The automation of smart environment systems is one of the
main goals of smart home researching. This paper focus on learning user
lighting preference, considering a working field like a standard office. A
review of the smart environment and devices setup is done, showing a
real configuration for test purposes. Suitable learning machine techniques
are exposed in order to learn these preferences, and suggest the actions
the smart environment should execute to satisfy the user preferences.
Learning machine techniques proposed are fed with a database, so a
proposal for the vectorization of data is described and analyzed.Ministerio de Educación y Ciencia TSI2006-13390-C02-02Junta de Andalucía TIC-214

Cruz, Manuel David

Fernández Montes González, Alejandro

González Abril, Luis

Ortega Ramírez, Juan Antonio

Álvarez García, Juan Antonio

English

idUS. Depósito de Investigación Universidad de Sevilla

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/259632396Smart environment vectorization - An approach tolearning of user lighting preferencesArticle  in  Lecture Notes in Computer Science · January 2008CITATIONS2READS1415 authors, including:Some of the authors of this publication are also working on these related projects:Multiclass SVM View projectVision and Crowdsensing Technology for an Optimal Response in Physical-Security (TIN2017-82113-C2-1-R) ViewprojectAlejandro Fernández-MontesUniversidad de Sevilla50 PUBLICATIONS   305 CITATIONS   SEE PROFILEJuan A. OrtegaUniversidad de Sevilla190 PUBLICATIONS   911 CITATIONS   SEE PROFILELuis Gonzalez-AbrilUniversidad de Sevilla247 PUBLICATIONS   1,195 CITATIONS   SEE PROFILEJuan Antonio Alvarez-GarciaUniversidad de Sevilla75 PUBLICATIONS   630 CITATIONS   SEE PROFILEAll content following this page was uploaded by Alejandro Fernández-Montes on 14 July 2015.The user has requested enhancement of the downloaded file.Smart Environment VectorizationAn Approach to Learning of User Lighting PreferencesAlejandro Fernández-Montes1, Juan A. Ortega1,Luis González2, Juan A. Álvarez1, and Manuel D. Cruz11 Department of Computer Science, University of Sevilla, Sevilla, Spain{afernandez,ortega}@lsi.us.es, {jaalvarez,mancrudia}@us.es2 Department of Applied Economics, University of Sevilla, Sevilla, Spainluisgon@us.esAbstract. The automation of smart environment systems is one of themain goals of smart home researching. This paper focus on learning userlighting preference, considering a working field like a standard office. Areview of the smart environment and devices setup is done, showing areal configuration for test purposes. Suitable learning machine techniquesare exposed in order to learn these preferences, and suggest the actionsthe smart environment should execute to satisfy the user preferences.Learning machine techniques proposed are fed with a database, so aproposal for the vectorization of data is described and analyzed.1 IntroductionSmart home technologies are often included as a part of ubiquitous computing.Mark Weiser [1] outlined some principles to describe Ubiquitous Computing(ubicomp) from which we emphasize that the purpose of a computer is to helpyou do something else.Home technologies have tried to help home inhabitants since its creation.Nowadays, due to the popularization of computational devices, ubiquitous com-puting is called to be the revolution to develop smart systems with artificialintelligence techniques.Domoweb [2] was a research project originally developed as a residential gate-way implementation over the OSGi (Open Services Gateway Initiative) serviceplatform. Nowadays Domoweb conform a great platform where researchers fromdifferent disciplines converges and where we can deploy, develop and test smarthome related solutions, due to the component based model, and the serviceoriented architecture that Domoweb and OSGi supports.This article focuses on modeling smart spaces to apply machine learning tech-niques. We have focused in the learning of user preferences for the lighting ofa space. In order to interact with the space and retrieve these preferences, anoffice at the department of Computer Languages of the University of Seville hasbeen provided of several devices to accomplish these tasks.Artificial intelligent methods can be supported by this model like machinelearning algorithms where is centered this article. Some techniques are presentedI. Lovrek, R.J. Howlett, and L.C. Jain (Eds.): KES 2008, Part I, LNAI 5177, pp. 765–772, 2008.c© Springer-Verlag Berlin Heidelberg 2008766 A. Fernández-Montes et al.to accomplish this goal in section 3. Finally we propose some expansions whichcould be studied in order to cover other cases.2 Experimental EnvironmentsTwo different environments are used for data collection. The first one is a sim-ulated environment developed in Java that allows researchers to generate simu-lated and synthetic databases.Second one is a standard office at the department of Computer Languagesis used for data collection during the experiments. This office is intended to beused by a single person, who could be eventually visited by other work mates orstudents. It is illuminated by natural light from the window and four artificialfluorescent lights which can act as a complement of the natural light or likeunique source of light.Figure 1 shows the distribution and setup of the room.As you can see, the orientation of the window is south, so it maximizes thequantity of light that receives during a day. This fact must be considered whenanalyzing