CMS - CISUC - Universidade de Coimbra

(c) Carlos Lisboa Bento, Ubiquitous Computing 1

CMS - CISUC - Universidade de Coimbra

Carlos Lisboa BentoUbiquitous Computing

Avances Tecnológicos para la Sociedad Digital

UEM April 14-18

(c) Carlos Lisboa Bento, Ubiquitous Computing

Ubiquitous Computing

Mark Weiser(1952-1999)

Weiser’s vision

"Ubiquitous computing names the third wave in computing, just now beginning. First were mainframes, each shared by lots of people. Now we are in the personal computing era, person and machine staring uneasily at each other across the desktop. Next comes ubiquitous computing, or the age of calm technology, when technology recedes into the background of our lives.”

Mark Weiser,"The Computer for the Twenty-First Century," Scientific American, pp. 94-10, September 1991.

Site dedicated to Mark Weiserhttp://www.ubiq.com/hypertext/weiser/UbiHome.html, Jan 07, 2007

The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it”

Mark Weiser, "The Computer for the Twenty-First Century," Scientific American, pp. 94-10, September 1991.

Mark Weiser** studied Computer and Communication Science at the University of Michigan

** 8 years teaching computer science at the University of Maryland

** his seminal work was in the field of ubiquitous computing while leading the computer science laboratory at PARC, which he joined in 1987

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Ubiquitous ComputingGeneral designations

Pervasive computing Common in US, also used by journals in this area: PERVASIVE COMPUTING JOURNAL (IEEE from 2002); PERVASIVE AND MOBILE COMPUTING (Elsevier from 2005)

Ubiquitous ComputingCommon in US and EU, also used by a journal: PERSONAL AND UBIQUITOUS COMPUTING (Springer from 2001)

Ambient IntelligenceCommon in EU projects

Ambient ComputingAlso being increasingly used, namely many times in UbiComp’07

Designations focused on the links between computing and physical objects

Internet of Things

A term used to designate physical world hyperlinks mediated by ubiquitous identifiers (e.g. barcodes, RFIDs)

Physical ComputingAssociated to software and hardware that intermediates the physical analogical world

different designations

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Enabling Technologies

Devices:

• Embedded (PIC, Tini)

• Mobile (PDA, mobile phones, ...)

Ubiquous Interfaces:

• Environment (walls, tables, street corners, ...)

• Electronic devices (microphones, video projectors ...)

Communications

• Wireless (WiFi, Bluetooth, GPRS, GSM, ZigBee, Wibree ...)

Sensors

• Localization (GPS, Ultrasonic, IR, Cell-based, RFID, ...)

• Environmental (Sound, Light, Pressure...)

PIC "Programmable Intelligent Computer".[3]

TINI (for TIny Network Interface) is a complete TCP/IP node with a Java JVM all on a $50 SIMM module produced by

ZigBeespecification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 802.15.4

WibreeWibree[1] is a digital radio technology (intended to become an open standard of wireless communications) designed for ultra low power consumption

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AURA (Carnegie Mellon University)

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UbiComp - Meeting Computing and Physical Spaces


IRS ambient interactionvideo clip

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InfoAssistant video clip

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invisible trainvideo clip

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Perspectives

Human-Computer Interaction

Distributed Computing

Sensor Networks

Complex Systems

Mobile Computing

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Architectures

Artificial Intelligence


Contents

o Context awareness o Multi-sensor data fusiono Security and privacy

o Urban spaceso Intelligent transport systems

o Conclusions

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Contents



o Conclusions

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Context Awarenessdefinitions

Context“ Context is the set of suitable environmental states and settings concerning a user, which are

relevant for a situation sensitive application in the process of adapting the services and information offered to the user.”

Context-awareness“A system is context-aware if it uses context to provide relevant information and/or services to the

user, where relevancy depends on the user‘s task.”

