Evolution of Smart Homes

Evolution of Smart al-QaidasI. IntroductionSmart collection plates, the next long leap in the field of station mechanization, have become an emerging research field in last few decades. Research on briskness homes has been gradually moving towards application of ubiquitous computing, tackling issues on kink heterogeneity and interoperability. A talented home adjusts its function to the inhabitants need according to the in markation it collects from inhabitants, the calculation agreement and the context 1.By 2050, approximately 20% of the world population will be at least 60 years old 2. This days group is more likely to suffer from long-term chronic diseases and will face difficulties in living independently. According to World Health Organization (WHO), 650 gazillion people live with disabilities around the world 3.The most common causes of disability include chronic diseases such as diabetes, cardiovascular disease and johncer injuries due to road traffic crashes, con flicts, falls, landmines, mental impairments, birth defects, malnutrition, HIV/AIDS and other communicable diseases. It is not possible and logical to offer all these patients in the aesculapian come to or nursing homes for an uncertain period of time. The theme is to accommodate health cargon expediencys and assistive technologies in their home milieu which is the main objective of wise(p) homes.Sensors, multimedia devices and physiological equipments be core components to apprehend information from home milieu Infrared (IR) sensing elements, pressure sensors, magnetic contacts, passive and progressive radio telephone receiver Frequency Identification (RFID) tags are used to jumper cable inhabitant jam mentionion. Electrocardiogram (ECG), photoplethysmograph(PPG), ,temperature, spirometry, galvanic skin response, colorimetry and pulse noticement equipments are used to get physiological information from the patient. Camera and microphones provide audiovisual resp onse from home exploiter. Inhabitant arsehole admittance the trunk through pompousness panel.Power line talk protocols are widely used for the connectivity of home public lavatorys. Public tele communication ne dickensrk with voice and text messaging service is gnarly to provide telecare facility from removed location. Videoconferencing is used as an inter dynamic communication media between caregiver and the client. TCP/IP protocols of Ethernet network provide information connectivity for topical anesthetic and remote sites and locations. Ethernet protocols are also used to connect health-monitoring equipments and to provide data repository service.Algorithms from apparatus learning, data compression, statistics and artificial knowing are employed to cry substance abuser behavior, detect activities of daily life (ADL) and location. C4.5 algorithm from machine learning is use to build spatiotemporal context of user. C4.5 algorithm is veritable by Quinlan in 1993 w hich classify the data to construct a determination tree according to data attri savees 47.Active LeZi from data compression algorithms is used to predict inhabitants next behavior. Active LeZi by Gopalratnam et al..in 2007 builds a decision tree utilizing similar methodology of LZ78 data compression algorithm and predict next event apply Prediction by un get along Matching (PPM) algorithm22.Statistical predictive algorithms like Bayesian filtering, dynamic Bayesian network algorithms classify the information and recognize ADL of home client344144. Different flavors of AI algorithms extended for unfermented home data processing. Markov model, Hidden Markov model, Artificial Neural Network rouse detect the living pattern of user and can also predict the user 7131438. Fuzzy Logic is used for home appliance control 36.Smart home is mainly dedicated to provide health care, safety, security and monitoring service for patient and elderly. The house is equipped with sensors, cameras t o track people and can trigger an alarm to a remote heath care service provider in the case of emergency. Sophisticated physiological devices monitor heart rate, blood pressure, dead body temperature, ECG record and the patient is being observed from a distance location. telecommunication service is used for communicating with service provider, relatives or neighbor and as a redundant ac acquaintancement method from the patient. For home comfort system, lighting, heating, doors, windows and home appliances are automatically controlled by ambient intelligence of intelligent home. Smart home also has significant contri hardlyion towards qualification conservation by desegregation of energy meter with snotty-nosed home 4.Home automation is the initial state of dexterous home where electronic technologies are used to provide an easy access to household devices. Rapid development of sensor technology accelerated the growth of smart home that involved more data processing. Improvem ent of information and communication technology make possible to develop easy and cost effective methods for data repository and ex transmute. Smart home is a festering concept, efficient and trim back cost solutions for general people are the main idea to promote it.II. Smart Home definationSmart home is an extension of modern electronic, information and communication technologies. The