Critical on the Smart Home - Literature review Example

Critical Review on the Smart Home Introduction The connected home or smart home is constantly evolving from basic connected homenetworks to a feature loaded collection of consumer electronic devices, multifunctional set-top boxes, broadband-enabled services, and residential gateways. Traditional and existing services continue to offer more in terms of functions, speed, and consumer benefits. Meanwhile, consumer demands and preferences are largely driving the technological advances in services and devices (Parks Associates, 2012). Nowadays, connectivity in the general sense is considered a high-end uniqueness in home appliances such as security cameras, utility meters, thermostats, Blu-ray players and TVs, rather than a feature for the mass-market. As the world moves into a future where connectivity is pervasive and embedded in virtually every household device, this view will become out-dated. According to many analysts, the future smart home is likely to hold between 15 and 30 connected devices and sensors, all linked through a home network and related to service providers’ systems as well as the internet. The array of connected devices will range from usual household appliances through electric vehicle charging infrastructure and solar panels that consume as well as generate electricity. It is expected that the combined revenues from the home energy management (HEM), home automation and smart metering segments will be worth over $44 billion as at 2016. However, the overall potential of the smart home is expected to be considerably greater as devices from the health, home security, and entertainment sectors also become connected (Groupe Speciale Mobile Association [GSMA], 2011). This paper aims to undertake a critical review on the smart home. Precisely, this paper shall undertake a review of the smart home techniques that are currently in use, the communication techniques, and the boards used. This paper shall further review the currently used boards against the Teensy board, Adafruit CC3000 board, and an Arduino board with yun processor. Segments of Smart Home Technologies Smart Home is a term used to refer to a residence that utilizes a home controller to integrate the different household automation systems within the residence (Robles & Kim, 2010). According to Levy, Taga, Saadoun & Riegel (2012), the Smart Home market consists of four main segments that include home automation/security, home assistance, home cloud, and e-Health. Levy et al (2012) point out that home automation is the centralization on a unique user interface of five major home systems. These include home utility and energy management (smart meters), home security, home motorization (remote control of devices like thermostats and alarm systems) entertainment and lighting. The key drivers of this segment are comfort, peace of mind and modularity, particularly with regards to security, while the emphasis of energy management is cost savings. Energy management and security are the more advanced systems with respect to integration, while home automation is still in its infant stages of development (Levy et al, 2012). Another segment is home assistance. This refers to the configuration, maintenance, repair, and support services that are available for digital home appliances, such as TVs, video players, game consoles, networks, and PCs. Home assistance can be further subdivided into two sub-categories that include in-home support, in the course of the physical occurrence of prop up staff, and remote aid, managed via the remote control of the appliance by an off-site technician. The ecosystem for home assistance is highly fragmented with numerous solutions on offer. Successful players are basically partnerships, with each partner focusing on particular aspects of the value chain, such as sales, personnel qualification, billing, CRM, and service delivery. For instance, OneForce operates as a technician network aggregator as well as an open marketplace in the United States (Levy et al, 2012). The other segment is home cloud. This segment covers three major kinds of digital data. These are content (music, video, and pictures), sensors (data collected via Smart Home devices, such as e-Health devices and smart meters), and productivity (documents, email and contacts). Home cloud solutions allow new collaborative and ubiquitous usages, but raise concerns regarding data management. The main market driver is increasing amount of data, particularly video, which leads to a strong demand for remote access and storage (Levy et al, 2012). The range of offers driving productivity in this segment is vast. Some include multi-screen usage and service personalization, such as Spotify and Hulu Plus, which up-sell their viewers to multi-screen services for a monthly subscription fee. Others include Boxee, which is integrating all sorts of online and locally-stored content, like music, video, and photos, and