Smart Home and Ambient Assisted Living Based on the Internet of Things

<p>One of the key components of the Internet of Things (IoT) is the Wireless Sensor Network (WSN). WSN is an effective and efficient technology. It consists of senor nodes; smart devices that allows data collection and pre-processing wirelessly from real world. However, issues related to power consumption and computational performance still persist in classical<br />wireless nodes since power is not always available in application like pipeline monitoring. Moreover, they could not be usually suitable and adequate for this kind of application due to memory shortage and performance constraints. Designing new IoT WSN system that matches the application specific requirements is extremely important. In this paper, we<br />present WiRoTip, a WSN node prototype for water pipeline application. An experimental and a comparative studies have been performed for the different node’s components to achieve a final adequate design.</p>

Section: Introductionmentioning

confidence: 99%

WiRoTip: an IoT-based Wireless Sensor Network for Water Pipeline Monitoring

Karray

Triki²,

Jmal³

et al. 2018

“…Digital technology, sensors, and learning algorithms are changing the way we interact with devices and appliances. Recent advances in miniature sensors, embedded processors, and wireless technologies have caused a rapid growth in smart applications such as controlling and monitoring home appliances [1]- [4], monitoring of smart parking lots [5], [6], automatic meter reading (AMR) using smart meters [7] etc. A microwave oven (commonly referred to as a microwave) is a kitchen appliance that heats and cooks food by exposing it to electromagnetic radiation in the microwave frequency range.…”

Section: Introductionmentioning

confidence: 99%

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

Khan¹

2018

When food is warmed in a microwave oven, the user guesses the estimated time for the heating. This cognitive process of guessing can be incorrect - resulting the final food temperature to be too hot or still cold. In this research, a novel closed-loop microwave oven is designed which automatically suggests the target temperature of a food by learning from previous experiences and the heating stops automatically when the food temperature reaches the target temperature. The proposed microwave captures and classifies the food image, and recommends the target temperature, thus the user does not need to remember the target food temperature each time the same food is warmed. The algorithm gradually learns the type of foods that are used in that household and becomes smarter in the recommendation. The proposed algorithm can recommend target temperature with an accuracy of 86.31% for solid food and 100% for liquid food. A prototype of the proposed microwave is developed using the embedded system and tested.

“…Detection the daily life activities of elderly residents (ADL) used Extended episodes of Discovery (xED) algorithm to obtain their periodicity and variability from several different datasets [8]. A Smart Home and Ambient Assisted Living (SHAAL) system developed and tested in a real experimental home experiment using wireless sensor network (WSN), also linked to the cloud network on the Internet for SHAAL system [9]. By making a monitoring system for integrated home network management based on the Internet of Things, a problem on existing home network systems is solved [10].…”

Section: Introductionmentioning

confidence: 99%

Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

Mukhlash

Yuanda

Iqbal

2018

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident's habits.