results.m5,43,5mm3,4Sentilla Tmote 1(outdoor ) Sentilla Tmote  2 (indoor)NBlind engineFluorescent lights)01X(Motion Sensor )01X(Fig. 1. Room setupSmart Environment Vectorization 7673 Related DevicesIn this section we present the devices which will interact in the setup proposed.The concrete model and manufacturer of the devices are detailed although thelearning system should be independent of these details.3.1 Sentilla TmoteSentilla Tmotes are the devices which detects the quantity of light. In this setupwe propose to install one mote indoor, and another outdoor. This way we cancompare the preferences of the user indoor with the quantity of light outdoor.We’ll be able to deduct some weather parameters in real time too. The dimen-sions are 8 cms. of width and 3.2 cm. of height, so it is quite small to suitubiquitous applications and non intrusive systems. These devices also imple-ment a humidity and temperature sensor, which could be requested for futureimprovement and expansions. The figure 2 shows the Sentilla Tmote module:The connectivity with other motes and computers is done through the IEEE802.15.4 (ZigBee) protocol, which minimizes battery consumption. Zigbee sup-ports mesh networking so this is the topology we will adopt and this way the motescan create a network to share information and forward it to reach wider areas.Nowadays Sentilla Tmotes are packaged in a beta development kit, includingan IDE based on Eclipse 3.2 for developing. The hardware implements a JavaRuntime Environment which can run different applications to retrieve, processand send information from sensors. Fig. 2. Front of the Tmote module768 A. Fernández-Montes et al.3.2 Motion SensorA motion sensor is indispensable to determine when a user is at the room. Thiscould act as a trigger of the learning algorithm to retrieve, process and send theinformation from sensors.Domoweb project implemented an OSGi platform with software componentsto interact with X10 devices. The figure 3 shows the MS13A, a wireless devicewhich interacts with a gateway that routes wireless messages over the electricalcable using X10 protocol. Notice that, although X10 protocol is an old-fashionedtechnology, it carries out its purpose perfectly.Fig. 3. Front of the MS13A. X10 Wireless Motion Sensor.3.3 Fluorescent LightThese lights are activated with an X10 actuator, like the Appliance moduleAM486 in order to determine when a light has been switched on and off, and itscurrent state.3.4 Blind EngineLighting preferences are directly related with the quantity of light that goesthrough the window. Therefore the state of the blind or curtain will affect thelighting of the room. Some devices are under study but at the moment of writ-ing this article none satisfied our requirements of wirelessly communication andstandardized protocols.4 Framework of LearningAs exposed before, our goal is the learning of the lighting preferences of a singleuser. This machine learning is done over the statistical data retrieved at theSmart Environment Vectorization 769environment shown in sections 2 and 3. In general, we will retrieve a set of dataX called inputX = {x1, x2, . . . , xn}and we will have a set of data Y called output.Y = {y1, y2, . . . , ym}The process of learning tries to search a functional dependence between bothsets of data. The framework is based on V.N. Vapnik [3] model of learning withexamples. The model is composed by three elements as shown in the figure 4.GData GeneratorSObjectiveLMLearning machinexi y iy i*Fig. 4. Learning model1. Data generator. Samples of X , retrieved by the infrastructure pro-posed.2. Objective (aka supervisor). User preferences.3. LM. Learning machine.The learning can be carried out due to the dependence of the user prefer-ences with his habits. Normally we have the same lighting preferences. Thesepreferences must be learned at every environment, due to its dependence withthe location, orientation of the window, devices setup and so on.4.1 TechniquesTwo families of algorithms, related with learning machine, can be consideredalthough both are going to be supervised.Support vector machine (SVM) and Neural networks (NN) are the selectedtechniques to learn user lighting preferences. The main advantage of these tech-niques is that always offer an output. On the other hand it is hard to interpretor understand their outputs which is an important feature that prediction algo-rithms should implement as expounded in [4].770 A. Fernández-Montes et al.The other family of algorithms considered is the machine learning techniquesbased on rules. These algorithms provide an output easier to understand andinterpret, but their main disadvantage is that if no rule matches current state,these algorithms don’t offer an output.4.2 Input and OutputTable 1 shows the input variables X proposed and two sample input data:Table 1. Input sampleOutdoor Indoor Indoor Blind Action Actionlighting lighting light state Motion over over Threshold[0, 1] [0, 1] state [0, 1] {0,1} light blind [0, 1]{0,1} {-1,0,1} [−1, 1]0.9 0 0 0 1 0 0 0.50.9 0.7 1 0.5 1 1 0.5 0.5– Outdoor lighting. This variable represents the quantity of light receivedfrom the outdoor Sentilla Tmote sensor. Continuous variable from 0 to 1.– Indoor lighting. This variable represents the quantity of light received fromthe indoor Sentilla Tmote sensor. Continuous variable from 0 to 1.– Indoor light state. This variable represents the state of the lights of theroom received from the X10 appliance module. Discrete variable, 0 for lightsoff, and 1 for lights on.