Smart home systems using wireless sensor network a comparative analysis


Published on

1 Comment
  • Dear Colleages, We develope the idea od Transforming "Origami" house as the subject of architectural mechatronic for the name of energo efficiently and ecological homes. This is the way haw nature and machine for living can coexist in harmony Please,cheek the calculations ant watch the video (It made by my son Nick,student of Robot Dep SFU) Waiting for your comments Love from Russia Andrew Vidish, Archite
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Smart home systems using wireless sensor network a comparative analysis

  1. 1. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 3, Issue 3, October - December (2012), pp. 94-103 IJCET© IAEME: Impact Factor (2012): 3.9580 (Calculated by GISI) © SMART HOME SYSTEMS USING WIRELESS SENSOR NETWORK – A COMPARATIVE ANALYSIS R. Kavitha Dr. G. M. Nasira Dr. N. Nachamai Research Scholar Assistant Professor Assistant Professor Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Christ University Chikkam Govt. Arts College Christ University Bangalore-29, Karnataka Tirupur, Tamil Nadu 641 602 Bangalore-29, Karnataka India India India ABSTRACT The advances in the field of communication network, Wireless Sensors Network (WSN) is became a very interesting and challenging area of Networks. Smart home system using wireless sensor network technology enrich human life and helps to take care of the very old people easier who lives alone. Smart Home is the integration of technology and services through home networking for better quality of living. The smart home system consists of three components: physical components, control system and communication system. In this paper, basic structure of a smart home system and a comparative analysis of different smart home system with its components are discussed. Key Words: Smart Home, ZigBee, Sensors. 1. INTRODUCTION The average age of people in India is on the rise. New challenges are arising to provide a safe and secure living environment for them. As per the survey done by S. Irudaya Rajan [1], by 2050, the world population will peak to three hundred million. In that population, more would be elder than younger. The situation arises; where elder people live alone without assistance really require constant monitoring. Figure 1 shows the raising percentage of elderly 60 and above from 2001 to 2051. 94
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Percentage of Elderly during the year 2001 - 2051 20 17.3 18 16 14.5 14 11.9 Percentage 12 9.9 10 8.2 Percentage 7.5 8 6 4 2 0 2001 2011 2021 2031 2041 2051 Year Figure 1. Percentage of elderly 60 and above during the year 2001 – 2051The smart home system provides an inviolable, safe sheltered and comfortable life like in anassisted living environment. It enhances traditional security and safety mechanism by usingintelligent monitoring and access control.The structure of this paper is as follows. Section 2 describes basic structure of a wirelesssmart home and its components. In section 3 comparative analysis of different smart homesystem is discussed. The conclusion and future research direction are presented in section 4.2. SMART HOME SYSTEMThe basic structure of a smart home system is depicted in figure 2. The smart homeintegration consists of three major areas, first, the physical components (electronic devices –sensors, actuators), second, the control system (artificial intelligence/expert system) and third,the communication system (wired/wireless network) which connects physical componentsand control system. The control system can access from home exterior through external homenetwork like mobile network or Internet. In a smart home system the physical componentssense the environment and pass to home control system through home sub networks andhome network. Home control system takes the decision and passes the control information tothe actuators through home network. For example, gas sensor detects the gas leakage in asmart home and passes this message to the home control system through ZigBee, a wirelessnetwork. Control system decides to switch off the gas valve and pass this to actuator, whichwill off/close the gas valve. 95