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Context Awareness

Context-awareness for ubiquitous applications (four “W”s Model)

Context

Identity Location Time

WHO WHERE WHEN WHAT

Activity

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Contents

o Context awareness

o Multi-sensor data fusion

o Security and privacy

o Urban spaces

o Intelligent transport systems

o Conclusions

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Multi-sensor data fusion

Humans integrate different sensory information to make inferences on the surrounding environment

definitions

hearing

taste

smell

sight

touch

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Multi-sensor data fusion

“Theory, techniques, and tools for exploiting the synergy in the information acquired from multiple sources: sensors, databases, intelligent sources, humans, etc”

definitions

Goals:o better understanding of some phenomenono induce or enhance system’s intelligence

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Multi-sensor data fusionadvantages

o Improved system performance

o Improved detection, tracking, and identification

o Improved situation assessment and awareness

o Improved robustness

o Sensor redundancy

o Graceful degradation

o Extended spatial and temporal coverage

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Multi-sensor data fusionmodels

generic model abstractionThe Dempster-Shafer theory is a mathematical theory of evidence[1] based on belief functions and plausible reasoning, which is used to combine separate pieces of information (evidence) to calculate the probability of an event. The theory was developed by Arthur P. Dempster and Glenn Shafer.

Sequential Bayesian filtering is a method to estimate the real value of an observed variable that evolves in time. The method is named filtering when we estimate the current value given past observations, smoothing when estimating past value given present and past measures, and prediction when estimating a probable future value.

The Kalman filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurements. It was developed by Rudolf Kalman.

Particle filters, also known as Sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation.They are usually used to estimate Bayesian models and are the sequential ('on-line') analogue of Markov chain Monte Carlo (MCMC) batch methods and are often similar to importance sampling methods. If well designed, particle filters can be much faster than MCMC. They are often an alternative to the Extended Kalman filter (EKF) or Unscented Kalman filter (UKF) with the advantage that, with sufficient samples, they approach the Bayesian optimal estimate, so they can be made more accurate than the EKF or UKF. The approaches can also be combined by using a version of the Kalman filter as a proposal distribution for the particle filter.

Recursive Bayesian estimation is a general probabilistic approach for estimating an unknown probability density function recursively over time using incoming measurements and a mathematical process model. 18


Multi-sensor data fusionarchitectures

Client/server Data meets processing

++ Permits a global view of the objects from the original data++ Proved for real-time problems++ Established computational model

-- Huge volumes of data traveling and processed in real-time-- Difficult to scale

H. Qi, X. Wang, S. S. Iyengar, and K. Chakrabarty, 2001

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Software/Intelligent agentsProcessing meets data

++ reduces network load++ computationally less expensive than c/s architectures++ robust and fault tolerant (e.g. interrupted connectivity)++ better scalability

-- Needs distributed algorithms for DF

H. Qi, X. Wang, S. S. Iyengar, and K. Chakrabarty, 2001

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Carlos Bento, Lição de Síntese


(Semantic) Content-based Publish/SubscribeData+Processing meets the User

++ is an extension to the agent based architecture++ promotes a data on demand approach-- Need of distributed algorithms for DF


Contents

o Context awareness



o Urban spaces


o Conclusions

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Security and Privacydefinitions

Concepts on security and privacy include:

o availabilityo confidentialityo authenticityo integrityo reliability

Incarnations of these concepts depending on the task at hand

Privacy, Security and Trust within the Context of Pervasive Computing (The Springer International Series in Engineering and Computer Science) Philip Robinson, Harald Vogt, Waleed Wagealla (Editors)

availability (disponibilidade) - you guarantee that the service is available

confidentiality (confidencialidade)

authenticity (autenticidade) - you guarantee the author of the information is who is announced

integrity (integridade) - you guarantee that data are not corrupted

reliability (confiabilidade) - you guarantee that there is no disruption of the system although under a certain level of misusage

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Security and Privacydefinitions

Privacy, Security and Trust within the Context of Pervasive Computing (The Springer International Series in Engineering and Computer Science) Philip Robinson, Harald Vogt, Waleed Wagealla (Editors)

Traditional solutions to guarantee privacy:

o privacy policies

o anonymization

o identifier abstraction (e.g. pseudonymization)

o data abstraction (e.g. reducing temporal or spatial accuracy)

o deletion after use

o assigning the data collection task to the application user

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Security and PrivacymodelsPeter Ruppel, Georg Treu, Axel Kuper, Claudia Linnhoff-Popien: Anonymous User

