A Smart Home Services Demonstration: Monitoring, Control and Security Services Offered to the User

Botticelli, Martina; Ciabattoni, Lucio; Ferracuti, Francesco; Monteriù, Andrea; Pizzuti, Stefano; Romano, Sabrina

doi:10.1109/icce-berlin.2018.8576259

Cited by 14 publications

(3 citation statements)

References 3 publications

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“…Several studies have been carried out that adopt a single approach to regulate an efficient measuring system required for residential homes. Some of those studies are similar to those discussed in this article, like [8] and [9] for example. Outlined in [10] is a HEMS that is capable of handling household appliances and lighting (through the use of consumption data collection).…”

Section: Related Literaturesupporting

confidence: 65%

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

et al. 2021

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Smart Energy Control Systems (SECS) has been increasingly implemented in the Smart homes scenario, due to the possibility of conditioning and controlling residential energy consumption, thus contributing to reducing energy losses and unnecessary electrical consumption. With the evolution of embedded systems in conjunction with the Internet of Things (IoT), Smart Outlet (SO), and devices that promote the Users Indoor Identification (UII) environments, they have assumed fundamental roles in the acquisition of data from electrical devices and also in the mapping of the individualized consumption of each resident throughout the day, providing essential information for SECS Systems as a way to assist in energy balance with minimal impact on the daily usability of electrical equipment. However, in most of the works that propose these types of assistance to SECS based on SO and UII, they have a massive implementation of sensors throughout the residence, misinterpretation of the data generated by the residents, difficulty in the identification of multiple residents. Thus, the present work proposes an evolution of an SECS architecture called SmartCom, with the implementation of accurate identification of electrical equipment through Near Field Communication (NFC)-based SO (data transfer between the appliance and the SO) and of multiple inhabitants through Wi-Fi handover using smartphones, with the least possible impact on the user's comfort, as well as in the building structure, achieving a rebalanced residential energy consumption 87.3% of the time it was used.

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Section: Related Literaturesupporting

confidence: 65%

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

et al. 2021

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“…Kecerdasan buatan banyak dimanfaatkan dalam berbagai bidang seperti smart garden (Humairoh Ratu Ayu et al, 2022;Serikul et al, 2018), smart home (Al-Kuwari et al, 2018;Botticelli et al, 2018;Hadwan & Reddy, 2016;Prasetyo et al, 2019), pengelolaan perpustakaan (Dewi, 2020), dan pengendalian sistem lalu lintas (Putra & Safrilah, 2021). Penerapan kecerdasan buatan tidak hanya pada bidang tersebut, namun lebih luas lagi, kecerdasan buatan dapat diaplikasikan untuk mendiagnosa penyakit seperti yang telah dilakukan (H. R. Ayu et al, 2021) yang mendeteksi penyakit pada daun singkong, dan (Russari, 2016) membuat sistem pakar untuk mendiagnosa penyakit batu ginjal.…”

Section: Pendahuluanunclassified

Workhsop Fundamental Artificial Intelligence dalam Kegiatan Penguatan Kompetensi Digital bagi Guru dan Siswa SMK Budi Karya Natar Lampung Selatan

Ayu

Junaidi

Pauzi

et al. 2023

Jur. Abd. Masy. Ind. (JAMSI)

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Pada era digital saat ini, kecerdasan buatan sangat populer dimanfaatkan dalam dunia kerja maupun industri. Salah satu upaya untuk beradaptasi dalam perkembangan teknologi yaitu dengan melaksanakan kegiatan workshop fundamental artificial intelligence (AI). Kegiatan ini terdiri dari pengenalan dasar dan sejarah perkembangan AI serta praktik membuat program deteksi wajah yang bertujuan untuk memberikan penguatan kompetensi digital kepada guru dan siswa SMK Budi Karya Natar sebagai mitra pengabdian agar dapat bersaing dengan mesin cerdas. Program deteksi wajah yang dibuat telah berhasil mendeteksi wajah menggunakan bahasa pemrograman python dan library opencv.

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“…With the help of a smart home system, homeowners can easily monitor home conditions and control household electronic products, such as lighting equipment, air conditioners, doors and windows, and sweeping machines. Smart homes have always been a hot topic, so many scholars are researching and developing a complete set of smart home systems, such as Botticelli et al (2018) proposed a smart home that can monitor environmental parameters, control doors and windows, and security services. The home system, system is equipped with sensors on the doors and windows.…”

Section: Smart Homementioning

confidence: 99%

Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi

Chen

Saravanarajan²,

Hung³

2021

Int. J. Appl. Sci. Eng.

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With the progress of the times and the rapid development of science and technology, machine learning and artificial intelligence are increasingly used in transportation, logistics, and homes. In terms of pets, pet monitoring has also become very popular in recent years. In this study, a real-time monitoring system for home pets using raspberry pie is developed. The proposed method consists of a raspberry Pi based YOLOv3-Tiny identification system for rapid detection and better boundary frame prediction of the cat behavior. Based on the YOLOv3-Tiny method, the following fine-tuning is implemented:(1) The images collected using the mobile phones are uniformly cropped to 416*416 pixels. (2) The images were captured in different periods and randomly rotated to plus or minus 20 degrees to make the dataset more robust for training. (3) One thousand four hundred pictures of the cat's movements in the room for marking and training are used. The dataset is also used to train the YOLOv3 model. According to the input image categories, the results of the output are categorized into six cat actions. They were sleeping, eating, sitting down, walking, going to the toilet, and search for a trash can. The average accuracy of both models was 98%. Based on environmental constraints like speed and memory consumption, any one of these models can be used for image recognition. The raspberry Pi system continuously captures the images and instantly sends a message to the registered mobile phone to achieve an instant preventive measure if the cat goes to the toilet for too long or flips through the trash can.

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A Smart Home Services Demonstration: Monitoring, Control and Security Services Offered to the User

Cited by 14 publications

References 3 publications

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

Workhsop Fundamental Artificial Intelligence dalam Kegiatan Penguatan Kompetensi Digital bagi Guru dan Siswa SMK Budi Karya Natar Lampung Selatan

Monitoring the behaviours of pet cat based on YOLO model and raspberry Pi

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