Animated Flight Atlas

For the real FlightAware lovers, Michael Peterson and Jochen Wendel worked on flight atlas in form of a DVD. It features animations of flights over North America. The animations are time-lapses that show flights over 24-hour period. The display times are between 30 and 90 seconds. Animations are sorted after flights, aircrafts, airports, cities corridors and regions.

The location of airplanes was mapped using FlyteTrax. The program updates the position of flights every minute and also allows for the selection of flights based on the aircraft, airline, and the departure or arrival airport. The DVD is accompanied by a 40-page book that explains the North American air traffic control system, Aircraft Situational Display to Industry (ASDI), flight mapping, and air traffic patterns.

Don’t yell “it’s Christmas in June!” too soon… the authors are still looking for a distributor.

Relation to my thesis: Beginning in the 1990s, data on the location of aircraft was made available to the airline industry. The Aircraft Situational Display to Industry (ASDI) system conveys up-to-date information on flights to the airline industry and the public. In 1995 the ASDI information became available on a “need-to-know” basis. Will there be an ASDI for any kind of objects in the future?

TraceME Tracking Module

TraceME is a GPS-based tracking module A GPRS/GSM modem handles communications that enables remote tracking. In areas with no GPRS/GSM coverage, position data and events are stored in memory for transmission when a network is available. A 640×480 resolution camera as well as a solar power supply can extend the module.

Relation to my thesis: Example of remote tracking module. I like that fact that the constraints have been taken into consideration. If there is not GPRS, then SMS is used as fail-over. In case of no GSM, data are stored and then probably sent later.

The Reliability of RFID

In extension to my discussions on the technical and physical limitations of RFID with Timo Arnall and Laurent Sciboz back at the second blogject workshop, I stumbled on silicon.com’s article Challenges for the smart tags outweigh the promise… that mentions:

“The reliability of chips is a dirty secret that is finally getting attention. Tags are only functioning at 80 per cent success rates,” she says, adding that antennas sometimes separate from their tags, and that even when the tags stay intact, tag readers are not always reliable. She cites the inability to read tags through metal or liquids (think of all those metal clothes racks in retail outlets) and interference from nylon conveyor belts.

Relation to my thesis: Keeping track of the limitation of ubiquitous technologies, in order to design with them

Improving Location-Aware Applications Through Reinforcement Learning

For my doctoral school course on the priniciples of Artificial Intelligence Problem Solving taught by Hector Geffner, R. Dechter and Andrew Barto, I wrote a paper on the use of Reinforcement Learning techniques to design adaptive location-aware applications:

Improving Location-Aware Applications Through Reinforcement Learning

Abstract. Reinforcement learning (RL) addresses the question of how an autonomous agent that senses and acts in its environment can learn to choose optimal actions to achieve its goals. Each time the agent performs an action in its environment, a trainer may provide a reward or penalty to indicate the desirability of the resulting state. In this paper, I suggest an approach to establish the ability to use RL to construct location-aware systems adapting to user’s expectations in terms of location quality and timeliness.

Relation to my thesis: Machine Learning models could be helpful to design context-aware systems that adapt the location information they deliver to the users (not only adapting the interface) according to the expectations and the environment.

The Geospatial Web, State of the Art and Implications on Web Search

For my doctoral school course on Information Retrieval taught by Ricardo Baeza-Yates, I wrote a paper on the geospatial web entitled:

The Geospatial Web, State of the Art and Implications on Web Search

Abstract. The world-wide web has become virtually ubiquitous. We assume we can learn anything about everything. Moving towards a post-PC era filled with mobile services, massively radio-frequency identifiable objects and wide wireless network coverage we expect the web to be everywhere. Indeed, the physical world is being tagged with location-coordinates and accessible for the asking on the web. This combination of digitalized physical information with web-wide hypermedia (web pages, video, audio, …) has been coined as the Geospatial web. “What is that monument?” and “where is my dog?” belong to the new set of “geo-localized” search queries. This paper covers the state of the art of the geospatial web and discusses the implications on web searching.

Design for Emergent Behaviour with Agents

Related to my thoughts on Wireless Mobile Agents and the Internet of Things, O’Reilly’s Paul Browne suggests that a agent computing might be a paradigm to replace OO programming. He mentions Cougaar for creating and managing agents.

Relation to my thesis: Ubicomp infrastructures are centrally controlled. Some could be designed for the emergence of behaviours as agent programming naturally suggests.

GlobalSat BT338

I got myself a GlobalSat BT338 with a built-in SiRFIII to make some experiments over the summer including:

• GSM antenna mapping for GSM positioning
• Calculate my GPS availability in my daily urban life
• Calculate my GPS availability while I am mobile
• Calculate the accuracy of the GPS signal while I am mobile
• In train tests for the locosound project

My GPS trace in the city

The lovely urban canyons of my neighborood (I walked on a perfectly straight street)

Straight line at the open sky, old seeport.