main objective of smart home research is to provide smartness to a dwelling facility for comfort, healthcare, security and energy conservation. Remote monitoring system is a common component of health smart home where telecommunication and meshing technologies are used to provide quick and proper medication to the patient from especial(a)ized assistance centre.The first formal definition of smart home was published by Intertek in 2003, which was involved to Department of Trade and Industry (DTI) smart-homes parturiency in UK 5. According to Intertek a smart home is a dwelling incorporating a comm unications network that connects the key electrical appliances and services, and allows them to be remotely controlled, monitored or accessed. A home needs three things to make it smartInternal network wire, cable, wirelessIntelligent control gateway to manage the systemsHome automation products within the homes and links to services and systems outside the homeIII. Review of Smart HomesSmart homes communicates are being conducted for last several decades and they convey different ideas, functions and utilities. It is growing to different brunches of specialization riveting the interest of the researchers and user requirements and expectations. This article is a weigh of the evolution of smart home according to time.Adaptive Control of Home Environment (ACHE) system is developed by Mozer in 1998 in USA. ACHE monitors user device usage pattern utilizing different personas of sensors and builds an adaptive inferential locomotive engine for neural network to control temperature , heating and lighting. ACHE can control three main components of a home while trying to maximize user comfort and conserve energy 7.ACHE is one of the early smart home rambles which is able to partially automate home environment via controlling lighting, temperature and heating components.CarerNet is an architectural model of integrated and intelligent telecare system proposed by Williams et al. in 1998. Its core components are sensor set, a sensor bus, intelligent monitoring system and a control unit. ECG, photoplethysmograph, spirometry, temperature, galvanic skin response, colorimetry, and pulse assessment tools used to collect physiological data. The communication network within the clients local environment is an integration of HomeLAN and personify Area Network (BAN) which is responsible to carry real-time data, event data, education and control data. It has a distri exactlyed intelligence system in the form of smart sensors, smart therapy units, body-hub, Local Intellig ence Unit (LIU) and Clients Healthcare Record (CHR). Home emergency alarm system, community health information and ambulatory monitoring service can be provided by the system. 8. CarerNet is an abstract model of health smart home and interconnecting components. No prototype of the model has been developed. Only a hypothetical case study is of an individual who had undergone brain surgery after suffering from a subarachnoid is discussed.Barnes et al. in 1998 have evaluated life style monitoring data of elderly using pedestal of British Telecom and Anchor Trust in England. The system detects inhabitants movement using IR sensors and magnetic contacts on the entrance of the doors. To measure temperature it uses a temperature sensor in the main living area. An alarm activation system is developed which detects abnormal behavior and communicates to remote telecare control center, the clients and their carers9. The researchers presented a lower cost solution for smart telecare. The limit ation of the system is it can identify exactly abnormal sleeping duration, unexpected in activity, uncomfortable home temperature and fridge usage disorder. Moreover, it uses a special new telecom protocol named No Ring Calling which demands modifying existing telecom protocols.TERVA is a health monitoring system developed in Finland by Korhonen et al.(1998). TERVA processes physiological information like blood pressure, heart beat rate, body temperature, body weight to draw graphical representation of wellness condition of the subject10.Research destination of the TERVA system is to develop a real time visual monitoring system but it is unable to provide long-term trend of certain physiological information. It cannot detect physiological problems and no assistive service is deployed to provide health care.The intelligent home (IHome) project at the University of Massachusetts at Amherst has developed an intelligent environment (Lesser et al.1999) IHome is a simulated environment designed with Multi Agent Survivability Simulator (MASS) and a Java Agent Framework (JAF) as tools to evaluate agent behavior and their coordination. The focus of the project is to model agent interactions and task interactions so that the agent can evaluate the tradeoff between robustness and efficiency 11.IHome is a simulation only solution, the project never build a practical smart home to evaluate their model.The Aware Home Research at the Georgia Institute of engineering science developed a smart home, which equipped with monitoring facilities to study human behavior (Kidd et al. 1999). To build a model of user behavior pattern, it uses smart shock to sense footsteps. Hidden Markov models, simple feature-vector averaging and neural network algorithms