Internet of Things for smart homes and buildings: Opportunities and Challenges

Ghayvat, Hemant; Mukhopadhyay, Subhas Chandra; Liu, Jie; Babu, Arun; Elahi, Eshrat; Gui, Xiang

doi:10.18080/ajtde.v3n4.23

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…The objective of an ambient assistance scenario incorporates smart home technology to enable elderly persons to live independently. Sensor activation-based smart home monitoring keeps caregivers updated about the inhabitant [ 18 , 19 ]. A usual concern for the caregiver is the ability to be immediately alerted and be available in case of any life threating conditions occur to the elderly.…”

Section: Introductionmentioning

confidence: 99%

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Ghayvat

Awais

Pandya³

et al. 2019

Sensors

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Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

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Section: Introductionmentioning

confidence: 99%

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Ghayvat

Awais

Pandya³

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Lampu LED digunakan untuk memberi peringatan pada saat berat beras hampir habis dan LCD digunakan untuk menampilkan informasi berat secara berkala dalam bentuk teks, seperti pada [3] dan [4]. Platform IoT yang digunakan adalah Thingspeak seperti yang digunakan dalam [5]- [8]. Platform ini dilengkapi dengan analisis program langsung menggunakan pemrograman matematis.…”

Section: A Implementasi Kotak Beras Cerdas Berbasis Iotunclassified

Rice consumption prediction using linear regression method for smart rice box system

Hanif

Abdurohman

Putrada

2020

Jurnal Teknologi dan Sistem Komputer

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Currently, the smart rice box has applied the Internet of Things (IoT) but without prediction of rice runs out which shows the amount of rice consumption. This study applies linear regression to predict the rice runs out in an IoT-based smart rice box and analyzes its performance. The prediction used the dataset obtained by measuring a smart rice box equipped with a load cell weight sensor and Hx711 module. The weight sensor accuracy was an RMSE of between 56 and 170 grams. The linear regression method applied to the smart rice box to predict rice running out has an MSE value of 0.2588 with a prediction window of 43 days. An R-squared value of less than one is obtained with a predictive threshold of 24 days.

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“…In addition, cities in Indonesia are more connected than the suburban areas in terms of speeds and devices. The pattern can be disputed as starting from the cities and spreading outwards to the suburban areas (Ghayvat, 2015). This can be attributed to the population in cities being very high hence the development and creation becomes a matter of necessity and no longer a luxury.…”

Section: Iot In Indonesiamentioning

confidence: 99%

An Examination of the Application and Measure of Iot in Indonesian Business Settings (Pengkajian Aplikasi Dan Pengukuran Iot Dalam Pengaturan Bisnis Indonesia)

Khan

2018

masy. telematika dan inf.

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This study seeks to how to measure the level of the Internet of things-based business services connectivity both in big cities and in sub-urban areas in Indonesia. Indonesia is yet a youthful market for IoT. However, administrator enthusiasm for this zone is developing. Telkomsel turned into the main administrator to market NB-IoT in the nation in March 2018, and Indosat Ooredoo has demonstrated that it has plans to move towards this innovation. This national report gives an outline of the Indonesian IoT showcase, including information on associations and income and an examination of the procedures of significant players. This study provides a review of the IoT market, related direction and nation designs as well as authentic information and conjectures for portable devices. Finally, it analysis key turning points and market elements. By examining all this, then it becomes easier to measure Iot since the variables become apparent. The first part of the paper introduces and defines what internet of things is by giving a background and evolution of the technology. It also highlights the readiness and application of internet of things among the people in the Indonesian region. In addition, it uses qualitative and quantitative approach to postulate as to how Iot can be measured. The first part is the background which entails a background information on Iot in Indonesia followed by the objectives of the study. This is followed by a hypothesis formulation, methodology, analysis and results of the research.

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Internet of Things for smart homes and buildings: Opportunities and Challenges

Cited by 21 publications

References 16 publications

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Rice consumption prediction using linear regression method for smart rice box system

An Examination of the Application and Measure of Iot in Indonesian Business Settings (Pengkajian Aplikasi Dan Pengukuran Iot Dalam Pengaturan Bisnis Indonesia)

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