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Home Network Home Sub Network-1 Home Home External Control Network System Home Sub Network-2 Wireless Smart Devices/ Wired/Wireless Network Sensors Network Figure. 2 Basic Structure of a Smart Home System2.1. Physical ComponentsThe role of physical components is very important. It measures and collects the informationand shares with the control system through network. Sensors, microcontroller, actuator andsmart devices are used as physical components. Different applications use different sensors.Table 1 lists the sensing property, sensing mode, and application of the sensors [2], [4].Sensors observe the smart home resident’s interaction with objects such as doors, windows,keys, and all home appliances. It recognizes activity of daily living. Table 1. Sensing property, modes and their applications Sensing property Sensing mode Sensor Applications Pressure, Temperature,Physical Properties Health Safety, Energy Efficiency Light, Humidity, Flow Position, Angular, Velocity,Motion and Presences Acceleration, Direction, Security, Location tracking, Falls detectionproperties Distance Security and health monitoring, PoolBiochemical Agents Solid, Liquids, Gases maintenance, Sprinkler efficiency Used to identify people and objects, VolumeOthers iButtons, Sound, Image control, Speech recognition, Context understanding2.2. Control SystemControl system receives the information from different sensors and classifies it for thedifferent types of activities. For example, the data collected from the accelerometer sensorpositioned on the body recognize actions that involve repetitive body motions like walking,running, etc.A number of machine learning models are used for activity recognition in smart homeapplication [4]. Following are some example. 96
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME • Nave Bayes Classifiers – identify the activity that corresponds with the greatest probability to the set of sensor values observed. • Decision trees - To learn logical description of the activities.The probabilistic sequence of sensor events are encoded using Markov models dynamic,Bayes networks and Conditional random fields. Temporal Reasoning with a rule basedsystem or Neural Network with reinforcement learner or Fuzzy rules develop an automateddecision-making and control techniques. 2.3. Communication SystemThe communication system is use to share the information between physical components andcontrol system in the smart home system. It can be wired or wireless communication. Thewidely used wireless technologies are Bluetooth, WiFi, WiMAX, and ZigBee [5]. Bluetoothis the first and popular low bandwidth wireless interface for the smart home. In the last fewyears the bandwidth requirement of the smart home has increased dramatically whichintroduces WiFi - a wireless local area networks technology based on the IEEE 802.11. Itcovers an entire house, and the data rate is reduced to 1 MB or below at the far distance. Itconsumes more power and provides low security. WiMAX provides wireless broadbandaccess and it is alternative to the cable connection. Due to the low cost, low powerconsumption and easy integration into smart home control system ZigBee a wirelesstechnology become a quite suitable for smart home environment. Comparison of abovementioned four wireless network technologies are shown in Table 2. Table 2. Comparison of wireless technologies Protocol Frequency Power Transmission Rate/bps Security standard band/Hz consumption distanceBluetooth 802.15.1 2.4G 1M >10mW High 10m 802.11b, Wi-Fi 2.4G/5G 11-54M >10mW Low 200m 802.11g WiMAX 802.16 2-11G 70M >10mW Medium 30Km 868/915M, ZigBee 802.15.4 20-250K <10mW High 100m 2.4G3. COMPARATIVE ANALYSIS OF A SMART HOME SYSTEMThe comparative analysis of a smart home system using wireless sensor network is done byclassifying a study into two categories as communication system and control system. Theliterature on the communication system explains more about network part of a smart home.The literature on the control system explains more about the methods and procedures used formonitoring and control process of a control system in a smart home. Table 3 represent theanalysis with two categories as communication system and control system. Thecommunication system is further divided into routing algorithm and network basedcategories. The first category discusses about the routing algorithm, which is used in home(ZigBee) network of a smart home. Second category discusses about implementation of bothinternal and external home network. 97
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Table 3. Comparative Analysis of Smart Home Systems Controlling / Group/ Smart Devices Home Home external Future Objective Control System User GUI Network Parameter /Sensors Used Network Network Enhancement Algorithm Jess / DMPR – Smart Home Energy Generic To support R Information Disjoint 1 management System Sensor, ZigBee Internet - location O Extractor Multipath – SHEMS [6] Actuator service U Routing T Improved I C/S architecture Development of node routing Apply the N based on socket and the coordinator Node, algorithm whole system G 2 ZigBee communication GPRS - for smart home coordinator based on the in practice and mechanism of system [7] Dijkstra test A TCP/IP protocol Algorithm L G Develop a new LQIR – Link Generic O intelligent home Quality To support 3 Sensor, ZigBee - Internet - R control system based Indicator Location Based Actuator I on WSN [8] Based Routing T Proposed an algorithm for