enabling users to share their content and preferences with friend through social network integration. Other top players such as Google and Apple also present multi-device and cloud-based elucidations for “Personal Information Management” (Levy et al, 2012). The fourth Smart Home segment is e-Health. It involves the application of information and communication technologies in the health sector. It offers a unique cost control measure for stakeholders in the healthcare sector by mainly de-materializing some components within healthcare. New firms like Cardiocom are coming up with innovative e-Health solutions, which offer a comprehensive solution for telemedicine, including body-connected sensors (Levy et al, 2012). A wide range of actors, like telecom operators, large pharmaceutical companies, and device manufacturers are operating in the e-Health market with two main market strategies: a niche market approach, such as remote patient monitoring as a B2B (or B2B2C) market, and a mass market strategy, such as Withings or Wii Fit-a WiFi body scale, mainly serving B2C markets (Levy et al, 2012). Smart Home Techniques According to Chan, Est`eve, Escriba & Campo (2008), several smart homes have already been developed in various parts of the world. Besides matters of leisure and comfort, they are mainly meant to monitor elderly individuals with visual, motor, cognitive or auditory disabilities. In all cases the houses, along with their different electric appliances, have been fitted with actuators, sensors, and/or biomedical monitors. The devices work in a network that is at times connected to a remote data collection and processing center. The remote center performs a diagnosis of the prevailing situation in the home at any time and initiates the necessary assistance procedures (Chan et al, 2008). Smart Home Technologies in USA ACHE System In Boulder, Colorado, an “adaptive” house that uses neural networks to control heating, lighting, and temperature without prior programming by residents has been developed. The system, known as ACHE, tries to economize energy resources while respecting the desires and lifestyles of the inhabitants. The system continuously monitors the environment while observing the residents’ actions (adjusting the thermostat, using the lights). Out of the data collected, the system infers patterns within the home and uses reinforcement learning, a stochastic type of dynamic programming that samples trajectories in state space, in order to make a prediction on future behavior (Chan et al, 2008). MavHome Project Another Smart Home project is the MavHome project, which was built up by the University of Texas, Arlington. The project aims to develop a home that will act as a rational agent by attempting to maximize the inhabitants’ comfort while minimizing the costs of operation. The agent must have the ability to sense and predict the inhabitants’ movement habits and their usage of electric appliances. The main aspire is to generate a typical estimator or predictor of user mobility. The so-called LeZi method is an information theory technique that is used to develop a probabilistic model that predicts the inhabitant’s typical path segments. Particularly, the Active LeZi (ALZ) algorithm calculates the chances of each possible action occurring in the sequence that has been observed, and predicts the potential actions with the greatest probability. Several technologies are combined in the MavHome project. These include multi-media computing, databases, mobile computing, robotics, and artificial intelligence (Chan et al, 2008). Elite CARE Elite CARE (creating an autonomy-risk equilibrium) in Portland, Oregon is an assisted living facility that employs smart home technologies. Its inhabitants are retirees, some of whom are suffering from Alzheimer’s or dementia. The Smart Home technologies employed in Elite CARE aim to prolong independence of the patients and assist the staff in identifying health problems at an early stage. By using digital technologies, including the Internet, health information is processed in real time. The system can detect behavioral cues that indicate a change in the individual’s cognitive or physical condition, enhance social networks through electronic mail, and regulate ambient conditions. Researchers associated with the Elite CARe project have designed a technique of unobtrusively monitoring the wake-up time and bed time of the each resident, as well as the residents’ position shifts when sleeping (Chan et al, 2008). In Canada, Toronto, Mihailidis, Carmichael and Boger (2004) created a vision-based system that has the ability to track the fine and gross motor movements of elderly people. The system contains three agents that include, sensing, prompting and planning. The sensing agent was created using a video camera and a MatroxMeteor II frame grabber installed on a 2.4 GHz PC. Physics-based as well as statistics-based techniques of segmenting skin color in digital images were used