Smart Environment Software ReferenceArchitecture

Alejandro Fernandez-Montes Gonzalez, 48808408Qafdez@lsi.us.es

Supervised by Prof. Dr. Juan Antonio Ortega and Prof. Dr. Luis Gonzalez

Project submitted to the Department of Computer Languagesand Systems of the University of Sevilla in partial fulfilment

of the requirements for the degree of Ph.D. in Computer Engineering.(Thesis Project)

Contents

1 Introduction 3

2 Hypothesis 5

3 Related work 7

4 Goals 9

5 Methodology and Work plan 11

6 Conclusions 13

A Curriculum vitae 15A.1 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15A.2 Significant publications . . . . . . . . . . . . . . . . . . . . . . . 15A.3 Prizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

i

ii CONTENTS

List of Figures

4.1 The components of a smart environment . . . . . . . . . . . . . . 10

iii

iv LIST OF FIGURES

List of Tables

v

vi LIST OF TABLES

Acknowledgements

Thanks to all the people are helping me to complete my thesis.

vii

viii LIST OF TABLES

Abstract

Nowadays ubiquitous computing is spreading to all scopes of our lives. Smartenvironments such us homes with automation and control devices, offices full ofcontrol networks to assist workers,or hotels with even more control devices inorder to save energy and satisfy guests preferences.

This project focus on the proposal of a Reference Architecture for devel-oping Smart Applications and deploy them in Smart Environments. The pro-posal consider three main process in the Software Architecture of these appli-cations:a) perception, b) reasoning and c) acting.

We also review a smart application focused on learning user lighting prefer-ence, considering a working field like a standard office.

Smart environment model is presented and a suitable instance of the modelis presented in order to learn these preferences, and suggest the actions thesmart environment should execute to satisfy the user preferences over lightingconditions in its office.

A review of the smart environment and devices setup is done, showing a realconfiguration for test purposes.

Learning machine techniques proposed are fed with a database, so a proposalfor the vectorization of data is described and analyzed.

1

2 LIST OF TABLES

Chapter 1

Introduction

We can define a smart environment as one that is able to acquire and applyknowledge about the environment and its inhabitants in order to improve theirexperience in that environment [17].

Smart home technologies are often included as a part of ubiquitous comput-ing. Mark Weiser [15] outlined some principles to describe Ubiquitous Comput-ing (ubicomp) from which we emphasize that the purpose of a computer is tohelp you 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 [1] was a research project originally developed as a residentialgateway implementation over the OSGi (Open Services Gateway Initiative) ser-vice platform. The experiences obtained from this project allow us to face aeven more challenging project, the proposal of a general software ReferenceArchitecture to develop smart applications.

3

4 CHAPTER 1. INTRODUCTION

Chapter 2

Hypothesis

The benefits of the approach are the better understanding of the issues a devel-oper must face developing smart environment solutions. The General SoftwareArchitecture will reduce the costs of the main cycles of software: design, devel-opment, deployment and maintenance.

5

6 CHAPTER 2. HYPOTHESIS

Chapter 3

Related work

There are several proposals of software architecture for developing smart envi-ronment solutions. Most of them has been published in IEEE Pervasive Com-puting journal, indexed in JCR with impact index of 2.293 (October 2008).

In [14] a high-level programming language is proposed for prototyping per-vasive applications called Visual RDK. This language generate a debug appli-cations and a prototype application from the same source. Main advantage ofthis proposal is that context is tightly integrated into the language itself, sodevelopers can attach functionality to locations, persons, or situations insteadof the device.

Even though in [2] focus the problem from other point of view: prototyp-ing of Activity Recognition applications. It features mechanisms for distributedprocessing and supports for mobile and wearable devices. The CRN Toolbox isa tool set specifically optimized for implementing multimodal, distributed activ-ity and context recognition systems running on Posix operating systems. It alsocontains a collection of ready-to-use algorithms (signal processing, pattern clas-sification, and so on). Its implementation is particularly optimized for mobiledevices.

On the other hand, the work of Costa et. al [6] focus the problem from avery high level point of view, summarizing challenges and issues that ubiquitouscomputing applications has to deal with.

7

8 CHAPTER 3. RELATED WORK

Chapter 4

Goals

The main goal of this work is the proposal of a Reference Architecture to de-velop smart environments where all the components of a smart environment caninteract flawlessly and reach automatism objectives.

Typical components of a smart environment are widely studied over theliterature, but we can emphasize the approach of D.J. Cook, and S.K. Dasat [4]. Figure 4 on page 4 shows a general organization of these components.These are divided in four layers: a) physical, b) communication, c) informationand d) decision. This approach joins hardware with software agents, so veryheterogeneous elements appear in the same component model such as a decisionmaker and sensors or actuators.

All these components must collaborate to achieve the goals of automatismthat a smart environment is required. This is the main motivation of currentwork, so a Reference (software) architecture is proposed.

9

10 CHAPTER 4. GOALS

Figure 4.1: The components of a smart environment

Chapter 5

Methodology and Workplan

We are going to follow a typical engineering methodology:

• studying of current solutions

• proposal and developing of a better solution from current solu-tions.

• measurement, analysis and validation of the solution proposed

so the work will be divided in three phases:

• Analysis state of art. Researching must start from reading current workand test some of the solution proposed by the researching community tofind issues and lacks.

• General Software Architecture Proposal. Proposal of a GeneralSoftware Architecture to develop Smart Environment solutions.

• Develop of a Smart Environment Solution. In order to test thearchitecture proposal, a smart environment application will be developedand deployed.

