IoT based Appliances Identification Techniques with Fog Computing for e-Health

Kelati, Amleset; Dhaou, Imed Ben; Kondoro, Aron; Rwegasira, Diana; Tenhunen, Hannu

doi:10.23919/istafrica.2019.8764818

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…The advantage of the presented work is that the utility company can further improve the DR programs targeting specific consumers. In addition, appliance identification when coupled with user behavior can be used to provide better health services, particularly for the elderly [53,54].…”

Section: Discussionmentioning

confidence: 99%

Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System

Dhaou

2023

Electronics

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The demand response program is an important feature of the smart grid. It attempts to reduce peak demand, improve the smart grid efficiency, and ensure system reliability. Implementing demand-response programs in residential and commercial buildings requires the use of smart meters and smart plugs. In this paper, we propose an architecture for a home-energy-management system based on the fog-computing paradigm, an Internet-of-Things-enabled smart plug, and a smart meter. The smart plug measures in real-time the root mean square (RMS) value of the current, frequency, power factor, active power, and reactive power. These readings are subsequently transmitted to the smart meter through the Zigbee network. Tiny machine learning algorithms are used at the smart meter to identify appliances automatically. The smart meter and smart plug were prototyped by using Raspberry Pi and Arduino, respectively. The smart plug’s accuracy was quantified by comparing it to laboratory measurements. To assess the speed and precision of the small machine learning algorithm, a publicly accessible dataset was utilized. The obtained results indicate that the accuracy of both the smart meter and the smart plug exceeds 97% and 99%, respectively. The execution of the trained decision tree and support vector machine algorithms was verified on the Raspberry Pi 3 Model B Rev 1.2, operating at a clock speed of 600 MHz. The measured latency for the decision tree classifier’s inference was 1.59 microseconds. In a practical situation, the time-of-use-based demand-response program can reduce the power cost by about 30%.

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Section: Discussionmentioning

confidence: 99%

Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System

Dhaou

2023

Electronics

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“…165 It refers to the continuous power data collection at the edge layer, which then transmits the data to a gateway acting as a fog platform. 166 Moreover, NILM algorithms are processed at the gateway stage, where computing resources are available. • P3.…”

Section: Computing Platforms (P)mentioning

confidence: 99%

“…Fog computing architecture : Data preprocessing and NILM modules are implemented in the middle layer located between the edge and cloud layers 165 . It refers to the continuous power data collection at the edge layer, which then transmits the data to a gateway acting as a fog platform 166 . Moreover, NILM algorithms are processed at the gateway stage, where computing resources are available. P3.…”

Section: Applications Of Nilm and Its Commercial Perspectivesmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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“…In addition, a Hybrid Dynamic Quantization approach for finding out how to hash has been proposed; studies show that other quantization methods are beaten by DAQ [9]. Kelati et al [10] have discussed recent advances in metered energy usage knowledge in locally formed administrations. It also studies and analyzes interference, reliable existing, and effective force strategies that demonstrate stable load.…”

Section: Literature Studymentioning

confidence: 99%

Evaluation and Quality Assurance of Fog Computing‐Based IoT for Health Monitoring System

QingQingChang

Ahmad

Liao

et al. 2021

Wireless Communications and Mobile Computing

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Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare.

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IoT based Appliances Identification Techniques with Fog Computing for e-Health

Cited by 3 publications

References 31 publications

Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System

Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Evaluation and Quality Assurance of Fog Computing‐Based IoT for Health Monitoring System

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