Dust Network for Real-time Predictive Traffic Information

The San Jose Mercury News has an article on Inrix which is a company that offers US nationwide traffic information based on the GPS data collected by third-party companies fleet vehicles rather than road sensors. They claim to track more that 500,000 probe floating cars.

Relation to my thesis: Inrix mentions that “since the accuracy, timeliness and thoroughness of traffic data is the key to its usefulness”

Learning Significant Locations and Predicting User Movement with GPS

Ashbrook, D. 2002. Learning Significant Locations and Predicting User Movement with GPS. In Proceedings of the 6th IEEE international Symposium on Wearable Computers (October 07 – 10, 2002). ISWC. IEEE Computer Society, Washington, DC, 101.

This paper investigates the creation of a predictive model of the user’s future movements, based on the clustering of GPS data and their incorporation into a Markov model that can be consulted for use with a variety of location-aware applications.

The contribution of this authors lies in the determination of users’ significant places (clustering) and predicting the next move (Markov model). One limitation of this approach is that changes in schedule may take a long time to be reflected in the model.

They mention problems in using GPS:

While in many respects GPS is an ideal sensor, some problems were encountered. Although Selective Availability has been turned off, the accuracy of our GPS receiver was 15 meters; this means that the same physical location will have a different GPS coordinate from day to day.

Relation to my thesis: In their litterature review, they mention some methods for inferring location based on GPS traces (start and end points of trips, building detection) that are close to the issues of a project I am involved in:

In their investigations of automatic travel diaries [16], Wolf et. al. used stopping time to mark the starting and ending points of trips. In their work on the comMotion system [10], Marmasse and Schmandt used loss of GPS signals to detect buildings. When the GPS signal was lost and then later re–acquired within a certain radius, comMotion considered this to be indicative of a building. This approach avoided false detection of buildings when passing through urban canyons or suffering from hardware issues such as battery loss.

[10] Natalia Marmasse and Chris Schmandt. Location–aware information delivery with ComMotion. In HUC, pages 157–171, 2000.

[16] Jean Wolf, Randall Guensler, and William Bachman. Elimination of the travel diary: An experiment to derive trip purpose from GPS travel data. In Notes from Transportation Research Board, 80th annual meeting, Washington, D.C., 2001.

This paper does not mention the many issues the prediction and inferences could create on the user level. However, their method could be interesting for post-game analysis of users intentions in CatchBob!

Naive Geography

Egenhofer, M.J. and Mark, D.M. Naive Geography. in Frank, A.U. and Kuhn, W. eds. Spatial Information Theory: A Theoretical Basis for GIS, Springer-Verlag, Berlin, 1995, 115.

A paper about the commen-sense way people infer information about geographic space and time. It defins the notion and concepts of Naive Geography. Naive Geography is the body of knowledge that people have about the surrounding geographic world. The authors focus on common-sense reasoning about geographic space and time, also called geographic reasoning. This is not to be confused with fuzzy reasoning, which is frequently applied to dealing with imprecise information.

People explore geographic space by navigating in it, and they conceptualize it from multiple views, which are mentally put together like a jigsaw puzzle. Therefore Naive geographic reasoning may actually contain “error” and will occasionally be inconsistent. The aim of the authors is to incorporate people’s concepts about space and time to mimic human thinking.

The framework for developing Naive Geography consists of two different research methodologies:

• The development of formalism of naive geographic models fro particualr tasks or sub-problems so that programmers can implement simulations on computers
• testing and analyzing of formal models to assess how closely the formalizations match human performance.

Examples of Naive Geography are:

• The earth is flat: Trans-Atlantic air travelers often ask why the flight path goes all the way up over Greenland, rather than going straight across
• Geographic information is frequently incomplete: people complete the information intelligently or by applying default rules, frequently based on common sense
• Topoloy matter, metric refines
• Distances are asymetric: in naive geograph space, this premise is frequently violated. Distance are not only thought of a lengths of paths on the Earth’s surface, but frequently seen as a measure for how long it takes to get from one place to another

Relation to my thesis: 2 interesting concepts are geographical reasoning and fuzzy reasoning. In the first case, our reasoning of space my be contrary to objective observations made by sensor in the real, physical world. I suppose the second could be applied to our reasoning with bad geospatial information. More on that isn L. A. Zadeh, “Fuzzy Logic and its application to approximate reasoning,” Information Processing 74, pp. 591–594, 1974. Other related concept is geospatial semantics, that is the study of how humans perceive geographical concepts in their everyday life, and how to exploit this understanding to create useful computing systems to increase our productivity

Incorporating user’s “naive” concept about space and time into location-aware system is an interesting underlying issue. A design goal could be to deal with (or supporting?) user’s naive geographical knowledge in location-aware systems in case of incomplete geographic information.