Predicting the Temperature Dynamics of Scaled Model and Real-World IoT-Enabled Smart Homes

Ling, Jason; Zehtabian, Sharare; Bacanli, Salih Safa; Bölöni, Ladislau; Turgut, Damla

doi:10.1109/globecom38437.2019.9013312

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…The experimental data were collected using ScaledHome-2 [36,37], a 1:12 scale model of a suburban home intended to facilitate experiments that would be prohibitively expensive or even impossible to do with real-world homes. The initial ScaledHome was developed by Ling et al [38]. The sensors of the testbed capture temperature, humidity, and light in every room, measure the energy consumption, and track the solar power generation and energy storage capabilities (see Figure 1).…”

Section: The Scaledhome-2 Testbedmentioning

confidence: 99%

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Burns

Fichthorn

Ling

et al. 2021

Sensors

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In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available.

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Section: The Scaledhome-2 Testbedmentioning

confidence: 99%

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Burns

Fichthorn

Ling

et al. 2021

Sensors

Self Cite

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“…In this situation users like smart home users use the gateways nodes to get the actual knowledge/material through smart devices installed on the network. IoT based application becomes a requirement for people because they give responsive and dependable network which use to control isolated smart IoT in real world [18,30,47].…”

Section: Introductionmentioning

confidence: 99%

Effective modelling of sinkhole detection algorithm for edge‐based Internet of Things (IoT) sensing devices

2022

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The sinkhole attacks for Internet of Things (IoT) situations can overcome the network and interrupt communication. Sinkhole attacker nodes can advertise the best possible shortest path towards the destination, and once the normal node starts transferring the packets by the given path, the attacker node starts troublemaking the flow of the network. In this environment, the destination is unable to receive proper information or may not receive complete information. Sinkhole attacked network keeps the problems like the enhanced end-to-end delay, less throughput, and reduced packet delivery. Furthermore, it can affect other network constraints. So, it has become important to design an effective model to prevent the IoT environment from sinkhole attacks. In this article, an intrusion detection model is proposed to protect the IoT environment from sinkhole attacks. A model proposed by using rich resourced edge nodes to detect various kinds of sinkhole attacker nodes by exchanging messages. A well-known NS2 simulator is used for the practical implementation of the model. The proposed model attains more than 95% detection rate with a near about 1.4% false-positive rate, which seems better than previously given schemes. Finally, the proposed scheme is the appropriate match for a sensitive platform like a monitoring and security system.

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Section: Introductionmentioning

confidence: 99%

“…Ali AM, Shukor SAA, Rahim NA, et al 25 have developed a system that will store user criticisms about the comfort of the environment using the enhanced PMV-based model to intelligently control the air-conditioning system without compromising the users’ health. Ling J, Zehtabian S, Bacanli S, et al 26 described a technique using long short-term memory (LSTM) neural networks to predict the temperature in a smart home using sensor collection scattered across a home and also take smart actions that maximize the thermal comfort and reduce electrical energy consumption. On average, only 38% of individuals are satisfied with the thermal comfort of the environment as HVAC systems do not control the thermal environment at an individual level.…”

Section: Introductionmentioning

confidence: 99%

“…The auto-regressive with exogenous variables (ARX) model is used to determine the relationships between thermal comfort and collected parameters in order to estimate the PMV index, improving the mean absolute error and the complexity of the prediction when compared to other models of machine learning. 23,24,26,27 Khalil M, Esseguir M and Merguem-Boulahia L 24 presented a system that uses machine learning to learn and predict thermal comfort indoors according to air temperature, air velocity, mean radiant temperature, relative humidity and personal information such as rate metabolic and clothing insulation. Ali AM, Shukor SAA, Rahim NA, et al 25 have developed a system that will store user criticisms about the comfort of the environment using the enhanced PMV-based model to intelligently control the airconditioning system without compromising the users' health.…”

Section: Introductionmentioning

confidence: 99%

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Internet of things and machine learning applied to the thermal comfort of internal environments

Nascimento

Lopes

2022

Indoor and Built Environment

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The internet of things connects objects to the internet, enabling the dialogue between devices and users, providing new opportunities for applications, such as thermal comfort. In the research, adequate sensors were used to measure the heat index, the thermal discomfort index and the temperature and humidity index based on the temperature and relative humidity of a remote indoor environment. This research evaluated the level of thermal comfort in real-time using tools of storage, processing and analysis of big data information from the collection of IoT devices. With the analysis of the environment, it is possible to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms were also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment. Health researchers, for example, have the necessary knowledge to evaluate clinical data, but they are not used to using data analysis resources and machine learning algorithms. The platform was developed to reduce dependence on data experts and encourage healthcare researchers to develop their own models by automating the steps required for model development, using automated machine learning (AutoML).

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Predicting the Temperature Dynamics of Scaled Model and Real-World IoT-Enabled Smart Homes

Cited by 7 publications

References 13 publications

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

Effective modelling of sinkhole detection algorithm for edge‐based Internet of Things (IoT) sensing devices

Internet of things and machine learning applied to the thermal comfort of internal environments

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