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Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location. [ABSTRACT FROM AUTHOR]

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated Learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks. [ABSTRACT FROM AUTHOR]

With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks. [ABSTRACT FROM AUTHOR]