are applied on these data to create and evaluate conductal model 12. The aim of the project is to study user behavior, which is the primary stage of smart home research. The project never developed home intelligence which is a bi g shortfall of the research.The EasyLiving project at Microsoft Research base on intelligent environment to track ten-fold residents using distributed image-processing system (Krumm et al. 2000). The system can identify residents through active badge system. Measurements are used to define geometric relationship between the people, devices, places and things 1314.The system is workable in single room only and can track upto three peoples simultaneously.SELF (Sensorized Environment for LiFe), is an intelligent environment, which enables a person to maintain his or her health through self-communication (Nishida et al. 2000). SELF observes the persons behavior with distributed sensors invisibly em spotded in the daily environment, extracts physiological parameters from it, analyzes the parameters, and accumulates the results. The accumulated results are used for reporting useful information to maintain the persons health. The researchers constructed a model room for SELF consisted o f a bed with pressure sensor array, a ceiling lighting dome with a microphone and a washstand with display15.SELF describes a self-assessment system of human health but measuring only respiratory system and sleeping disorder, which is not sufficient to monitor health condition.The ENABLE project was set up in 2001 to measure the impact of assistive technology on the patient suffering from mild or moderate dementia (Adlam et al. 2004). The researcher installed two devices (cooker and night light) in the apartment of several patients in different locations to evaluate the efficiency of the system 16. The research scope is limited to only two household devices but to assist this type of patient the whole house must possess some kind of intelligence.Health Integrated Smart Home Information System (HIS) is an experimental course of study for home based monitoring (Virone.et al. 2002). IR sensors are used to track inhabitant activities and the information is transmitted via Controller Area Network (CAN) to a local data processor. The system generates alerts according to some predefine zones 17. The research is only limited to single inhabitant monitoring.In 2002, Guilln et al. developed a system composed of two parts home station (HS) and caregiver medical center (CMC) connected via integrated service digital network (ISDN) backbone. The home station is equipped with critical signs recording module to monitor physiological data like blood pressure, temperature, ECG, pulse oximetry. Caregiver medical center is like a call center designed specially with patient monitoring software. An interactive communication system between home and caregiver center is developed using videoconferencing technology 18. bet 1 shows functional modules of multimedia smart home. The system requires high Internet bandwidth for videoconferencing, which needs expensive equipments and high maintenance cost.Functional module of multimedia computer programme 18At University of Tokyo, Nogu chi et al (2002) designed an intelligent room to support daily life of the inhabitant. The system has three main components data collection, data processing and integration of processed data. The system learns current state of environment from sensors prone to bed, floor, table and switches. A summarization algorithm is used to track whatever changes in the system. The algorithm segments the collected sensory data at the points where sensor outputs changes drastically (i.e. pressure data appears suddenly or switch sensors are changed). It labels the segment with the room state. It joins a state of each segment to quantize the accumulated data and ties up the changed situation. The algorithm also tries to eliminate and reduces situations that changes slightly 19.The proposed summarization algorithm can detect user activities which is tested for single room only. No home automation method discussed utilizing the algorithm.MavHome (Managing an Adaptive Versatile Home) first introduce d by Das et al. in 2002 at the University of Texas, Arlington 20. Figure 2 describes MavHome architecture in brief. MavHome use multi disciplinary technologies artificial intelligent, multimedia technology, mobile computing and robotics. It is divided into four abstract layers physical, communication, information and decision. X10 protocol is used to control and monitor more than sixty X10 devices plugged into the home electric wiring system 21. Active LeZi algorithm is developed that makes a decision tree based on kth order Markov model and predict next action calculating probability of all actions applying prediction by partial matching method 22. Although MovHome utilize algorithms to make accurate prediction and decision, it only predicts the behavior of single inhabitant 23.concrete architecture of MavHome21The Rehabilitation Engineering Research midriff on engine room for Successful aging (RERC-Tech-Aging) at the University of Florida introduced House of Matilda (Helel et a l. 2003, 2005)24.The home is inhabited by a dummy called Mutilda. The main aim of this research is to perceive user location using ultrasound