for real time hand and face tracking. The principal goal of the technology is not only to recognize and track the hand positions that are associated with every activity of daily living (ADL) step, but also to perform it discreetly in order to better support a policy of aging-in-place (Mihailidis et al, 2004). Smart Home Technologies in Asia Welfare Techno-Houses In Japan, 15 smart houses were developed in 1998. The aim of the project is maximizing usage of assistive technologies enabling elderly people to live at home by developing a comfortable and smart environment. Known as Welfare Techno-Houses (WTH), the objective of the care-houses is to improve the eminence of old people, including their caregivers. The care-houses have been used as a test platform for new diagnostic technologies, and the evaluation of the residents’ living conditions. Data on the residents’ physiological and health signs are collected by installing fully automated medical devices in the bathroom. Monitoring of the residents’ physical activity is done by installing infrared sensors in rooms and magnetic switches on the doors. Over time, the system stores a lot of raw data on the subject’s ECG, localization, and bed temperature (Chan et al, 2008). Personal Behavior Modeling and Recognition System NTT DoCoMo, a multimedia laboratory in Japan, has developed a system that can model and recognize personal behavior based on Radio Frequency Identification (RFID) - tagged objects and sensors. According to Isoda, Kurakake, & Nakano (2004), the resident’s daily activity is modeled as a sequence of states that describe their varying contexts. The subject’s state is represented by attributes that express which objects are in the subject’s vicinity, and the period of time the objects remain in the vicinity. Another attribute is the subject’s absolute position. Modeling of behavior is based on learning. The raw data obtained from the sensors and RFID tags is classified “typical” states by developing a decision tree. The subject’s behavioral context at any particular time is obtained by matching the most recently detected states to previously defined task models. Isoda et al (2004) conclude that this system is an effective means of acquiring the subject’s spatio-temporal context. Sensing Room Noguchi, Mori, & Sato (2002) developed a sensing room at the University of Tokyo. The system is designed to collect quantitative data regarding human daily actions, and then it learns and conducts data analysis. This project’s goal is basically to support an individual in his/her daily life. Three main components make up the system. They include data collection, processing, and integration of the processed data. The state of the bed, table, switches and floor are recorded by the room’s sensing modules. The combination of the data is defined as the “room state”. An algorithm known as “summarization” was developed to segment the accumulated sensor data at points where there is a drastic change of outputs. These segments are matched with and assigned to the “room states”. Additionally, the algorithms attempt to eliminate any redundant states that have only slightly changed. The system also includes switch sensors on several appliances and the sensors are able to detect if a person is sleeping or standing, the position of his/her hands on the table, and the position of objects on the table. The refrigerator, toaster, freezer, windows, chest of drawers, and microwave are all fitted with switch sensors for detecting if their doors are closed or open (Noguchi et al, 2002). Location System A location system that is based on pyroelectric (P) IR sensors was developed through a collaboration of two universities in Korea. The test bed is a room that measures 4m x 4m x 2.5m. The ceiling is fitted with twelve PIR sensors. The aim of the researchers was to build a smart home that is able to detect the inhabitant’s lifestyle and state of health, so that their needs can be better anticipated and appropriate services can be provided. A performance evaluation using the test bed was carried out and the next step in the study is to build up an algorithm that can establish the location as well as trajectory of several residents simultaneously (Ha, Lee & Lee, 2006). Smart Home Technologies in Europe Numerous smart homes have been developed in Europe. Usually, their main goal is to provide support for elderly people who live at home (Chan et al, 2003). For instance, the UK Government Engineering and Physical Sciences Research Council, Barnwood Trust, the European Commission, and Gloucester Social Services are jointly funding Gloucester’s Smart House Project. Bath Institute of Medical Engineering and Dementia Voice Housing 21 are leading the project, which is aimed at helping individuals suffering from dementia. Most objects in the house are constantly monitored via sensors. For example, the indoor environment and bathwater temperature are continuously monitored (Orpwood, Adlam &Gibbs, 