegie Mellon University's Human-Computer Interaction Institute. His research interests lie in the intersection of human-computer interaction and ubiquitous computing, including context-aware systems and more usable ubicomp systems. Dey has a PhD in computer science from the Georgia Institute of Technology. Contact him at firstname.lastname@example.org.
Jeffrey Hightower is a senior scientist and engineering manager at Intel Labs Seattle. His research interests include sensors, machine learning, and location technology for ubiquitous computing. He has a PhD in computer science and engineering from the University of Washington. Contact him at email@example.com.
Eyal de Lara is an associate professor at the University of Toronto. His research interests lie in systems-level support for mobile and pervasive computing. He has a PhD in electrical and computer engineering from Rice University. Contact him at firstname.lastname@example.org.
Nigel Davies is a professor in Lancaster University's Computing Department. He has participated actively in the mobile computing research community and was a founding associate editor in chief of this magazine. Davies has a PhD in computer science from Lancaster University. Contact him at email@example.com.