Smart home systems using wireless sensor network a comparative analysis

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: www.iaeme.com/ijcet.asp
Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME
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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
kavitha.r@christuniversity.in nasiragm99@yahoo.com nachamai.m@christuniversity.in

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.

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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 – 2051

The smart home system provides an inviolable, safe sheltered and comfortable life like in an
assisted living environment. It enhances traditional security and safety mechanism by using
intelligent monitoring and access control.
The structure of this paper is as follows. Section 2 describes basic structure of a wireless
smart home and its components. In section 3 comparative analysis of different smart home
system is discussed. The conclusion and future research direction are presented in section 4.

2. SMART HOME SYSTEM

The basic structure of a smart home system is depicted in figure 2. The smart home
integration 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 components
and control system. The control system can access from home exterior through external home
network like mobile network or Internet. In a smart home system the physical components
sense the environment and pass to home control system through home sub networks and
home network. Home control system takes the decision and passes the control information to
the actuators through home network. For example, gas sensor detects the gas leakage in a
smart home and passes this message to the home control system through ZigBee, a wireless
network. Control system decides to switch off the gas valve and pass this to actuator, which
will off/close the gas valve.

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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 System

2.1. Physical Components

The role of physical components is very important. It measures and collects the information
and shares with the control system through network. Sensors, microcontroller, actuator and
smart 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 detection
properties
Distance
Security and health monitoring, Pool
Biochemical Agents Solid, Liquids, Gases
maintenance, Sprinkler efficiency
Used to identify people and objects, Volume
Others iButtons, Sound, Image control, Speech recognition, Context
understanding

2.2. Control System
Control system receives the information from different sensors and classifies it for the
different types of activities. For example, the data collected from the accelerometer sensor
positioned 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 home
application [4]. Following are some example.

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• 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 based
system or Neural Network with reinforcement learner or Fuzzy rules develop an automated
decision-making and control techniques.

2.3. Communication System
The communication system is use to share the information between physical components and
control system in the smart home system. It can be wired or wireless communication. The
widely used wireless technologies are Bluetooth, WiFi, WiMAX, and ZigBee [5]. Bluetooth
is the first and popular low bandwidth wireless interface for the smart home. In the last few
years the bandwidth requirement of the smart home has increased dramatically which
introduces WiFi - a wireless local area networks technology based on the IEEE 802.11. It
covers an entire house, and the data rate is reduced to 1 MB or below at the far distance. It
consumes more power and provides low security. WiMAX provides wireless broadband
access and it is alternative to the cable connection. Due to the low cost, low power
consumption and easy integration into smart home control system ZigBee a wireless
technology become a quite suitable for smart home environment. Comparison of above
mentioned 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 distance
Bluetooth 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
2.4G

3. COMPARATIVE ANALYSIS OF A SMART HOME SYSTEM

The comparative analysis of a smart home system using wireless sensor network is done by
classifying a study into two categories as communication system and control system. The
literature 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 for
monitoring and control process of a control system in a smart home. Table 3 represent the
analysis with two categories as communication system and control system. The
communication system is further divided into routing algorithm and network based
categories. 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 both
internal and external home network.

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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

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
prediction

3.1. Communication System
3.1.1. 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 home
devices such as lamps, gas valves, curtains, TV, and air conditioners with the support of
active sensor networks having both sensor and actuator components. A personal area network
based SHEMS consists of three software components, Sensing Infra – gathers sensing data
and provides this to the decision components, Context Aware – a intelligent computing
behaviour, Service Management – a decision component adaptively selects the correct home
services based on the current home state. A new routing protocol DMPR (Disjoint Multi Path
based 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 Shao
suggested [7] a star-mesh hybrid topology based smart sensor network architecture using
general mobile devices to provide more efficient and valuable sensor network application and
services. This architecture consists of four components, ZigBee network coordinator –
responsible for communication, ZigBee node – composed of sensors and ZigBee wireless
module, GPRS network – transfer the gathered data to monitoring centre via the GPRS
network and the Internet, Monitoring centre – manage the data generated by all ZigBee
network. An improved Dijkstra algorithm is presented and the performance is evaluated
through simulation.

C. Suh and Y.B. Ko put forwarded an intelligent home control system which divides
and assigns various home network tasks to appropriate components[8]. With the support of
active sensor networks, which is having sensor and actuator components, information are
sensed and control various consumer home devices. A new routing protocol LQIR (Link
Quality Indicator based Routing) is developed to improve the performance of active sensor
networks.

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Dexing Zhong, Wei Ji, Yongli Liu and Jiuqiang Han demonstrated a design of smart
home network based on Zigbee wireless sensor network technology [9]. A more convenient
and reliable wireless communication environment for Zigbee wireless network in the smart
home system is achieved by using an improved routing algorithm, which is combination
Cluster-Tree and AODVjr algorithms.

3.1.2. Network Based
Anthony Fleury, Michel Vacher and Norbert Noury examined a SVM-Health Smart
home [10] in a real flat, with Infra-Red Presence Sensors (location), door contacts (to control
the use of some facilities), temperature and hygrometry sensor in the bathroom, and
microphones (sound classification and speech recognition). The data collected from the
various sensors, is then used to classify each temporal frame into one of the activities of daily
living that was previously acquired (seven activities: hygiene, toilet use, eating, resting,
sleeping, communication, and dressing/undressing). This is done by using Support Vector
Machines.