2001). CareNet CareNet is a Smart Home project designed in the UK. It deploys several telecare and “hospital at home” services like access to community health information, ambulatory monitoring, and emergency alarm (Williams, Doughty & Bradley, 1998). Besides therapy units, CareNet includes a sensor bus, a sensor set, a control unit, and an intelligent monitoring system. The system incorporates many functions such as collecting physiological data (photoplethysmograph, ECG, temperature, spirometry, galvanic skin response, pulse, and colorimetry), determining the lifestyle of a patient (via passive IR sensors, inductive badges, piezoelectric sensors, and accelerometers), and environmental awareness (IR smoke alarm, microphone, and thermometer). Certain aspects of the communication network entirely lie within the user’s local environment: the Body Area Network (BAN) and the HomeLAN. The distributed intelligence of this system is based on smart sensors, the “body-hub”, smart therapy units, the user’s health records, and a Local Intelligence Unit (Chan et al, 2008). Smart House/Millennium Home The Smart House that was designed by Anchor Trust in England and British Telecom monitors the subject’s activity using interior IR sensors, a magnetic contact on the refrigerator door, and other magnetic contacts on the entrance doors for detecting when the user leaves or enters the house. The main lounge has a temperature sensor for monitoring the ambient temperature. Based on observations of the subject’s lifestyle, the smart house can trigger an alarm in case there is abnormal activity. This project is the basis for the Millennium Home, which is a means for supporting elderly people in the home. The key issue is developing a more sensitive and sophisticated approach to monitoring activity and behavior modeling, in order to control the alarms in a better way. Additionally, the Millennium Home offers the user with a fast and easy way to cancel false alarms and more rapid emergency response to real emergencies. The system also supports some of the independent living requirements of the elderly (Chan et al, 2008). The technical infrastructure consists of passive IR sensors for detecting movement, pressure sensors under the bed and chairs, and burglar alarm-style sensors on the front door and windows for detecting when they are opened. It also includes adjustable timers for reminding the user of medications, and temperature sensors to ensure the house does not get too warm or too cold for the inhabitant’s health. The system finally incorporates a computer-activated telephone, TV screen, loudspeaker, and an interactive dialogue system to enable communication between the care giver and the user (Chan et al, 2008). Dutch Model House A model house was constructed in Eindhoven in 1994 based on the Dutch Senior Citizens Label’s demands. Since 1995, other model houses have been developed in various cities in the Netherlands. The houses are likewise fitted with assistive and monitoring technologies. The resident’s activity is measured using motion detection sensors, which alert service providers in case of unusual inactivity and suspicious entry. The security system is completed by a button-based call system. The cooker, heating, and lighting are controlled by electronic actuators. The primary purpose of the project is to utilize information technology for facilitating communication among caregivers, the elderly, and service providers. Connection and Communication Techniques From a technical point of view, Home Automation, a key element of Smart Homes, is made up of five building blocks. These include devices under control (DUC), actuators and sensors, the control network, the controller, and remote control devices (Kyas, 2013). Devices under Control Devices under control include the entire component, such as consumer electronics or home appliances, which are controlled by and connected to the home automation system. Most of the component nowadays come with built in functionality (Z-Wave-interfaces, WLAN, Web-servers, Bluetooth, etc.), which enables the component to directly connect to the control network. Other components have to be fitted with adapters to be incorporated with the smart home infrastructure (Kyas, 2013). Actuators and Sensors Sensors control the home network. Sensors are available for a wide variety of applications such as measuring light, temperature, gas, humidity, liquid, and detecting noise or movement. Actuators on the other hand serve as the hands of the home network. They provide the means by which the smart network is actually able to perform things in reality. Based on the kind of interaction needed, there are electronic actuators like electric dimmers and switches or mechanical actuators like electrical motors and pumps (Kyas, 2013). Control Networks Control networks provide the connectivity between the devices under control, actuators, and sensors on the one hand and the remote control devices and controller on