Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Biogas is a renewable energy source that comes from biological waste. In the biogas generation process, various factors such as feedstock composition, digester volume, and environmental conditions are vital in ensuring promising production. Accurate prediction of biogas yield is crucial for improving biogas operation and increasing energy yield. The purpose of this research was to propose a novel approach to improve the accuracy in predicting biogas yield using the stacking ensemble machine learning approach. This approach integrates three machine learning algorithms: light gradient-boosting machine (LightGBM), categorical boosting (CatBoost), and an evolutionary strategy to attain high performance and accuracy. The proposed model was tested on environmental data collected from biogas production facilities. It employs optimum parameter selection and stacking ensembles and showed better accuracy and variability. A comparative analysis of the proposed model with others such as k-nearest neighbor (KNN), random forest (RF), and decision tree (DT) was performed. The study’s findings demonstrated that the proposed model outperformed the existing models, with a root-mean-square error (RMSE) of 0.004 and a mean absolute error (MAE) of 0.0024 for the accuracy metrics. In conclusion, an accurate predictive model cooperating with a fermentation control system can significantly increase biogas yield. The proposed approach stands as a pivotal step toward meeting the escalating global energy demands.

Bees, like other insects, indirectly contribute to job creation, food security, and poverty reduction. However, across many parts of the world, bee populations are in decline, affecting crop yields due to reduced pollination and ultimately impacting human nutrition. Technology holds promise for countering the impacts of human activities and climatic change on bees’ survival and honey production. However, considering that smart beekeeping activities mostly operate in remote areas where the use of grid power is inaccessible and the use of batteries to power is not feasible, there is thus a need for such systems to be energy efficient. This work explores the integration of device-to-device communication with 5G technology as a solution to overcome the energy and throughput concerns in smart beekeeping technology. Mobile-based device-to-device communication facilitates devices to communicate directly without the need of immediate infrastructure. This type of communication offers advantages in terms of delay reduction, increased throughput, and reduced energy consumption. The faster data transmission capabilities and low-power modes of 5G networks would significantly enhance the energy efficiency during the system’s idle or standby states. Additionally, the paper analyzes the application of both the discovery and communication services offered by 5G in device-to-device-based smart bee farming. A novel, energy-efficient algorithm for smart beekeeping was developed using data integration and data scheduling and its performance was compared to existing algorithms. The simulation results demonstrated that the proposed smart beekeeping device-to-device communication with data integration guarantees a good quality of service while enhancing energy efficiency.

Nowadays, building infrastructures are pushed to become smarter in response to desires for the environmental comforts of living. Enhanced safety upgrades have begun taking advantage of new, evolving technologies. Normally, buildings are configured to respond to the safety concerns of the occupants. However, advanced Internet of Things (IoT) techniques, in combination with edge computing with lightweight virtualization technology, is being used to improve users’ comfort in their homes. It improves resource management and service isolation without affecting the deployment of heterogeneous hardware. In this research, a containerized architectural framework for support of multiple concurrent deployed IoT applications for smart buildings was proposed. The prototype developed used sensor networks as well as containerized microservices, centrally featuring the DevOps paradigm. The research proposed an occupant counting algorithm used to check occupants in and out. The proposed framework was tested in different academic buildings for data acquisition over three months. Different deployment architectures were tested to ensure the best cases based on efficiency and resource utilization. The acquired data was used for prediction purposes to aid occupant prediction for safety measures as considered by policymakers.

Recently, the significance and demand for biogas energy has dramatically increased. However, biogas operators lack automated and intelligent mechanisms to produce optimization. The Internet of Things (IoT) and Machine Learning (ML) have become key enablers for the real-time monitoring of biogas production environments. This paper aimed to implement an IoT framework to gather environmental parameters for biogas generation. In addition, data analysis was performed to assess the effect of environmental parameters on biogas production. The edge-based computing architecture was designed comprising sensors, microcontrollers, actuators, and data acquired for the cloud Mongo database via MQTT protocol. Data were captured at a home digester on a time-series basis for 30 days. Further, Pearson distribution and multiple linear regression models were explored to evaluate environmental parameter effects on biogas production. The constructed regression model was evaluated using R² metrics, and this was found to be 73.4% of the variability. From a correlation perspective, the experimental result shows a strong correlation of biogas production with an indoor temperature of 0.78 and a pH of 0.6. On the other hand, outdoor temperature presented a moderated correlation of 0.4. This implies that the model had a relatively good fit and could effectively predict the biogas production process.