technology. After two years, in 2005 they designed the second generation of this home named GatorTech25. GatorTech is actually integration of smart device with sensors and actuators to optimize the comfort and safety of older peoples. The system is not user friendly because it requires wearable device for user tracking.In 2004, Mihailidis et al. developed a computer vision system in pervasive healthcare systems. The vision system consists of three agents sensing, planning and prompting. Statistics and physics based methods of segmenting skin color in digital images are used for face and hand tracing 26. Only hand and face tracing is not sufficient to make an efficient smart home system, the system should include body tracking and hand gesture reorganization.Multimedia Laboratories, NTT DoCoMo Inc. in Japan, has developed a system for modeling and recognizing personal behavior utilizing sensors and Radio Frequency Identification (RFID) tag (Isoda et al. 2004)27. C4.5 algorithm is used to construct decision tree from the data obtained from the sensors and RFID tags. The users behavioral context at any given minute of arc is obtained by matching the most recently detected states with previously defined task models. The system is an effective way for acquiring users spatiotemporal context but no intelligent system is developed for home appliances control.Andoh et al. in 2004 developed a networked non-invasive health monitoring system analyzing breath rate, heart rate, snoring and body movement. Researchers adopted Ethernet network for breath monitoring system implementation. The system can estimate sleep stages analyzing data using the algorithm developed for the purpose 28. The system cannot summarize long term observation of patients sleeping disorder.In 2005, Masuda et al. have developed a health monitoring arrangement u sing existing telecommunication system for home visit rehabilitation therapists. Researchers used an air filled mat to measure fanfare and respiratory condition. When the patient lies on the air mat, his heartbeat and respiratory movement cause significant change in air pressure inside the mat, which is measured by pressure sensor and analyzed by appropriate filtering process 29. The interesting part of the project is the usage of an air bag as monitoring equipment but its limitation is, it can only measure heart rate and respiratory condition.In 2005, Ma et al emphasized on context awareness to provide automatic services in smart home. They used case-based reasoning (CBR) to provide more appropriate services. CBR technique relies on previous interactions and experiences to find solutions for current problems. The system can adopt any manual adjustment done by modifying case data 30.This is the initial state of the project where few scenarios like AC, TV, lamp interaction is evalua ted. Their future plan is to make up more contexts and enrich the features of case tables.The House_n group at MIT designed PlaceLab a new living laboratory for the study of ubiquitous technologies in home environment (Intille et al. 2005). PlaceLab deployed with numerous wire, light, pressure, temperature water, gas, current sensors with video and audio devices to create vast amount of real life data from single volunteers as well as couples 31.The intent of the project is to study human behavior, influence of technology on the people and how technology can be used to simplify user interaction with home appliances. Their main contribution is an open online database of smart home sensor events and a well featured analyzing software 48.Researchers never implemented the study to build an free intelligent home.Yamazaki (2006) constructed Ubiquitous Home, a real-life test bed, for home context-aware service. It is a housing test facility for the creation of useful new home services b y linking devices, sensors, and appliances across data networks. Active and passive RFID tags located above the ceiling and at the entrance of the door are used to detect and recognize inhabitants. storm sensors are used to track user movement and furniture. The system is occupied with plasma panels, liquid crystal display and microphone for better interaction with the users. A network robot is employed to perform certain home services. Researchers concluded that the last of smart home is not to design an automated home but to develop an environment using interface technologies between human and the system 32.Although, the researchers installed enough sensors and interfacing devices , the system is only sensible to few task automations like TV program selection, cooking recipe display and forgotten property service.Ha et al. (2006) presents a sensor-based indoor location-aware system that can identify residents location. Researchers used an array of pyroelectrical Infrared (PIR) s ensor and proposed a framework of smart home location aware system. An algorithm is developed to process the information collected from PIR sensors for inhabitant location contracting. Their next step is to design an algorithm to determine location and trajectory of multiple residents simultaneously 33. The project in dedicated to user location detection system which is an essential part of smart home. No system is developed to provide intelligence to the house employing user location.In 2007, Rahal