the other. Today, there are three major technology options for home and developing automation control networks. These include power-line communication, wireless transmission, and wire-line transmission (Kyas, 2013). All the three control networks – power line, wire line, and wireless based – have significantly improved in terms of transmission speed, interoperability, and reliability through standardization efforts in the course of the last ten years. Generally, control networks that are based on wireless transmission and power line communication are mostly dominant in residential home automation because of lower installation costs and component prices. On the other hand, wire line networks are more dominant in industrial building control applications and premium residential segments (Kyas, 2013). Controller The controller refers to the computer system that acts as the mind of the building automation system. It receives commands via remote control devices and collects information via sensors. It operates based on the commands or sets of predefined rules through actuators or communication means such as email, telephone, or loudspeakers. When it comes to residential home automation, the controller is typically an, always-on, embedded or standalone Windows/Linux/OS-X PC, running the control application for the home. Industrial and higher end buildings use dedicated high availability, redundant controller systems with uninterruptible power supplies (UPS) (Kyas, 2013). Remote Control Devices One of the most important reasons for the escalating acceptance of home automation systems within the residential segment is that, with the increasingly high presence of smartphones and hand held devices like tablets, the need for dedicated automation control devices has been eliminated. In the past few years, virtually every home automation system in the market has introduced tablet and smartphone based control applications. Additionally, advancement in voice recognition has eventually brought voice based control to smart homes. This is possible either by connecting to the controller via the control network, or via any other interface provided by the controller, like WLAN, the telephone network, or the internet. Therefore, using smartphones as home remotes makes the capability of remote building control through the mobile telephone network or the internet a feature that is available as a default (Kyas, 2013). Networking Technologies It is not surprising that, given the array of wireless and wired technologies that are currently available, there are numerous options to choose from. Some of the major technologies and alliances that facilitate home networking protocols include ZigBee, Z-Wave, Ethernet, HomePlug, HomePNA, HomeGrid/G.hn, wM-Bus, and Wi-Fi (Jalali & Arora, 2012). ZigBee ZigBee is a wireless mesh technology that was created as an open standard to address the special need of low-power and low-cost wireless machine-to-machine (M2M) networks. ZigBee uses digital radios that are based on the IEEE 802 standards for “personal area networks” with a particular focus on control, monitoring and sensor application. The technology is targeted at applications that need a low data rate, secure networking, prolonged battery life – as in electrical meters, wireless switches, smart energy, lighting control, health monitoring, HVAC control and so on. ZigBee’s success can be measured by the fact that more than 300 major manufacturers of semiconductors, OEMs, service companies and technology firms belong to the ZigBee Alliance Membership (Jalali & Arora, 2012). Z-Wave Z-Wave is a proprietary low-power wireless communications protocol that is designed for home automation, especially for remotely controlling applications in light commercial and residential environments. Currently, Z-Wave is supported by more than 160 manufacturers across the world and it appears in a wide range of consumer products in Europe and the US. Z-Wave applications and products fall into every imaginable area of control for light commercial and residential environments. These include security and HVAC control, lighting, automated window treatments, spa and pool controls, access and garage controls, enhanced demand management, home theaters, and enhanced demand management (Jalali & Arora, 2012). Ethernet To a large extent, Ethernet is the most deployed, most known and most trusted wired technology for local area networks (LANs). Nowadays, approximately all devices are Ethernet-enabled. Other than cost benefit and providing good Quality of Service (QoS), most Ethernet users are well aware of this and installation is not a problem. Newer installation does present problems with respect to wiring and cabling within the home. Additionally, it may fail to provide good QoS especially for multimedia applications (Jalali & Arora, 2012). HomePlug HomePlug technology offers the opportunity of using electrical wires to distribute broadband internet, digital music, smart energy