11

12 CHAPTER 5. METHODOLOGY AND WORK PLAN

Chapter 6

Conclusions

A general software architecture is very necessary to develop smart environ-ment applications, which involves lots of issues difficult to avoid such as a) dis-tributed applications, b) heterogeneous systems, protocols, devices, languagesor c) context-aware applications. This work will divide the development of so-lutions in three main layers, which will organize the tasks of developers anddesigners.

13

14 CHAPTER 6. CONCLUSIONS

Appendix A

Curriculum vitae

A.1 Education

• Master degree in Engineering and Software Technologies. University ofSeville. 2008.

• Computer Science and Software Engineering Degree. University of Seville.2006

A.2 Significant publications

• Smart environment vectorization. Alejandro Fernandez-Montes, Juan An-tonio Ortega, Luis Gonzalez, Juan Antonio Alvarez. KES2008 12th Inter-national Conference on Knowledge-Based and Intelligent Information &Engineering Systems. ISBN 978-3-540-85562-0

• An Orientation Service for Dependant People Based on an Open ServiceArchitecture. Alejandro Fernandez, Juan Antonio Alvarez, Juan AntonioOrtega, Natividad Martınez, Ralf Seepold. USAB 07 - Usability & HCIfor Medicine and Health Care. Graz. Alemania. Paginas: 155-164. ISSN:0302-9743

• Modelling smart homes for prediction algorithms. Alejandro Fernandez-Montes, Juan Antonio Alvarez, Juan Antonio Ortega, Manuel David Cruz,Luis Gonzalez, Francisco Velasco KES2007 11th International Conferenceon Knowledge-Based and Intelligent Information & Engineering Systems.ISSN: 0302-9743

A.3 Prizes

• First prize for the Best Graduate Final Project 2005-2007 of the EscuelaTecnica Superior de Ingenierıa Informatica granted by FIDETIA (Fun-

15

16 APPENDIX A. CURRICULUM VITAE

dacion para la Investigacion y Desarrollo de las Tecnologıas de la Infor-macion en Andalucıa).

Bibliography

[1] Juan A. Alvarez, Manuel D. Cruz, Alejandro Fernandez, Juan A. Ortega,and Jesus Torres. Experiencias en entornos de computacion ubicua me-diante arquitecturas orientadas a servicios. CEDI-JSWeb, pages 167–174,sept 2005.

[2] D. Bannach, P. Lukowicz, and O. Amft. Rapid prototyping of activityrecognition applications. Pervasive Computing, IEEE, 7(2):22–31, April-June 2008.

[3] J. Choi, D. Shin, and D. Shin. Research on Design and Implementation ofthe Artificial Intelligence Agent for Smart Home Based on Support VectorMachine. Lecture notes in computer science.

[4] D.J. Cook and S.K. Das. How smart are our environments? An updatedlook at the state of the art. Pervasive and Mobile Computing, 3(2):53–73,2007.

[5] D.J. Cook, M. Youngblood, and S.K. Das. A multi-agent approach to con-trolling a smart environment. Lecture notes in computer science, 4008:165,2006.

[6] C.A. da Costa, A.C. Yamin, and C.F.R. Geyer. Toward a general soft-ware infrastructure for ubiquitous computing. Pervasive Computing, IEEE,7(1):64–73, Jan.-March 2008.

[7] S.K. Das and D. Cook. Designing Smart Environments: A Paradigm Basedon Learning and Prediction. Mobile, Wireless, and Sensor Networks.

[8] A. Fernandez-Montes, JA Alvarez, JA Ortega, MD Cruz, L. Gonzalez, andF. Velasco. Modeling Smart Homes for Prediction Algorithms. LectureNotes in Computer Science, 4693:26, 2007.

[9] H. Hagras, V. Callaghan, M. Colley, G. Clarke, A. Pounds-Cornish, andH. Duman. Creating an Ambient-Intelligence Environment Using Embed-ded Agents. 2004.

[10] L. Jiang, D.Y. Liu, and B. Yang. Smart home research. Machine Learningand Cybernetics, 2004. Proceedings of 2004 International Conference on,2, 2004.

17

18 BIBLIOGRAPHY

[11] D. Leake, A. Maguitman, and T. Reichherzer. Cases, Context, and Com-fort: Opportunities for Case-Based Reasoning in Smart Homes. DesigningSmart Homes. LNCS (LNAI), 4008:109–131.

[12] J. Li, Y. Bu, S. Chen, X. Tao, and J. Lu. FollowMe: On Researchof Pluggable Infrastructure for Context-Awareness. Proceedings of the20th International Conference on Advanced Information Networking andApplications-Volume 1 (AINA’06)-Volume 01, pages 199–204, 2006.

[13] N. Roy, A. Roy, and S.K. Das. Context-Aware Resource Managementin Multi-Inhabitant Smart Homes: A Nash H-Learning based Approach.Proc. of 4th IEEE Int’l Conf. on Pervasive Computing and Communica-tions (PerCom2006), 2006.

[14] T. Weis, M. Knoll, A. Ulbrich, G. Muhl, and A. Brandle. Rapid prototypingfor pervasive applications. Pervasive Computing, IEEE, 6(2):76–84, April-June 2007.

[15] Mark Weiser. The computer for the 21st century. SIGMOBILE Mob.Comput. Commun. Rev., 3(3):3–11, 1999.

[16] T. Yamazaki. Beyond the Smart Home. Proceedings of the 2006 Inter-national Conference on Hybrid Information Technology-Volume 02, pages350–355, 2006.

[17] G.M. Youngblood, E.O. Heierman, L.B. Holder, and D.J. Cook. Automa-tion intelligence for the smart environment. Proceedings of the InternationalJoint Conference on Artificial Intelligence, 2005.