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Most fire spread is caused by the absence of suppression means at the beginning of the fire. This results in the missed golden time. There are various factors that cause initial fires, such as electrical outlets, general distribution circuits, and oil–vapor–gas cluster spaces. In most cases, these places are out of reach of human hands or they lose the initial suppression time when a fire occurs, causing the spread of fire. This study implements an intelligent fire suppression device that connects sensor IoT based on microcapsules to secure initial fire suppression and golden time in the event of a fire in blind spots that cannot be seen by humans or at a time when it is difficult to recognize a fire. The microcapsule is a micro-collection unit that collects Novec 1230 gas generated in the semiconductor production process. The microcapsule is molded into a form with a fire suppression function and, when a fire occurs, the molded body explodes and absorbs ambient oxygen to suppress the fire. The complex-sensor IoT executes smoke and heat detection generated when a fire is suppressed within 10 s, which ensures the reliability of the detector by notifying of the fire and detecting the ignition point through communication linkages such as Ieee 485 and WiFi or LoRa. [ABSTRACT FROM AUTHOR]

The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a methodology that involves dataset preprocessing, including data cleaning, encoding, and normalization. The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique (SMOTE) and performing feature reduction using correlation analysis. Multiple ML classifiers, including Logistic Regression, multi-layer perceptron, Decision Trees, Random Forest, and XGBoost, are employed to model IoT attacks. The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset, which represents current imbalanced IoT scenarios. The results highlight that the XGBoost model, integrated with SMOTE, achieves outstanding attack detection accuracy of 0.99 in binary classification, 0.99 in multi-class classification, and 0.81 in multiple sub-classifications. These findings demonstrate our approach’s significant improvements to attack detection in imbalanced IoT datasets, establishing its superiority over existing IDS frameworks. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) has captured a significant amount of focus from the research field in recent years, with the rise of the Internet of Things (IoT) application. Anomalies, often known as outliers, are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data. The IoT which is described as a network of Internet-based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport (MQTT) protocol. Brute-force, Denial-of-Service (DoS), Malformed, Flood, and Slowite attacks are the most common in theMQTT network. One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures. For instance, if an attack is detected late, the loss of attack is irreversible. This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms. The new approach has been evaluated on Random Forest, Decision Tree, Naïve Bayes, Multi-Layer Perceptron, Artificial Neural Network, Logistic Regression, and Gradient Boost. The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering, primarily to determine the most relevant features in the MQTT dataset. This is, to the best of our knowledge, the first study on MQTTset that reduces the prediction time for DoS 0.92 (95% CI −0.378, 2.22) reduced to 0.77 (95% CI −0.414, 1.97) and for Malformed 2.92 (95% CI −2.6, 8.44) reduced to 0.49 (95% CI −0.273, 1.25). [ABSTRACT FROM AUTHOR]

The mining industry is crucial to the global economy. However, it is not one of the safest industries in which to work. The life of mining workers is constantly under risk. The goal of this work is to provide urgent assistance to the distressed miners. The miner's helmet can be improved more effectively by including a wireless sensor mote and other necessary sensors, which will aid in tracking the miner's health. This can result in the development of a network of wireless sensors. This wireless sensor network (WSN) can be used to track the well-being of all miners on the site. The routing protocol developed for this project is based on Distance Vector Routing (DVR) and determines the optimum path based on the shortest hop. The protocol is also robust in terms of failures. The nodes do not need to be synchronized. The protocol takes proactive measures to reduce delays. The helmet's mote tries to send a distress signal to its Room Manager via the pre-stored route whenever the load on the force sensor is greater than a threshold value. Officials from the Health Centre can send a medical team in response to a miner's distress alert via Room Manager. The miners in the adjacent rooms are also alerted so that they might volunteer to assist. The mote occasionally was unable to determine how to get to its room manager. A failed recovery procedure is carried out in these situations. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion. [ABSTRACT FROM AUTHOR]

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

Based on the limitations of water resource data acquisition and monitoring, such as technology and other problems, manual recording can only be used. The method has high cost, poor real time and low accuracy, and it does not play an important role in the prevention and control of flood disaster and water resources protection. On this basis, the method of combining Zigbee technology with General Packet Radio Service technology is proposed to collect and monitor the data of water resources. The water resources data acquisition and monitoring system designed in this paper is mainly composed of three parts: water resources data acquisition, coordinator part, and remote control center. The water resource data acquisition and monitoring system based on the Internet of things technology is simple in structure and convenient for installation and maintenance. It is suitable for remote area to collect and monitor water resources data, which overcomes the defects and shortcomings of the previous water resource data acquisition and monitoring system. In addition, it realizes the data collection of water resources and long-distance transmission and monitoring of data, which provides a theoretical basis for the work of water conservancy department. [ABSTRACT FROM AUTHOR]

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts. [ABSTRACT FROM AUTHOR]