H improved routing routing M 4 algorithm - ZigBee - - - - discovery and combining Cluster- maintenance Tree and AODVjr [9] Improving a Monitoring of IR, classification Support Vector Classification 5 Elderly people at Temperature, - - - result using Machine Algorithm home [10] Hygrometry priori C knowledge O M Wi- M Magnetic, fi(camara- Multi-sensor centric Convergence U photo diode, control smart sensor network UMPC-Ultra Mobile Only technology N 6 microphone, system)Zig Wi-fi design using mobile mobile PC module device user monitoring with 3D I motion, bee (sensor- device [11] modelling C vibration control A system T Mobile health I monitoring system Ring –type HSDPA, Wi- GUI in O using wearable ring- pulse sensor, 7 Bluetooth - Fi, Wimax, Smart - - N type pulse monitor smart phone, GPRS phone sensor with smart ASUSP552W S phone [12] Y N Design and S E 8 implementation of Light and ZigBee - Wi-Fi Virtual _ _ T T home automation Smoke sensor Home GUI E W architecture [13] M O Dynamic intelligent R Android Implementing home control system User behavior Smart Classifier using K 9 - platform GSM, WiFi the proposed using Android phone Analysis phone Database network idea [14] B Incorporate A Sensor unit for more S electric Monitoring In-House monitoring intelligent E 10 appliance, bed ZigBee Only monitoring In-home only software - for elders [15] features like D usage, water GUI positioning usage method Pulse sensor, Implementing E- Mixed Pressure 11 Healthcare using WSN - Internet - Positioning - sensor, fire WSN [16] Algorithm sensor Implementing a smart Temperature, home with digital Door lock 12 gas, fire ZigBee - Internet - - door lock as base LCD sensors station [17] Deployment of a activity-centered WSN in a living Temperature, 13 ZigBee computing - - - - laboratory home Pressure, Light middleware Environment [18] Updating the Implement a smart Smoke, gas, system with 14 home security system temperature, ZigBee Sending SMS GSM - - intelligent [19] biosensor home security C Hybrid O Algorithm Improved N Prediction of user (Prediction Algorithm for T interaction – in Decision Tree Algorithm) – 15 - - - - short memory R energy management method Day Type of first order O smart home [20] Model, First markov model L order Semi markov Mode 98
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Study of S psychological Clustering Improving an Y characteristics of Multi agent algorithm for algorithm 16 - - - - S home user using System smart home using pattern T multi agent system events recognition E [21] M Smart Field Friendly SH energy Smart meter, Grid based Multi-in-one 17 Zigbee - Interactive Programmable mgmt. [22] Smart Switch controller smart meter terminal gate array The interface Jadex Agent- of the agent To apply agent based Hieratical goal Thermal Jadex-using BDI system with 18 system to control a - - - decomposition- Sensor Agent building smart home [23] Goal Plan automation Hierarchy installations Smart devices- Play Station, Plogg – Optimize the Develop an energy Lamp, Coffee Hydra –a wireless Hydra – Event performance of 19 efficient smart home Maker middleware frame - Ubilenc smart meter Management reading energy system [24] work plugs consumption Making this to assisted living Changing the Develop a pro active, for the elderly. weighting adaptive, fuzzy Fuzzy control Using 20 Light sensor - - - factors and for home-control system process knowledge of adding and [25] the user removing rules routines for prediction3.1. Communication System3.1.1. Routing Algorithms D.M. Han and J.H. Lim proposed smart home energy management system-SHEMS[6]. This system divides and assigns various home network tasks to appropriate components.It can integrate diversified physical sensing information and control various consumer homedevices such as lamps, gas valves, curtains, TV, and air conditioners with the support ofactive sensor networks having both sensor and actuator components. A personal area networkbased SHEMS consists of three software components, Sensing Infra – gathers sensing dataand provides this to the decision components, Context Aware – a intelligent computingbehaviour, Service Management – a decision component adaptively selects the correct homeservices based on the current home state. A new routing protocol DMPR (Disjoint Multi Pathbased Routing) to improve the performance of the ZigBee sensor networks is also developed. Ming Xu, Longhua Ma, Feng Xia, Tengkai Yuan, Jixin Qian, and Meng Shaosuggested [7] a star-mesh hybrid topology based smart sensor network architecture usinggeneral mobile devices to provide more efficient and valuable sensor network application andservices. This architecture consists of four components, ZigBee network coordinator –responsible for communication, ZigBee node – composed of sensors and ZigBee wirelessmodule, GPRS network – transfer the gathered data to monitoring centre via the GPRSnetwork and the Internet, Monitoring centre – manage the data generated by all ZigBeenetwork. An improved Dijkstra algorithm is presented and the performance is evaluatedthrough simulation. C. Suh and Y.B. Ko put forwarded an intelligent home control system which dividesand assigns various home network tasks to appropriate components[8]. With the support ofactive sensor networks, which is having sensor and actuator components, information aresensed and control various consumer home devices. A new routing protocol LQIR (LinkQuality Indicator based Routing) is developed to improve the performance of active sensornetworks. 99
