A Multi-Agent Gamification System for Managing Smart Homes

Winnicka, Alicja; Kęsik, Karolina; Połap, Dawid; Woźniak, Marcin; Marszałek, Zbigniew

doi:10.3390/s19051249

Cited by 16 publications

(11 citation statements)

References 19 publications

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“…A home-based human-robot interaction (HRI) system object-tracking mechanism for non-GPS environments using an improved deep neural network object tracking system for applications in home robotics [49]. A multi-agent system for gamification between householders and also between families managing smart homes [50].…”

Section: Topic and Objective Technology And Feature Outcome And Futurmentioning

confidence: 99%

“…Winnicka, Kęsik, Połap, Woźniak and Marszałek [50] proposed multi-agent gamification that tends to assign tasks to family members. This system consists of environmental sensors, such as motion sensors and gas sensors, smartphone or smartwatch, camera, and machine learning algorithms, such as backpropagation for numerical values, or 'RMSProp' algorithm for graphical data.…”

Section: Artificial Intelligence and Indoor Systemsmentioning

confidence: 99%

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A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

et al. 2020

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This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

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Section: Topic and Objective Technology And Feature Outcome And Futurmentioning

confidence: 99%

Section: Artificial Intelligence and Indoor Systemsmentioning

confidence: 99%

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

et al. 2020

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“…Petnik et al describes the implementation of KNX technology for controlling and monitoring operational and technical functions in SHs within the IoT cloud platform [6]. The SHC platform and health care platform [7,8] are being prepared to use the IoT concepts within the fifth generation of the mobile network standard [9].…”

Section: Related Workmentioning

confidence: 99%

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

Vaňuš

Fiedorova

Kubíček

et al. 2020

Sensors

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The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

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“…Multi-agent systems offer an intriguing solution for managing the various devices and catering to the occupant’s needs. In [ 6 ], the authors describe a gamification approach, built on the multi-agent paradigm, for assigning and distributing tasks amongst occupants.…”

Section: Summary Of This Special Issuementioning

confidence: 99%