Tracking for Location-Based Community Services. LoCA 2006: 116-133

Two step location obfuscation

Distance preserving obfuscation of two target positions p(e1,t) and p(e2,t) A trace of coordinates (+) and its obfuscated version (x)

with a bigger and a smaller value for r max_loc

Peter Ruppel, Georg Treu, Axel Kuper, Claudia Linnhoff-Popien, 2006

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Location-based community services (LBCSs):

distance-preserving coordinate transformations + pseudonymization


Contents

o Context awareness



o Urban spaces


o Conclusions

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Urban SpacesWikiCity: Real Time Urban EnvironmentsFrancesco Calabrese, Kristian Kloeckl, and Carlo Ratti

Real Time Control System Wikicity: Real-Time Urban Environments

• an entity to be controlled in an uncertain environment

• sensors that acquire info in real time

• intelligence that can evaluate the system against desired performance

• physical actuators that realize the control strategy

• transportation systems; environmental conditions; other aspects in a city;...

• cell phones and GPS receivers, other sensors in the environment

• intelligence system

• traffic lights; inhabitants as complex distributed intelligent actuators...

Fran

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, and

Car

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200

7

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Research

ejakiFrancisco C Pereira ...

urban spaces


Research

ejakiFrancisco C Pereira ...

urban spaces

o An ejaki is a POI (position, designation, vote, comments)

o An ejaki is shared according to a social computing paradigm

o Ejakis can be displayed according to proximity; acceptance; group interests


Challenges

WikiCity / ejaki

urban spaces

o Integration of data from multiple and heterogeneous sources

o Improved algorithms for analysis of GSM signatures and GPS traces (e.g. spacial data mining for classification of relevant events from signature and trace patterns)

o Other algorithms for city dynamics analysis from multiple data

o Improved models on city dynamics

o Automatic acquisition of semantics for locations (from location to place)

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Contents

o Context awareness



o Urban spaces


o Conclusions

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Intelligent Transport Systems

YouTraceFrancisco C Pereira, Nuno Pereira, Hugo Costa, Ana Almeida, Pedro Quaresma, Carlos Bento

Visi

on

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Francisco C Pereira et al. 2007


Visi

on

Gen

eral

Arc

hite

ctur

e

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50mn

(10mn)Intelligent Transport Systems


Filterso Kalman (KF)o Cumulative Displacement Filter (CDF)o Recursive Least Squares (RLS)

So far, the CDF has better behaviour. It includes Kalman filter’s integration of GPS error, but it is not restricted to the “past” of each point. It’s future is also important.

CDF: The new filter proved itself quite efficient in an urban environment with dense network, when used on the output of a standard Bluetooth GPS unit (Sirf III) and before feeding a road reduction filter with positions.

The road reduction filter has since been rewrote and its overall complexity has dropped a lot, since this filter now feeds it with smooth, noise-free positions.

The CDF is meant to work on positions received from the GPS unit (i.e. after the GPS unit's Kalman Filter's works),

Recursive least squares (RLS) algorithm is used in adaptive filters to find the filter coefficients that relate to recursively producing the least squares (minimum of the sum of the absolute squared) of the error signal (difference between the desired and the actual signal). This is contrast to other algorithms that aim to reduce the mean square error. The difference is that RLS filters are dependent on the signals themselves, whereas MSE filters are dependent on their statistics (specifically, the autocorrelation of the input and the cross-correlation of the input and desired signals). If these statistics are known, an MSE filter with fixed co-efficients (i.e., independent of the incoming data) can be built.

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Map Matching (a genetic algorithmic approach):o An individual is a traceo Fitness favours low distance (to base map), correct topology, less gapso Splitting into segments (as in Chawathe 2007) to improve efficencyo Studying improvements in representation and possible GP approach


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Aggregationo Given two or more distinct tracks that should intersect at some pointo Find, in one track (the “base map”) the point of “aggregation”o Find in the other track (the new trace), the first point that didn’t get matched to the first track (“base map”)o Connect those two points with a smooth curve (now using “Hermite” curves)

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UpdateEach GPS trace is used to improve the quality of that road (as quality varies... Care must be taken not to degrade the quality of the map!)o Use Dilution Of Precision (DOP) estimate as a weight for the averageo Take direction and velocity into accounto Take sampling positions into account

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YouTrace (research challenges)

intelligent transport systems


o Mode detection (bike, car, train, bus…)

o Event analysis (classification of trace patterns)

o Multi-modal off-line filtering of GPS traces;

o Off-line map matching of entire traces (as opposed to typical real-time matching to last N points);

o Privacy and security of mobility data;

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Challenges


Contents



o Conclusions

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Events

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Journals

http://www.elsevier.com/wps/find/

journaldescription.cws_home/704220/

description#description

http://www.computer.org/portal/site/pervasive/

http://www.springerlink.com/content/106503/

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Why Spending Time with Research on UbiComp?

