A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.

A Review of Smart Homes—Past, Present, and Future

Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported.

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

Prediction of thermal energy inside smart homes using IoT and classifier ensemble techniques

Smart home research requires study of psychological characteristics of home user. People follow some specific patterns in their life style. Inhabitant activity classification plays a vital role to predict smart home events. The 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.

Human activity classification for smart home: A multiagent approach

As the development of complex metal oxide semiconductor (CMOS) technology, fast low-power static random access memory (SRAM) has become an important component of many very large scale integration (VLSI) chips. Lot of applications preferred to use the 6T SRAM because of its robustness and very high speed. However, the leakage current has increasing with the increase SRAM size. It consumes more power while in standby condition. The power dissipation has become an importance consideration due to the increase integration, operating speeds and the explosive growth of battery operated appliances. The objective of this paper is to review and discuss several methods to overcome the power dissipation problem of SRAM. Low power SRAM can be produced with improvement in term of power dissipation during the standby condition, write operation and read operation. Discharging and charging of bit lines consumes more power during write ‘0’ and ‘1’compared to read operation. One of the methods to produce low power SRAM design is with make modification circuit at a standard 6T SRAM cell. This modification circuit will help to decrease power dissipation and leakage current. Several method was discussed in this paper for understand the method to produce low power design of SRAM cell. Recommendations for future research are also set out. This review gives some idea for future research to improve the design of low power SRAM cell.

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Advances on Low Power Designs for SRAM Cell

An AI based self-moderated smart-home

Prototyping of Smart Home: A Multiagent Approach

smart-home, multi-agent, pattern-matching, reinforcement-learning Future Smart-Home device usage prediction is a very important module in artificial intelligence. The technique involves analyzing the user performed actions history and applies mathematical methods to predict the most feasible next user action. Present a new algorithm of user action prediction based on pattern matching and techniques of reinforcement learning. Synthetic data had been used to test the algorithm and the re-sult shows that the algorithm presented in this paper performs 20% better than the current available techniques.

Smart home device usage prediction using pattern matching and reinforcement learning

Hardware simulation of home automation using pattern matching and reinforcement learning for disabled people

Smart-home conception has emerged in recent years and played a very important part in the formation of future houses. Since the beginning of the smart-home era, home automation benefits have never overshadowed the cost of such systems. One of these costs is that there is always the need for home inhabitants to program the system to perform daily tasks. In this paper we present prototype of a system that overcomes this problem by giving the home enough intelligence to adapt to its inhabitants life style without the need for the inhabitants to exercise authority. The system makes use of multi-agent and prediction techniques to provide intelligent smart-home appliances automation. Results indicate that the technique applied in this research not only makes the system very fast and accurate but also makes it portable and cost effective.

http://www.wseas.us/e-library/conferences/2006istanbul/papers/521-132.pdf

Awss Assim

Papers

An AI based self-moderated smart-home

Prototyping of Smart Home: A Multiagent Approach

Smart home device usage prediction using pattern matching and reinforcement learning

Hardware simulation of home automation using pattern matching and reinforcement learning for disabled people

Multiagent system for home automation