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The paper proposes a Wireless Sensor Network (WSN) solution for monitoring environmental parameters during industrial fires. The system includes sensors that collect real-time data on temperature ,humidity, and gas emissions. When the system detects a fire orgas out break ,it generates alerts and presents the data on a single webpage for easy visualization by first responders. The solution aims to provide near real time alerting, enabling prompt response to minimize the impact of industrial fires. The paper also presents experimental analysis of sensor node behavior during a fire in a test set-up, which demonstrated the effectiveness of the system in detecting fire outbreaks and generating alerts promptly. The proposed system has the potential to reduce the impact of industrial fires, including loss of human lives, vegetation, economic loss ,and environmental pollution.

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.