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55mn

(5mn)

o Ubiquitous Computing is a new computational paradigm, that touches all areas of computer science

o Ubiquitous Computing community developed important concepts for CS&T like:

o ambient interaction

o fusion between computational and physical spaces

o computational devices as proxies of human beings

o (and if other reasons were not there...) Human central challenges for the XXI century like environmental sustainability and energy efficiency can have valuable contributions from research on Ubiquitous Computing


ReferencesThe following books, book chapters and papers were explicitly used on the preparation of this presentation. Figures and other elements of these materials used along this presentation are referenced by the respective authors and year.

Mark Weiser, 1991. The Computer for the Twenty-First Century. Scientific American, pp. 94-10, September 1991.

Wei Li, 2003. A Service Oriented SIP Infrastructure for Adaptive and Context-Aware Wireless Services. ACM 1-58113-826.

Ted McFadden, Karen Henricksen, Jadwiga Indulska, Peter Mascaro, 2005. Applying a Disciplined Approach to the Development of a Context-Aware Communication Application. Proceedings of the 3rd IEEE Int’l Conf. on Pervasive Computing and Communications (PerCom 2005).

Anders Kofod-Petersen and Jorg Cassens, 2005. Using Activity Theory to Model Context Awareness. IJCAI-05 Workshop on Modeling and Retrieval of Context (MRC-05).

Llinas J, Hall DL, 1998. An Introduction to Multi-Sensor Data Fusion. Proc IEEE Int Sympo Circuit Syst 6:537–54.

Paradis S Roy J, Treurniet W, 1998. Integration of All Data Fusion Levels Using a Blackboard Architecture.In: Proceedings of 3rd Eurofusion Conference October pp.195–202.

H. Qi, X. Wang, S. S. Iyengar, and K. Chakrabarty, 2001, Multisensor Data Fusion in Distributed Sensor Networks Using Mobile Agents, Proc. Intl. Conf. Information Fusion, August 2001, pp. 11--16.

Alex Wun, Milenko Petrovic, and Hans-Arno Jacobsen, 2007. A System for Semantic Data Fusion in Sensor Networks. In Proceedings of the Inaugural International Conference on Distributed Event-Based Systems (DEBS'07), June 20-22, 2007 Toronto, Ontario, Canada.

Thomas Strang, 2005. Ubiquitous Computing: Context and Context-Awarenesse. University of Innsbruck, 2005.

Henry Lieua, 2003. Roadmap for the Research, Development and Deployment of Traffic Estimation and Prediction Systems for Real-Time and Off-Line Applications (TREPS, TREPS-P). Travel Management Team Office of Operations RD&T Federal Highway Administration.

Yun Zhang, Pingping Xie, Hui Li, 2007. Data Fusion and Integration for Multi-Resolution Online 3D Environmental Monitoring. ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs.

Batya Friedman, Ian E. Smith, Peter H. Kahn Jr., Sunny Consolvo, Jaina Selawski, 2006. Development of a Privacy Addendum for Open Source Licenses: Value Sensitive Design in Industry. Ubicomp 2006: 194-211.

Peter Ruppel, Georg Treu, Axel Kuper, Claudia Linnhoff-Popien, 2006. Anonymous User Tracking for Location-Based Community Services. LoCA 2006: 116-133

Jonathan Reades, Francesco Calabrese, Andes Sevtsuk, and Carlo Ratti, 2007. Cellular Census: Explorations in Urban Data Collection. In IEEE Pervasive Computing Tansactions, Vol 6, N.3, July-September 2007.

Francesco Calabrese, Kristian Kloeckl, and Carlo Ratti, 2007. Wikicity: Real-Time Urban Environments. In IEEE Pervasive Computing Tansactions, Vol 6, N.3, July-September 2007.

Francisco C Pereira, 2007. YouTrace. Take off 28 April 2007, Coimbra (http://takeoff.ideias3.com/apresentacoes/YouTrace.pdf , last visited Jan 11, 2007)

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Ubiquitous Systems

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Thank you for your time and attention

... that’s time for *questions*

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