An Implementation of Smart Dormitory System Based on Internet of Things

Lee, Wooyoung; Ko, Hwa-Mun; Yu, Jong Min; Sim, Kwee-Bo

doi:10.5391/jkiis.2016.26.4.295

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Because user intervention cannot be freely performed, it is difficult to change a device's status at the desired time. For example, sensors with low rates of use can lead to data waste and power loss when tasks are executed according to rules set by the developer without accounting for specific characteristics of a user [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Design of Smart Home Implementation Within IoT Natural Language Interface

Kim

Bae

2020

IEEE Access

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To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.INDEX TERMS Application, Internet of Things (IoT), sensor data, smart home system, SVM algorithm.

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Section: Introductionmentioning

confidence: 99%

Design of Smart Home Implementation Within IoT Natural Language Interface

Kim

Bae

2020

IEEE Access

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“…Currently, IoT is evolving into an intelligent system that connects networks to objects and helps people to communicate with each other [1][2][3]. This provides intelligent services to products or users as the system provides convenience [4]. To provide intelligent IoT services, it is essential to use sensors that collect data instead of human senses [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Automatic control system based on iot data identification

Kim

Park

Jung

2020

IJEECS

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<p>Recently, as the Internet of Things (IoT) technology has developed, researches are rigorously conducted to construct smart environments such as smart home, smart grid, and industrial IoT. However, currently existing systems consists of a series of events, and even if an existing task is running, unnecessary work still occurs as both works happen simultaneously. In this paper, we propose an automatic work control system to solve this problem. The proposed system transmits the data measured by the sensor to the server and identifies non - real - time tasks such as real - time work which is related to the dangerous situations, ventilation and temperature control. In addition, priority among the tasks is set in a way that existing tasks are stopped when high priority tasks occur. Accordingly, this can reduce the unnecessary waste of power, and the user is able to receive a proactive service.</p>

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“…In the past, it only supported inter-object communication via M2M (Machine To Machine), but it is evolving as a step to decide between the objects. It is also a step of recognizing and controlling the situation in the surroundings where the person is not aware [1,2,3,4]. To provide these services, it is necessary to use various sensors to measure environmental data.…”

Section: Introductionmentioning

confidence: 99%

User command acquisition based IoT automatic control system

Kang¹,

Song²,

Jung³

2019

IJEECS

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<p>Recently, researches are being conducted to build a smart environment using various sensors and devices and to provide customized services to users through IoT (Internet of Things). The existing IoT system operates the device only by using the threshold value. So, there are problems that device operates when a service that does not consider a user characteristic is provided or when user not necessary. In this paper, to solve these problems, the system collects the remote control commands and compares it with the value of device operation threshold and sets the changed threshold value. In addition, devices not registered in the server constitute the environment by linking with existing task. Therefore, it is possible to provide a customized service to the users.</p>

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An Implementation of Smart Dormitory System Based on Internet of Things

Cited by 6 publications

References 4 publications

Design of Smart Home Implementation Within IoT Natural Language Interface

Design of Smart Home Implementation Within IoT Natural Language Interface

Automatic control system based on iot data identification

User command acquisition based IoT automatic control system

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