et al. at DOMUS laboratory, Universite de Sherbrooke, Canada, utilized Bayesian Filtering methods to determine location of the inhabitants. Bayes filters are efficiently used to estimate a persons location using a set of fixed sensors. In this method, the last known position and the last sensor event are both used to estimate a new location. The algorithm based on Bayesian filtering shows a mean localization accuracy of 85% 34.This project also deals with user location detection algorit hm, no home automation is developed using the processed information.De Silva et al. (2007) have implemented an audiovisual retrieval and summarization system utilizing multimedia technology for human behavior tracking. Using a large number of cameras a hierarchical clustering of audio and video handover used to create personalized video clips. An adaptive algorithm is used for complete and compact summary of the video retrieved. Basic audio analysis methods are applied for accurate audio segmentation and source localization. An interface allowed users to incorporate their knowledge into the search process and obtain more accurate results for their queries 35.The system can track people, extract key frame, localize sound source, detect lighting change but cannot distinguished different people.At Tampere University of Technology, Vainio et al.(2008) developed a proactive fuzzy home-control system. An adaptive algorithm applied to evaluate the test on obtained results. The goal of the research is to help elderly people live independently at home. Developed system can recognize routines and also recognize deviations from routines. The system can provide information to caregivers about living rhythm, sleeping disorders, and medicine taking of inhabitant 36. But the system works sensibly only for lighting control.In 2008, Swaminathan et al. proposed an object reorganization system using visual image localization and registration. Appliances are first registered in the image processing system. According to the voice command of the user, appropriate object is selected using an environmental map 27.It is actually a home automaton project using speech reorganization to receive user command and commands are executed to the objects which already known to the system.Growing Self-Organizing Maps (GSOM) used a self-adaptive neural network to detect and recognize activities of daily life address by Zheng et al in 2008 38 39. The GSOM follows the basic principle of the Kohone n self-organizing map with a special focus on adaptive architecture. The learning process of the GSOM is started by generating an initial network composed by four neurons on a 2-dimensional grid, followed by iteratively presenting training data samples. The system is tested in single room apartment for about two weeks where it can recognized user pattern of 22 distinct activities. Like other Self Adaptive Neural Networks (SANN), the system is depends on several learning parameters to be determined in advance such as initial learning rate and the size of the initial neighborhood. Other machine learning method must be utilized in parallel to determine optimum parameter for best performance.In 2008, Perumal et al. from Institute of Advanced Technology of University Putra Malaysia (UPM) have presented a design and implemented Simple Object Access Protocol (SOAP) based residential engagement for smart home systems appliances control 40. An appliance control module based on SOAP and web s ervices developed to solve the interoperation of various home appliances in smart home systems. fifteen feedback based control channels implemented with residential management system through Web Services. If the residential management system experiences server downtime, the home appliances can still be controlled using alternate control mechanism with GSM network via SMS Module locally and remotely. This system offers a complete, bi-directional real-time control and monitoring of smart home systems. No security mechanism is used to protect the web server from unauthorized access.Virone et al. present a dozens of statistical behavioral patterns obtained from an activity monitoring pilot study. The pilot study examined home activity rhythms of 22 residents in an assisted living environment with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their d eviations (Virone et al. 2008). The system cannot differentiate multiple inhabitants 41.Yoo et al. examined web-based implementation possibility of a central repository to integrate the biosignal data arrives from various types of devices in a remote smart home. Medical waveform explanation Format Encoding Rule (MFER) standard is followed for communicating and storing the biosignal data in ubiquitous home health monitoring system. The web-based technology allowed ubiquitous access to the data from remote location. The paper presents a common data format for all types of sensor (Yoo et al. 2008)42.Figure 3 describes functional architecture of web based data retrieval system. Information security, which is a burning issue for any web based system is not considered in this research.A web-based architecture for transferring the measured biosignal data from the u-House to the remote central repository.A snow-flake data model is designed by Zhang et al. in 2008 to represent the activitie