– Blind state. This variable represents the state of the blind or curtains.Continuous variable from 0 to 1. 0 for totally closed and 1 for totally open.– Motion. This variable represents the detection of motion sent by the MS13AX10 device. Discrete variable, 0 for no motion, 1 for motion detected.– Action over light. This variable represents the action done by the userover indoor light. Discrete variable, -1 lights switched off, 0 no action, +1lights switched on.– Action over blind. This variable represents the action done by the userover the blind/curtains. Continuous variable, -1 means the user closed ittotally, 0 no action, +1 means the user opened it totally.– Threshold. Represents the current user lighting preference. 0 representsminimum room lighting, 1 represents maximum room lighting.Notice that Sentilla Tmotes offer the quantity of light received in luxes. We haveto standarize this data to a [0, 1] interval.Table 2 shows the output variables Y proposed and two sample output data:– Action over light. Represents the action over indoor light predicted bythe learning machine in order to satisfy user lighting preferences. Discretevariable, -1 lights switched off, 0 no action, +1 lights switched on.Smart Environment Vectorization 771Table 2. Output sampleAction over Action over Action overlight blind threshold{0,1} [−1, 1] [−1, 1]0 0 00 0 α– Action over blind. Represents the action over the blind/curtains predictedby the learning machine in order to satisfy user lighting preferences. Contin-uous variable, -1 means the machine closed it totally, 0 no action, +1 meansthe machine opened it totally.– Action over threshold. Represents the correction the machine must donein order to adapt current user threshold. Negative values reduce threshold,0 represents no action, and positive values represent an increase correctionover threshold. The value α of the correction must be determined with care,in order to avoid infinity jumps around user preference, and converge to thereal user preference.5 Future WorkComparative results must be done between machine learning techniques pro-posed in section 4.1. Next step should focus in enlarge action field, to otherrooms with different users, locations, orientations, and so on. This way we couldcompare results obtained with these techniques in different (but similar) envi-ronments, and create a wider motes mesh network.Other field of action could be applying these techniques and algorithm to learnuser preference over conditioning. Sentilla Tmote devices also include sensors toretrieve temperature and humidity, useful to learn conditioning preferences.Acknowledgements. This research is supported by the MEC I+D project In-Care. Ref: TSI2006-13390-C02-02 and the Andalusian Excellence I+D projectCUBICO Ref: TIC2141.References1. Weiser, M.: The computer for the 21st century. SIGMOBILE Mob. Comput. Com-mun. Rev. 3(3), 3–11 (1999)2. Alvarez, J.A., Cruz, M.D., Fernández, A., Ortega, J.A., Torres, J.: Experienciasen entornos de computación ubicua mediante arquitecturas orientadas a servicios.CEDI-JSWeb, 167–174 (September 2005)3. Vapnik, V.: Statistical learning theory. Wiley, New York (1998)772 A. Fernández-Montes et al.4. Fernández-Montes, A., Álvarez, J.A., Ortega, J., Cruz, M., González, L., Velasco,F.: Modeling Smart Homes for Prediction Algorithms. In: Apolloni, B., Howlett,R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, p. 26. Springer,Heidelberg (2007)5. Cook, D., Youngblood, M., Das, S.: A multi-agent approach to controlling a smartenvironment. In: Augusto, J.C., Nugent, C.D. (eds.) Designing Smart Homes.LNCS (LNAI), vol. 4008, p. 165. Springer, Heidelberg (2006)6. Li, J., Bu, Y., Chen, S., Tao, X., Lu, J.: FollowMe: On Research of PluggableInfrastructure for Context-Awareness. In: Proceedings of the 20th InternationalConference on Advanced Information Networking and Applications (AINA 2006),vol. 01, pp. 199–204 (2006)7. Das, S., Cook, D.: Designing Smart Environments: A Paradigm Based on Learningand Prediction. Mobile, Wireless, And Sensor Networks8. Leake, D., Maguitman, A., Reichherzer, T.: Cases, Context, and Comfort: Opportu-nities for Case-Based Reasoning in Smart Homes. In: Augusto, J.C., Nugent, C.D.(eds.) Designing Smart Homes. LNCS (LNAI), vol. 4008, pp. 109–131. Springer,Heidelberg (2006)9. Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., Duman, H.:Creating an Ambient-Intelligence Environment Using Embedded Agents (2004)10. Roy, N., Roy, A., Das, S.: Context-Aware Resource Management in Multi-Inhabitant Smart Homes: A Nash H-Learning based Approach. In: Proc. of 4thIEEE Int’l Conf. on Pervasive Computing and Communications (PerCom 2006)(2006)11. Choi, J., Shin, D., Shin, D.: Research on Design and Implementation of the Artifi-cial Intelligence Agent for Smart Home Based on Support Vector Machine. LNCS.Springer, Heidelberg12. Yamazaki, T.: Beyond the Smart Home. In: Proceedings of the 2006 InternationalConference on Hybrid Information Technology, vol. 02, pp. 350–355 (2006)13. Jiang, L., Liu, D., Yang, B.: Smart home research. In: Proceedings of 2004 Inter-national Conference on Machine Learning and Cybernetics, vol. 2 (2004)View publication stats

Smart Environment Vectorization : An Approach to Learning of User Lighting Preferences

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Smart Environment Vectorization : An Approach to Learning of User Lighting Preferences

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