Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

Alshammari, Talal; Ramadan, Rabie A.; Ahmad, Aakash

doi:10.3390/app13127326

Cited by 3 publications

(2 citation statements)

References 44 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset includes measurements of an indoor environment from 6 sensors (timestamp, light, temperature, humidity, pressure, and altitude) from July 2022, consisting of 8,910 real-time records [24]. Samples were captured every 5 min.…”

Section: Real Indoor Environmental Datasetmentioning

confidence: 99%

Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home

Alshammari

2024

Eng. Technol. Appl. Sci. Res.

Self Cite

View full text Add to dashboard Cite

The acceleration of house technology via the use of mobile phones has made it easier to control houses, where occupants (especially older people) spend most of their time. The climate of Saudi Arabia, especially in the northern area, is too hot during summer and cold during winter. Control of the indoor environment in a smart home is a preferable choice that can reduce power consumption to operate heating, ventilation, and air-conditioning. Machine learning algorithms have been used to predict physical variables of indoor environment, such as temperature and humidity. The model can be trained, learn, and make predictions using historical data. Machine learning techniques can automate temperature monitoring and control. This paper proposes an algorithm that combines Artificial Neural Networks (ANNs) and GridSearchCV to predict physical variables in indoor environments in Saudi Arabia. GridSearchCV was utilized to tune the parameters of the machine learning algorithm. The assessment of the proposed algorithm involved its performance comparison to state-of-the-art machine learning algorithms. A real-world dataset was generated to estimate the performance of the considered algorithms. The room data were collected every 5 min for 31 days during July 2022. The dataset contains 6 columns and 8,910 records from 6 sensors (timestamps, light, temperature, humidity, pressure, and altitude). Random Forest (RF), Decision Tree (DT), and ANN methods were compared with the proposed algorithm. The RF had the highest R2 value of 0.84 and the lowest Mean Square Error (MSE) of 0.43. The DT achieved an R2 score of 0.78, while the ANN achieved R2 score of 0.61, MSE of 1.04, and Mean Absolute Error (MAE) of 0.75. The proposed algorithm achieved an R2 of 0.69, MSE of 0.87, and MAE of 0.67.

show abstract

Section: Real Indoor Environmental Datasetmentioning

confidence: 99%

Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home

Alshammari

2024

Eng. Technol. Appl. Sci. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the context of the IoT, these sensors enable automated and continuous monitoring, providing real-time input that can be used for timely intervention and efficient resource management [25]. Deep and machine learning approaches use sensor data to improve prediction accuracy, fostering the development of intelligent systems capable of maintaining thriving indoor plant ecosystems, with broader implications for occupant well-being and indoor sustainability [26].…”

Section: Introductionmentioning

confidence: 99%

Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

Radočaj,

Rapčan,

Jurišić

2023

Horticulturae

View full text Add to dashboard Cite

Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates.

show abstract

Blockchain-Based Smart Farm Security Framework for the Internet of Things

Aliyu,

Liu

2023

Sensors

View full text Add to dashboard Cite

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.

show abstract

Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

Cited by 3 publications

References 44 publications

Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home

Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home

Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

Blockchain-Based Smart Farm Security Framework for the Internet of Things

Contact Info

Product

Resources

About