• International journal of advanced smart convergence
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• v.12 no.2
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• pp.108-116
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• 2023

Smart home and artificial intelligence technologies are developing rapidly, and various smart home systems associated with artificial intelligence (AI) improved the quality of living for people. In the present research, we examine the role of individuals' motivational system in their responses to the application of AI in smart homes. In particular, this research focuses on individuals' prevention motivational system and investigates whether individuals' attitudes toward the application of AI in smart homes differ according to their level of prevention motivation. Specifically, it is hypothesized that individuals with strong (vs. weak) prevention motivation will have more favorable attitudes toward the application of AI in smart homes. Consistent with the hypothesis, the results reveal that the respondents in the strong (vs. weak) prevention motivation reported significantly more favorable attitudes toward the six types of AI-based application in smart homes (e.g., AIbased AR/VR games, AI pet care system, AI robots, etc.). Our findings suggest that individuals' prevention motivational system may be an effective market segmentation tool in facilitating their positive responses to the application of AI in smart homes.

• Journal of the Korean housing association
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• v.27 no.6
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• pp.105-112
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• 2016

Although smart homes have been much developed in China, smart homes has been mainly towards the adoption of new technologies. There is little development of smart homes to consider and meet residents' needs in China. This study investigated residents' living in apartments in China using a questionnaire to identify their demands on smart homes. Through the survey, this study analyzed residents' space use patterns, daily living patterns etc. according to their ages. The results implied that there are significant differences in the use of spaces and demands on daily living within apartments. The results of this study should be considered for the development of smart homes in future. For example, it might be easier for people in the 20's to adopt Internet of Things (IoT) and environmental control systems compared to other age groups because most of them in the 20's use smart phones effectively without difficulties. In case of people in their 50's who stay home more times for taking a rest and eating meals compared to other age groups, smart technologies should be applied to support their health care and works in housings. This research emphasizing residents' experiences could be basis for the development of smart homes in China.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• Korean Institute of Interior Design Journal
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• v.25 no.6
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• pp.98-105
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• 2016

China has already entered the aging society and is predicted to become a super-aged society in 2020. The recent studies identified that the elderly has more interest in 'Aging-in-Place' which emphasizes deinstitutionalization since welfare facilities such as care homes and silver towns have separated the elderly from their local communities where they used to live in. The aim of this research is to propose a promising way for smart housing services who support the elderly's living in their homes, China. This research is to investigate the elderly's life and to identify their demands on housings for implementing such smart services. The elderly's living in apartments in Luoyang city, China, were investigated through interviews using a questionnaire survey. The results show that smart housing services should be provided to support the elderly's health, safety, leisure activities, comfortable living, and social relationships sustainably. In addition, such smart housing services should be intuitive since the elderly need to use easily smart services for their autonomous life in their homes. The smart housing services should be developed in the direction of enhancing the elderly's healthy and desirable life, and lessening their discomforts due to aging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1796-1816
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• 2020

The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home. Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa. This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables. We examine different models to obtain the best network, one that has higher assessment scores and a smaller size. Experimental evaluations of real datasets show the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.321-344
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• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.486-494
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• 2014

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.1-7
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• 2018

A smart-home is considered as one of most important alternatives of future homes with rising attention on IT technologies. The purpose of the study was to analyse the trend of smart-home technologies and to see how they reflect changing lifestyle needs. The research method includes a content analysis and a case study. The result shows that 'automation' functions are 35% of total. 'Health'(19%) and 'entertainment'(15%) functions are followed by 'Energy'(15%), 'Information'(11%). and 'relationship(6%).' This study is meaningful in that it examined smart-home technologies centering on the needs of residents rather than technological perspectives. Further researches on specialized smart-homes should be continued reflecting segmented needs of residents such as a medical home and a energy saving home etc.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.478-479
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• 2013

Smart home is one of the emerging domains to come up after advances in home appliances and automation technologies. There are many commercial solutions for smart homes yet many of them have yet to truly exploit the potential of private cloud for low level contextual services and ability to migrate to public cloud for more processing and storage. We propose a private cloud gateway for smart home which will have the ability to sense the new devices, ability to detect context of the situation and act in an appropriate way. It will also record the user logs which will be audited for improvement of the overall system.