Design and Implementation of an IoT Access Point for Smart Home

Chang, Chih-Yung; Kuo, Chin-Hwa; Chen, Jiancheng; Wang, Tzu-Chia

doi:10.3390/app5041882

Cited by 52 publications

(28 citation statements)

References 36 publications

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“…The ZigBee protocol is a low-power wireless transmission protocol, providing a suitable data rate for control and monitoring purposes of smart houses as a use case [27]. So, the ZigBee-based wireless communication network was used in the residential environment for the HEMS presented in this article.…”

Section: Methodsmentioning

confidence: 99%

“…The centralized HEMS, as a benchmark, continually collected data from each of the individual major household appliances every 20 s by talking to the installed ZigBee-based plug-load smart e-meters. ZigBee is an efficient short-range wireless technology in terms of power consumption and deployment scalability [27]. ZigBee Alliance [28] was formed in 1998 by Honeywell Corporation (Honeywell International Inc., Morris Plains, USA), whose aimed to use IEEE 802.15.4 low-power wireless network protocols as the basis for the development of the specification of IoT applications.…”

Section: Methodsmentioning

confidence: 99%

“…ZigBee Alliance [28] was formed in 1998 by Honeywell Corporation (Honeywell International Inc., Morris Plains, USA), whose aimed to use IEEE 802.15.4 low-power wireless network protocols as the basis for the development of the specification of IoT applications. The ZigBee protocol is a low-power wireless transmission protocol, providing a suitable data rate for control and monitoring purposes of smart houses as a use case [27]. So, the ZigBee-based wireless communication network was used in the residential environment for the HEMS presented in this article.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes

Lin

2018

Applied Sciences

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Smart cities are built to help people address issues like air pollution, traffic optimization, and energy efficiency. Electrical energy efficiency has become a central research issue in the energy field. Smart houses and buildings, which lower electricity costs, form an integral part of a smart city in a smart grid. This article presents an Internet of Things (IoT)-oriented smart Home Energy Management System (HEMS) that identifies electrical home appliances based on a novel hybrid Unsupervised Automatic Clustering-Integrated Neural-Fuzzy Classification (UAC-NFC) model. The smart HEMS designed and implemented in this article is composed of (1) a set of IoT-empowered smart e-meters, called smart sockets, installed as a benchmark in a realistic domestic environment with uncertainties and deployed against non-intrusive load monitoring; (2) a central Advanced Reduced Instruction Set Computing machine-based home gateway configured with a ZigBee wireless communication network; and (3) a cloud-centered analytical platform constructed to the hybrid UAC-NFC model for Demand-Side Management (DSM)/home energy management as a load classification task. The novel hybrid UAC-NFC model proposed in DSM and presented in this article is used to overcome the difficulties in distinguishing electrical appliances operated under similar electrical features and classified as unsupervised and self-organized. The smart HEMS developed with the proposed novel hybrid UAC-NFC model for DSM was able to identify electrical household appliances with an acceptable average and generalized classification rate of 95.73%.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes

Lin

2018

Applied Sciences

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“…IoT applications that provide the storage and processing capabilities of cloud computing to application developers and present the resulting insights to end users are enabled in the third layer of the IoT stack. Heterogeneity of IoT end devices with local communication networks can be realized; IoT end devices with their local communication networks can be homogenized for heterogeneous data sources . In this paper, edge computing rather than cloud computing is conducted from the second layer to the first layer of the IoT stack, incorporated into the first layer of the IoT stack, and applied for smart homes service (home energy management/DSM) of the third layer of the IoT stack.…”

Section: Proposed Architectural Design With Distributed and Embedded mentioning

confidence: 99%

Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand‐side management

Lin

2019

Int Trans Electr Energ Syst

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Summary Demand‐side management (DSM), as an energy audit, has the potential to reduce electricity costs and the carbon emission associated with the electrical energy used in modern society. With the high penetration rate of the Internet of things (IoT) paradigm in domestic environments, IoT‐oriented smart homes can skillfully control energy consumption of monitored domestic electrical home appliances and all other features related to domestic environments for home automation, home energy management/DSM, and home health care. While many of today's IoT devices take advantage of cloud‐centered analytics, IoT manufacturers and application developers are devoting themselves to empowering IoT devices to store sensed data and perform data processing via edge analytics at the edge of the Internet. In contrast to cloud‐centered analytics where (1) network connectivity is not always available or is limited and (2) real‐time responsiveness cannot be guaranteed, edge analytics (1) moves cloud‐centered analytics to IoT end devices and (2) guarantees real‐time responsiveness for local actionable insights. In this paper, a novel smart home system architecture that considers edge analytics capabilities is proposed. Additionally, an embedded flexible edge‐sensing device prototype for monitoring and remotely controlling electrical home appliances for home energy management/DSM is designed and implemented in the architecture. Energy efficiency is one of the central issues in smart homes. In this paper, a case study of autolabeling monitored electrical home appliances as smart home energy management is presented and used to experimentally validate the feasibility and efficiency of the proposed novel smart home system architecture facilitated with on‐site distributed and embedded flexible edge‐sensing devices.

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“…The basic concept of IoT is that various smart objects can be automatically linked into a network for interacting with humans through perception and networking technologies [1]. Smart objects in the IoT have the ability to send information through the Internet to provide the interaction among multiple things and people.…”

Section: Introductionmentioning

confidence: 99%

A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems

et al. 2017

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Ambient Assisted Living (AAL) has become an attractive research topic due to growing interest in remote monitoring of older people. Development in sensor technologies and advances in wireless communications allows to remotely offer smart assistance and monitor those people at their own home, increasing their quality of life. In this context, Wireless Acoustic Sensor Networks (WASN) provide a suitable way for implementing AAL systems which can be used to infer hazardous situations via environmental sounds identification. Nevertheless, satisfying sensor solutions have not been found with the considerations of both low cost and high performance. In this paper, we report the design and implementation of a wireless acoustic sensor to be located at the edge of a WASN for recording and processing environmental sounds which can be applied to AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, audio sampling and computation capabilities for audio processing. The proposed wireless acoustic sensor is able to record audio samples at least to 10 kHz sampling frequency and 12-bit resolution. Also, it is capable of doing audio signal processing without compromising the sample rate and the energy consumption by using a new microcontroller released at the last quarter of 2016. The proposed low cost wireless acoustic sensor has been verified using four randomness tests for doing statistical analysis and a classification system of the recorded sounds based on audio fingerprints.

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Design and Implementation of an IoT Access Point for Smart Home

Cited by 52 publications

References 36 publications

Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes

Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes

Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand‐side management

A Low Cost Wireless Acoustic Sensor for Ambient Assisted Living Systems

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