applications, and HD video. The main benefit is that users can establish a network via the existing wiring in the home as the medium of communication. Adapters that are standalone modules that plug into wall outlets and offer one or more Ethernet ports are the most commonly deployed power line networking devices. Various specifications exist under the HomePlug family – the main ones being HomePlug GreenPhy which targets the Smart Grid market and HomePlug AV for applications like VoIP and HDTV (Jalali & Arora, 2012). HomePNA HomePNA alliance creates wired home networking specifications for the distribution of triple play data and entertainment over existing phone wires and coaxial cables. The technology is standardized by the ITU G.hn and it provides up to 320 Mbps of data rates with a guarantees QoS and remote diagnostics and management capabilities that enable service providers to meet as well as drive the increasing demand for new services like VoIP and IPTV. The technology was recently approved by DNLA for inclusion in its Networked Device Interoperability Guidelines (Jalali & Arora, 2012). HomeGrid/G.hn This forum supports the deployment of the netxt generation of home networking through in-home wiring (phone line, power line, and coaxial cable). HomeGrid’s goal is not to create a specification but to support the development of a specification in ITU-T G.hn. G.hn offers service providers the opportunity of deploying new offerings such as IPTV in a cost effective way. It also allows manufacturers of consumer electronics to offer powerful devices for connecting every kind of entertainment, security products, home automation, and smart energy management in the entire house (Jalali & Arora, 2012). wM-Bus Wireless Meter Bus refers to a radio variant of the special Meter Bus European standard for remotely reading electricity or gas meters. This technology fulfills the requirements of battery powered or remotely driven systems as well as consumer utility meters. Although it is suitable for various applications like alarm systems, heating control, and illumination installations, it fails to meet the requirements of large scale home networking technology (Jalali & Arora, 2012). Wi-Fi Home networking appliances and devices can use Wi-Fi wireless local area network connections by using technology under the 802.11 IEEE standards. The wireless network may be used to for communication between several electronic devices, to connect to the internet, or to communicate to the wired networks that use Ethernet technology. Although it has a high bandwidth, it may not be the most appropriate option for home networking particularly if the devices must be on full-time to transmit data to a central server. Additionally, it is expensive and has high power consumption, and could present issues regarding ‘electronic smog’ (Jalali & Arora, 2012). Review of the Boards Used Atmega88 This microcontroller is favored due to various reasons. First, it is fairly cheap and it is readily available off-the-shelf. It also offers a rather interesting connection set with for example five analogue-to-digital converters that can be used for light sensors, motion sensors, and accelerometers. It can also offer connections with USART serial communication devices which can be used for communicating with XBee and ZBee transceivers as well as SPI serial edge (used as a programming interface) lastly, the firmware can be C language with the free environment AVR studio. The microcontroller runs at 8 MHz (Oliver, 2008). Adruino Platform Adruino is an open source electronics prototyping platform that is based on flexible software and hardware. The Adruino is simple yet highly sophisticated device that is based on Atmel’s ATmega microcontrollers. The software on which Adruino runs is supported by Macintosh OSX, Linux OS, and Windows. This is an advantage because most microcontrollers are restricted by the fact that their software is limited to Windows operating system. The software language for the Adruino boards is based on AVR C programming language and it is expandable through C++ libraries (Luitel, 2013). Adruino Uno This is one of the microcontrollers manufactured under the Adruino and it is based on Atmel’s ATmega328 microcontroller. It is the most recent in a succession of USB Adruino boards which is the reference model for the Adruino platform. The board has a 16MHz ceramic resonator, USB connection, power jack, ICSP header, reset button, 6 analogue inputs and 14 digital inputs/output pins (of which 6 are usable as PMW outputs). The board uses Atmega 16U2 programmed as a USB-to-serial converter rather than FTDI USB-to-serial driver chip that was used in all the previous ceding boards. It has 32 KB memory with 0.5 KB utilized by boot-loader, 2 KB of SRAM, 1 KB of EEPROM and 16 MHz clock speed (Adruino, 2013). ATmega 328 This microcontroller is a low-power CMOS (Complementary Metal Oxide Semiconductor) 8-bit microcontroller that is based on the AVR improved