IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Franco, Patricia; Martínez, J.M.; Kim, Young-Chon; Ahmed, Mohamed A.

doi:10.1109/access.2021.3067029

Cited by 73 publications

(39 citation statements)

References 26 publications

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“…e system initialization setting and data backup and restoration operations are completed through this module. e system architecture of the core management system is a combination of two-tier structure and three-tier structure, and the interpenetration of the application layer and business processing layer is deeper [25][26][27]. e system adopts component technology for system construction, and the system components are divided into core (basic) components and extended components.…”

Section: Resultsmentioning

confidence: 99%

Optimization of IoT-Based Sporting Goods Consumer Service Management System

Bao-bin

2021

Complexity

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With the rapid development of computer technology and information industry technology, the use of these technologies has been deeply applied to the management of various industries. Data time-sharing transmission can solve the problem that the system is not affected by the network; that is, it makes good use of the network and is not bound by the network. Even when the network speed is low or the network is not smooth, the system of each store can run normally, and when the network is smooth, the data will be uploaded in packets and automatically received by the server. The system is well designed with functional modules’ management, including sports goods import and export management, inventory management, membership management, and ledger management. System operation can well solve the coordination work between the head office and stores; the head office can master the operation of each store, check the generated ledger at any time, form statistical reports, and guide the operation status of each store so that the stores can adjust their operation methods and learn from each other at any time. The system network management can make valuable data information aggregated, rather than single data. The head office can make overall data backup promptly to provide good logistic support for each store.

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Section: Resultsmentioning

confidence: 99%

Optimization of IoT-Based Sporting Goods Consumer Service Management System

Bao-bin

2021

Complexity

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“…In [18], the authors presented an ILM approach for load monitoring to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer.…”

Section: On Iot Based Approaches For Appliance Recognitionmentioning

confidence: 99%

“…Both datasets are different in structure and format. Based on the analysis performed in [18], a 10-feature vector showed to be more than enough to capture the appliance behavior and to make the classifier model distinguish one from the other. Therefore, a set of ten new features was formed.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…However, different requirements should be considered concerning data resolution, accuracy, real-time, and the number of appliances to be covered [8], [17]. In [18], authors presented an appliance and activity recognition system which operates in the application layer of an IoT architecture. Although this system achieved a high accuracy in the classification process, the procedure in the feature extraction limits the system implementation in practical scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for IoT Based Appliance Recognition in Smart Homes

et al. 2021

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Internet of Things (IoT) technologies will play an important role in enabling the smart grid achieving its goals in monitoring, protecting, and controlling by incorporating sensors, actuators, and metering devices while supporting various network functions and system automation. In this regard, home energy management systems (HEMS) enable households to better manage energy consumption by providing information about energy usage and allowing more precise control of major appliances. This work proposes a novel framework for IoT based appliance recognition in smart homes. It consists of two parts: training framework and inference framework. The proposed framework allows incorporating different loads in the monitoring system and enables selecting and testing specific parameters related to dataset configuration, feature extraction, and classifier model setting. The work contributes by developing an easy-to-use tool that allows customization of the training/prediction parameters according to the user criterion. Once the data and all its parameters are loaded, a novel feature extraction algorithm is used to obtain a total of ten statistical features. For the classification task, three machine learning models are included: a feed-forward neural network (FFNN), a long short-term memory (LSTM) and a support vector machine (SVM). In addition, the user can apply a set of techniques to overcome the class imbalance, and also measure the influence of the selected features in the classifiers' prediction by performing a feature importance analysis.INDEX TERMS Appliance recognition, frameworks, intrusive load monitoring, internet of things, smart grids, smart homes.

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“…Moreover, the ethernet connection was considered for data transmission, which is not feasible at all. The IoT-based intrusive load monitoring (ILM) had been proposed by Franco et al for activity recognition in smart homes [41]. As ILM requires a lot of sensors, it is not a cost-effective solution.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

Ahammed¹,

Hasan²,

Rahman

et al. 2021

IEEE Access

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In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using lowfrequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a dataacquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web application are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoTbased information exchange systems ensure the proposed system's applicability for efficient energy management.INDEX TERMS Non-intrusive load monitoring, Real-time load classification, IoT framework, Machine learning, Variation of supply voltage.

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IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Cited by 73 publications

References 26 publications

Optimization of IoT-Based Sporting Goods Consumer Service Management System

Optimization of IoT-Based Sporting Goods Consumer Service Management System

A Framework for IoT Based Appliance Recognition in Smart Homes

Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

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