Bonhyun Koo Kyusuk Han James J. Park and Taeshik Shon implemented [11] the
design and implementation of the wireless sensor node and the coordinator based on ZigBee
technology. A monitoring system is built by using the GPRS network. An improved routing
algorithm 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 monitoring
system with the integration of a wearable ring-type pulse monitoring sensor with a smart
phone [12]. Through Bluetooth measurements are transmitted to the smart phone where user
can monitor own pulse or temperature. Measured data are transmitted to a remote server
through 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 ZigBee
based virtual home automation system and Wi-Fi network which integrated through a
common home gateway [13]. The home gateway provides a simple and flexible user interface
and remote access to the home system.

Jiali Bian, Dengke Fan, and Junming Zhang proposed a new type of intelligent home
control system [14], using Android Phone which can act as a temporary home gateway
instead of default gateway. An intelligent control system also constructed based on user
behaviour analysis. To achieve energy savings and reduce cost they made the system to
automatically shut down the unused device.

Anuroop Gaddam, Subhas Chandra Mukhopadhyay, and Gourab Sen Gupta deliberate
and 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 develop
a typical in-house home monitoring for elder care application.

Hairong Yan, Hongwei Huo, Youzhi Xu, and Mikael Gidlund recommended a
wireless sensor network application for 24 hour constant monitoring [16] without disturbing
daily activities of elderly people. A mixed positioning algorithm is also proposed to
determine the location, where the elderly person is. This helps the system to determine the
person’s activities and further to make decisions about his/her health status. This system
provides two types of basic needs: identify abnormal events and emergency alarms to doctor

100


through 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 lock
system for home automation [17]. This system consists, network of sensor nodes and
actuators with digital door lock as base station. A door lock system consists of RFID reader
for user authentication; touch LCD, motor module for opening and closing of he door, sensor
modules for detecting the condition inside the house, communication module, and control
module for controlling other modules. Advantage of this system is, it can be easily installed
immediately as per necessity without any prerequisite additional infrastructures.

Dipak Surie, Olivier Laguionie, Thomas Pederson implemented a smart home to keep
track of every day object and their state changes produced based on the user’s interaction
with them[18]. A ZigBee communication protocol based wireless sensor networking of 42
everyday objects embedded with 81 simple state change sensors of eight sensor types in a
living 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 GSM
module, data collecting node, device control node and mobile phone. This system send
alaram signal to WSN center node when ever the ubnormal situation arise. The center node
send alarm short message to the users through the GSM module and GSM network
immediately. Similarly the user can also control the various devices connected with device
control unit through SMS.

3. 2. Control System
Kaibin Bao, and Florian Allerding discussed about the prediction of user interactions
within a real world scenario of energy management for a smart home [20]. To address the
challenge of balancing energy demand and generation, external signals, reflecting the low
voltage grid’s state, are used. Two prediction algorithms to estimate the future behaviour of
the smart home are presented: The Day Type Model and a probabilistic approach based on a
first order Semi Markov Model. Some experimental results with real world data of the KIT
smart 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]. People
follow some specific patterns in their life style. Inhabitant activity classification plays a vital
role to predict smart home events. This paper proposed a multiagent system to track the user
for task isolation. The system is composed of cooperative agents, which works by sharing
local views of individual agents. An algorithm is derived based on opposite entity state
extraction for activity classification. The algorithm clusters the smart home events by
isolating opposite status of home appliance. Result shows that the proposed algorithm can
successfully identify inhabitant activities of various lengths.

Yong Zhao, Wanxing Sheng, Junping Sun, Weijun Shi build up the friendly smart
home energy system [22] which is composed of smart meter, smart socket/switch, grid
friendly appliance controller, smart interactive terminal and other smart devices. Then it
respectively elaborates and analyses their main functions, and gives their design block
diagrams. Then they look into the future of home energy system and also introduce the
ZigBee communication technology and its smart energy profile, and give a few typical
transactions based on the ZigBee Smart Energy profile.

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Christian Reinisch and Wolfgang Kastner developed [23] a smart home control
system with an agent characteristic such as goal drivenness, robustness and learning capable
behaviour complement. They describe the complete process from selection of the most suited
agent approach for smart homes to detailing the transition from system specification to
implementation.

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 efficiency
features [24]. The smart home application is built on top of Hydra, a middleware framework
that facilitates the intelligent communication of heterogeneous embedded devices through an
overlay P2P network. Common devices available in private households and integrate wireless
power metering plugs to gain access to energy consumption data are interconnected. These
data 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 the
same time transparent, intuitive user interfaces showing information regarding the energy
usage e.g. energy price, energy source, standby consumption has the potential to bring the
vision of the energy efficient smart home within reach.

Antti-Matti Vainio, Miika Valtonen, and Jukka Vanhala make out an idea to change
the environment, which always conforms, to the user’s desires and needs [25]. The
environment actuators are controlled proactively, so that the system can always anticipate the
user’s requirements. In this way, the user would not need to bother with equipment control.

4. CONCLUSION

Smart home system is used to monitor object/human remotely. Different kinds of sensors like
temperature, gas, light, ect., are used in smart home. Recommended home network is ZigBee
IEEE 802.15.4 because of its low power consumption. Internet, GSM, Mobile Network are
use as an external home access network. Using embedded concept any device can be used as
a gateway. Digital lock is used as a gateway. Smart phone can play dynamic roll as gateway
as well as external home network.
People who are elderly or disabled benefit the most from a smart home system. These
systems help people those who are less mobile, or in delicate health, the opportunity to be
independent, rather than staying in an assisted living facility. In this paper, the comparative
study of different smart home system has been discussed. Since the smart home systems are
application specific, no particular system can be considered better than other. Future
perspectives of this work are focused towards developing an energy efficient smart home
system.

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


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.

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