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Dexing Zhong, Wei Ji, Yongli Liu and Jiuqiang Han demonstrated a design of smarthome network based on Zigbee wireless sensor network technology [9]. A more convenientand reliable wireless communication environment for Zigbee wireless network in the smarthome system is achieved by using an improved routing algorithm, which is combinationCluster-Tree and AODVjr algorithms.3.1.2. Network Based Anthony Fleury, Michel Vacher and Norbert Noury examined a SVM-Health Smarthome [10] in a real flat, with Infra-Red Presence Sensors (location), door contacts (to controlthe use of some facilities), temperature and hygrometry sensor in the bathroom, andmicrophones (sound classification and speech recognition). The data collected from thevarious sensors, is then used to classify each temporal frame into one of the activities of dailyliving that was previously acquired (seven activities: hygiene, toilet use, eating, resting,sleeping, communication, and dressing/undressing). This is done by using Support VectorMachines. Bonhyun Koo Kyusuk Han James J. Park and Taeshik Shon implemented [11] thedesign and implementation of the wireless sensor node and the coordinator based on ZigBeetechnology. A monitoring system is built by using the GPRS network. An improved routingalgorithm based on the Dijkstra algorithm is presented to support multi-hop communications. Yu-Chi Wu, Pei-Fan Chen, and Zhi-Huang Hu presented a mobile health monitoringsystem with the integration of a wearable ring-type pulse monitoring sensor with a smartphone [12]. Through Bluetooth measurements are transmitted to the smart phone where usercan monitor own pulse or temperature. Measured data are transmitted to a remote serverthrough the mobile communication of the smart phone, such as SDPA, Wi-Fi, WiMax,GPRS, etc. The remote server also tracks the position of the monitored person in real time. Khusvinder Gill, Shuang-Hua Yang, Fang Yao, and Xin Lu implemented a ZigBeebased virtual home automation system and Wi-Fi network which integrated through acommon home gateway [13]. The home gateway provides a simple and flexible user interfaceand remote access to the home system. Jiali Bian, Dengke Fan, and Junming Zhang proposed a new type of intelligent homecontrol system [14], using Android Phone which can act as a temporary home gatewayinstead of default gateway. An intelligent control system also constructed based on userbehaviour analysis. To achieve energy savings and reduce cost they made the system toautomatically shut down the unused device. Anuroop Gaddam, Subhas Chandra Mukhopadhyay, and Gourab Sen Gupta deliberateand test the performance of the smart home monitoring system using zigbee radio frequency(RF) communication [15]. Few highly accurate inexpensive smart sensors are used to developa typical in-house home monitoring for elder care application. Hairong Yan, Hongwei Huo, Youzhi Xu, and Mikael Gidlund recommended awireless sensor network application for 24 hour constant monitoring [16] without disturbingdaily activities of elderly people. A mixed positioning algorithm is also proposed todetermine the location, where the elderly person is. This helps the system to determine theperson’s activities and further to make decisions about his/her health status. This systemprovides two types of basic needs: identify abnormal events and emergency alarms to doctor 100
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEMEthrough auto call, SMS and e-mail and day-to-day requirement such as taking of medicine,turn off water heater and so on. Yong Tae, Park Pranesh Sthapit, and Jae-Young Pyun designed a smart digital door locksystem for home automation [17]. This system consists, network of sensor nodes andactuators with digital door lock as base station. A door lock system consists of RFID readerfor user authentication; touch LCD, motor module for opening and closing of he door, sensormodules for detecting the condition inside the house, communication module, and controlmodule for controlling other modules. Advantage of this system is, it can be easily installedimmediately as per necessity without any prerequisite additional infrastructures. Dipak Surie, Olivier Laguionie, Thomas