s data in smart homes 43. Sensor data are stored in the homeML structure. A new algorithm is proposed on the prediction of class labels for variable person and activities of daily life (ADL) indicating who is doing what, given the observed episode and time information. true statement is calculated as the proportion of the number of correctly predicted class over the total number of episodes in the evaluation dataset. The learning output in the form of a joint probability distribution is also assessed by the distance to the true underlying probability distribution, using the Euclidean metric. The smaller the distance is, the proximate the learned model to the true situation. The algorithm is based on probabilistic distribution and able to predict ADL of more than one inhabitant. The result given is based on simulated data and the example shows only one task identification (making drink activities).In 2008, Park et al. proposed a method for recognizing ADL at multiple levels of deta ils by combining multi-view computer vision and RFID based direct sensor 44. A hierarchical recognition scheme is proposed by mental synthesis a dynamic Bayesian network (DBN) that encompasses both coarse-level and fine-level ADL recognition. Their methodology combines the two tracking technology. The system requires wearable RFID tag which is not comfortable for users.Rashidi et al. developed CASAS at Washington State University in 2008. CASAS is an adaptive smart home that utilizes machine-learning techniques to discover patterns in user behaviour and to automatically mimic these patterns. The goal is to keep the resident in control of the automation. Users can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests. In addition, CASAS can discover changes in residents behaviour patterns automatically. Frequent and Periodic action mechanism Miner (FPAM) algorithm mines this data to discover frequent and periodic activity pat terns. These activity patterns are modelled by their Hierarchal Activity Model (HAM), which utilizes the underlying temporal and structural regularities of activities to achieve a satisfactory automation policy. User can provide feedback on proposed automation activities, modify the automation policies, and introduce new requests 45.To make a system more interactive smart home should be equipped with voice reorganization facilities which is absent in this system.Raad et al. developed a cost-effective user-friendly telemedicine system to serve the elderly and disabled people. An architecture of telemedicine support in smart home that consists of web and telecom interface is considered in their research (Raad et al. 2008)46. This system also suffers from information security issues.PRIMA (Perception, recognition and integration for interactive environments) research group of the LIG laboratory at the INRIA Grenoble research center in France has defined a model for contextual learning in smart homes (2009). The authors developed a 3D smart environment consisting cameras, a microphone array and headset microphones for situation modeling. It relies on 3D video tracking and role detection process regarding activities of the person. Roles are learned by support vector machines (SVM). It is also capable to learn speed of the inhabitant and distance to the interacting object. Proposed system can identify situations like introduction, presentation, aperitif, game and siesta. Its error rate is truly high 49.Kim et al. developed a pyroelectric infrared (PIR) sensor based indoor location aware system (PILAS) in 2009.The system uses an array of PIR sensors attached with the ceiling and detects inhabitants location by combining overlapped detected areas. PIR sensors construct a virtual map of resident location transition. To improved accuracy, they applied Bayesian classifier using a multivariate Gaussian probability density function to determine the location of an inhabita nt. PILAS is unable to detect multiple residents 50.Wang et al. have developed a smart home monitoring and controlling system(2009). The system can be controlled from remote locations through an embedded controller. They have developed different GUI for mobile devices and PCs. distributively device has a unique address. A new command format to control the devices is introduced. It is a complex system and not compatible to previous smart homes architectures 51.Yongping et al. have developed an embedded web server to control equipments using Zigbee protocol (2009). For this purpose they used S3C2410 microprocessor which was programmed with Linux 2.6 kernel. To provider online access a small web server (only 60 Kbytes) named Boa is installed. An interface had also been designed to communicate with Zigbee module (MC13192).The system do possess any type of intelligence 52.Hussain el al. have developed inhabitant identification system using wireless sensor network (WSN) and RFID sensors (2009). The system can identify user location by the intensity of the Radio Signal Strength Indicator (RSSI) of WSN. A person is recognized by attached a RFID tag. The combined reading of RSSI signal and RFID receiver can successfully identify specific location of a resident in the home. The system is limited to single person tracking 53.At industrial Technology Research Institute (ITRI) in Taiwan, Chen et al.