RISC (“Reduced Instruction Set Computer”) design. The strong execution of instructions in a single clock cycle results in the achievement of 1 MIPS per MHz throughputs, which allows the designer to optimize power consumption versus processing speeds. The MCU (Microcontroller Unit) has 4K/8K bytes of in-system programmable flash with read/write aptitude, 256/412/1K bytes EEPROM along with the 512/1K/2K bytes of SRAM. Other features include 23 general purpose I/O lines and 32 general purpose working registers. It also has 3 flexible timer/counters with compare modes, a serial programmable USART, and internal and external interrupts. Additionally, the board has a byte-oriented 2-wire serial interface, a 6-channel 10-bit ADC (8 channels in TQFP and QFN/MLF packages) an SPI serial port, a programmable watchdog timer with internal oscillator and 5 software-selectable power saving modes (Atmel Corporation, 2013). Adfruit CC300 This microcontroller board comes with a Wi-Fi breakout that has an onboard ceramic antenna. It uses SPI for communication rather that UART which allows for faster or slower data transmission. It also has an interrupt system with IRQ pin that allows for asynchronous connections. It supports 802.11b/g, open/WEPWPA/WPA2 security, TKIP and AES, and built-in TCP/IP stack with a BSD socket interface. The TCP and UDP are available in both server and client mode, and allows for up to 4 concurrent sockets. It does not support AP mode. Although it can connect to an access point, it cannot act as an access point (Adafruit, 2014). References Adruino (2013). Introduction to Adruino. Retrieved from http://adruino.cc/en/Guide/Introduction Adruino. (2013). Adruino Board Uno. Retrieved from http://adruino.cc/en/Main/AdruinoBoardUno Atmel Corporation. (2013). ATmega328 Datasheet. Retrieved from http://www.atmel.com/images/doc816.pdf Chan, M. Esteve, D. Escriba, C. & Campo, E. (2008). A Review of Smart Homes-Present State and Future Challenges. Computer Methods and Programs in Biomedicine 91 (55-81). Groupe Speciale Mobile Association [GSMA] (2011). Vision of Smart Home-The Role of Mobile in the Home of the Future. Retrieved from http://www.gsma.com/connectedliving/wp-content/uploads/2012/03/vision20of20smart20home20report.pdf Ha, K.N. Lee, K.C. Lee, S. (2006). Development of PIR Sensor Based Indoor Detection System for Smart Home. Proceedings of the SICE-ICASE International Joint Conference, Bexco, Busan, Korea, October 18–21, pp. 2162–2167. Isoda, Y. Kurakake, S. & Nakano, H. (2004). Ubiquitous Sensors based Human Behavior Modeling and Recognition using a Spatio-temporal Representation of User States. Proceedings of the 18th International Conference on Advanced Information Networking and Application Jalali, S. & Arora, S. (2012). Technology Review and Trends in M2M Communication. Tata Consultancy Services White Paper. Kyas, O. (2013). How to Smart Home: A Step-by-Step Guide Using Internet, Z-Wave, KNX and OpenRemote. Key Concepts Press. Levy, D. Saadoun, O. Taga, K. & Riegel, L. (2012). Catching the Smart Home Opportunity: Room for Growth for Telecom Operators. Telecommunication, Information, Media & Electronics, Retrieved from http://www.adlittle.com/downloads/tx_adlreports/ADL_TIME_2012_Smart_Home_Opportunity.pdf Luitel, S. (2013). Design and Implementation of a Smart Home System. Helsinki Metropolitan University of Applied Sciences. Retrieved from http://www.theseus.fi/bitstream/handle/10024/66512/thesis.pdf?sequence Mihailidis, A. Carmichael, B. & Boger, J. (2004). The Use of Computer Vision in an Intelligent Environment to Support Aging-in-place, Safety, and Independence in the Home, IEEE Trans. Inform. Technol. Biomed. 8 (3) 238–247. Parks Associates (2012). Trends, Technologies, & Ecosystems: Evolution of the Digital Home. Retrieved from http://www.ospmag.com/files/pdf/whitepaper/Parks_Assoc_Evolution_of_the_Digital_Home_White_Paper.pdf Noguchi, H. Mori, T. & Sato, T. (2002). Construction of Network System and the First Step of Summarization for Human daily Action in the Sensing Room: Proceedings of the IEEE Workshop on Knowledge Media Networking (KMN’02). Oliver, L. (2008). Designing a Smart Home Environment Using a Wireless Sensor Networking of Everyday Objects. UMEA University Department of Computing Science. Retrieved from http://www8.cs.umu.se/education/examina/Rapporter/OlivierLaguionie.pdf Orpwood, R. Adlam, T. & Gibbs, C. (2001). User-centered Design of Support Devices for People with Dementia for Use in Smart House in Assistive Technology-added Value to the Quality of Life. IOS Press, 314-318. Robles, R. S. & Kim, T. (2010). Application Systems and Methods in Smart Technology: A Review. International Journal of Advanced Science and Technology, Vol. 15. 37-45. Williams, G. Doughty, K. & Bradley, D.A. (1998). A Systems Approach to Achieving CarerNet-An Integrated and Intelligent Telecare system. IEEE Trans. Inform. Technol. Biomed. 2 (1) 1 9. 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