Pederson implemented a smart home to keeptrack of every day object and their state changes produced based on the user’s interactionwith them[18]. A ZigBee communication protocol based wireless sensor networking of 42everyday objects embedded with 81 simple state change sensors of eight sensor types in aliving laboratory smart home environment is implemented. Syam Krishna, J.Ravindra designed a remote home security system based on ZigBee[19]. It consists of microcontroller based wireless sensor network center node with GSMmodule, data collecting node, device control node and mobile phone. This system sendalaram signal to WSN center node when ever the ubnormal situation arise. The center nodesend alarm short message to the users through the GSM module and GSM networkimmediately. Similarly the user can also control the various devices connected with devicecontrol unit through SMS.3. 2. Control System Kaibin Bao, and Florian Allerding discussed about the prediction of user interactionswithin a real world scenario of energy management for a smart home [20]. To address thechallenge of balancing energy demand and generation, external signals, reflecting the lowvoltage grid’s state, are used. Two prediction algorithms to estimate the future behaviour ofthe smart home are presented: The Day Type Model and a probabilistic approach based on afirst order Semi Markov Model. Some experimental results with real world data of the KITsmart home are presented. M. R. Alam, M. B. I. Reaz, M. A. Mohd Ali, S. A. Samad, F. H. Hashim, M.K.Hamzah, have an idea to study the psychological characteristics of home user [21]. Peoplefollow some specific patterns in their life style. Inhabitant activity classification plays a vitalrole to predict smart home events. This paper proposed a multiagent system to track the userfor task isolation. The system is composed of cooperative agents, which works by sharinglocal views of individual agents. An algorithm is derived based on opposite entity stateextraction for activity classification. The algorithm clusters the smart home events byisolating opposite status of home appliance. Result shows that the proposed algorithm cansuccessfully identify inhabitant activities of various lengths. Yong Zhao, Wanxing Sheng, Junping Sun, Weijun Shi build up the friendly smarthome energy system [22] which is composed of smart meter, smart socket/switch, gridfriendly appliance controller, smart interactive terminal and other smart devices. Then itrespectively elaborates and analyses their main functions, and gives their design blockdiagrams. Then they look into the future of home energy system and also introduce theZigBee communication technology and its smart energy profile, and give a few typicaltransactions based on the ZigBee Smart Energy profile. 101
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Christian Reinisch and Wolfgang Kastner developed [23] a smart home controlsystem with an agent characteristic such as goal drivenness, robustness and learning capablebehaviour complement. They describe the complete process from selection of the most suitedagent approach for smart homes to detailing the transition from system specification toimplementation. Marco Jahn, Marc Jentsch, Christian R. Prause, Ferry Pramudianto, Amro Al-Akkad,and Rene Reiners evolve a novel smart home system with integration of energy efficiencyfeatures [24]. The smart home application is built on top of Hydra, a middleware frameworkthat facilitates the intelligent communication of heterogeneous embedded devices through anoverlay P2P network. Common devices available in private households and integrate wirelesspower metering plugs to gain access to energy consumption data are interconnected. Thesedata are used for monitoring and analyzing consumed energy on device level in near real-time. The combination of both, a technically sophisticated smart home application and at thesame time transparent, intuitive user interfaces showing information regarding the energyusage e.g. energy price, energy source, standby consumption has the potential to bring thevision of the energy efficient smart home within reach. Antti-Matti Vainio, Miika Valtonen, and Jukka Vanhala make out an idea to changethe environment, which always conforms, to the user’s desires and needs [25]. Theenvironment actuators are controlled proactively, so that the system can always anticipate theuser’s requirements. In this way, the user would not need to bother with equipment control.4. CONCLUSIONSmart home system is used to monitor object/human remotely. Different kinds of sensors liketemperature, gas, light, ect., are used in smart home. Recommended home network is ZigBeeIEEE 802.15.4 because of its low power consumption. Internet, GSM, Mobile Network areuse as an external home access network. Using embedded concept any device can be used asa gateway. Digital lock is used as a gateway. Smart phone can play dynamic roll as gatewayas well as external home network. People who are elderly or disabled benefit the most from a smart home system. Thesesystems help people those who are less mobile, or in delicate health, the opportunity to beindependent, rather than staying in an assisted living facility. In this paper, the comparativestudy of different smart home system has been discussed. Since the smart home systems areapplication specific, no particular system can be considered better than other. Futureperspectives of this work are focused towards developing an energy efficient smart homesystem.5. REFERENCES 1. S.Irudaya Rajan, “Population Aging and Health in India”, Centre for Enquiry into Health and Allied Themes, Survey Number 2804, 2805, Mumbai, 2006. 2. Diane J. Cook and Sajal K. Das, “How Smart are our Environments? An Updated Look at the State of the Art”, Parvasive and mobile computing, vol.3, pp. 53-73, 2007. 3. Rosslin John Robles, Tai-hoon Kim, “Applications, Systems and Methods in Smart Home Technology: A Review”, International Journal of Advanced Science and Technology, Vol. 15, 2010. 4. Diane J. Cook, Juan C. Augusto, Vikramaditya R. Jakkula, “Ambient Intelligence: Technologies, Applications, and Opportunities”, Parvasive and mobile computing, vol.5, pp 277-298, 2009. 102
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME 5. Wei LIU, Yuhua YAN, "Application of ZigBee Wireless Sensor Network in Smart Home System", IJACT: International Journal of Advancements in Computing Technology, Vol. 3, No. 5, pp. 154 - 160, 2011. 6. D.M. Han, J.H. Lim “Design and Implementation of Smart Home Energy Management Systems based on ZigBee”, IEEE, 2010. 7. Ming Xu, Longhua Ma, Feng Xia, Tengkai Yuan, Jixin Qian, Meng Shao, “Design and Implementation of a Wireless Sensor Network for Smart Homes”, 2008. 8. C. Suh and Y.-B. Ko: Design and Implementation of Intelligent Home Control Systems based on Active Sensor Networks, IEEE, 2008 9. Dexing Zhong, Wei Ji, Yongli Liu, Jiuqiang Han, An Improved Routing Algorithm of Zigbee Wireless Sensor Network for Smart Home System, 5th International Conference on Automation, Robotics and Applications, , Wellington, New Zealand, 2011. 10. Anthony Fleury, Michel Vacher and Norbert Noury, “SVM-Based Multi-Modal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms and First Experimental Results”, IEEE transation on Information Technology in Biomedicine, pp. 274 - 283, 2010. 11. Bonhyun Koo, Kyusuk Han, James Jong Hyuk Park, Taeshik Shon, “Design and implementation of a wireless sensor network architecture using smart mobile devices”, Springer Science Business Media, 2011. 12. Yu-Chi Wu, Pei-Fan Chen, Zhi-Huang Hu, “A mobile health monitoring system using RFID ring-type pulse sensor” , Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009. 13. Khusvinder Gill, Shuang-Hua Yang, Fang Yao, and Xin Lu, “A ZigBee-Based Home Automation System”, IEEE Transactions on Consumer Electronics, Vol. 55, No. 2, 2009. 14. Jiali Bian, Dengke Fan, Junming Zhang, “The new Intelligent Home Control System Based on the Dynamic and Intelligent Gateway”, Proceedings of IEEE IC-BNMT,2011. 15. Anuroop Gaddam, Subhas Chandra Mukhopadhyay, Gourab Sen Gupta, “Trial & Experimentation Of A Smart Home Monitoring System For Elderly”, IEEE, 2011. 16. Hairong Yan, Hongwei Huo, Youzhi Xu, Mikael Gidlund, “Wireless Sensor Network Based E-Health System –Implementation and Experimental Results”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, 2010. 17. Yong Tae, Park Pranesh Sthapit, Jae-Young Pyun,” Smart Digital Door Lock for the Home Automation”, IEEE, 2009. 18. Dipak Surie, Olivier Laguionie, Thomas Pederson, “Wireless Sensor Networking of Everyday Objects in a Smart Home Environment”, IEEE, 2008. 19. Syam Krishna, J.Ravindra, “ Design And Implementation Of Remote Home Security System Based on WSNs And GSM Technology”, International Journal Of Engineering Science & Advanced Technology, Vol.2, pp. 139-142, 2012. 20. Kaibin Bao, Florian Allerding, “User Behavior Prediction for Energy Management in Smart Homes”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery, pp.1335-1339, 2011. 21. M. R. Alam, M. B. I. Reaz, M. A. Mohd Ali, S. A. Samad, F. H. Hashim, M.K. Hamzah, Human Activity Classification for Smart Home:A Multiagent Approach, ISIEA -2010, pp. 3- 5, 2010. 22. Yong Zhao, Wanxing Sheng, Junping Sun, Weijun Shi, “Research and Thinking of Friendly Smart HomeEnergy System Based on Smart Power, IEEE, 2011. 23. Christian Reinisch, Wolfgang Kastner, “Agent based Control in the Smart Home”, IEEE , 2011. 24. Marco Jahn, Marc Jentsch, Christian R. Prause, Ferry Pramudianto, Amro Al-Akkad, Rene Reiners, “The Energy Aware Smart Home”, IEEE, 2010. 25. Antti-Matti Vainio, Miika Valtonen, Jukka Vanhala, “Proactive Fuzzy Control and Adaptation Methods for Smart Homes”, IEEE, 2008. 103