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Technological advancements for diverse aspects of life have been made possible by the swift development and application of Internet of Things (IoT) based technologies. IoT technologies are primarily intended to streamline various processes, guarantee system (technology or process) efficiency, and ultimately enhance the quality of life. An effective method for pandemic detection is the combination of deep learning (DL) techniques with the IoT. IoT proved beneficial in many healthcare domains, especially during the last worldwide health crisis: the COVID-19 pandemic. Using studies published between 2019 and 2024, this review seeks to examine the various ways that IoT-DL models contribute to pandemic detection. We obtained the titles, keywords, and abstracts of the chosen papers by using the Scopus and Web of Science (WoS) databases. This study offers a comprehensive review of the literature and unresolved problems in applying IoT and DL to pandemic detection in 19 papers that were eligible to be read from start to finish out of 2878 papers that were initially accessed. To provide practitioners, policymakers, and researchers with useful information, we examine a range of previous study goals, approaches used, and the contributions made in those studies. Furthermore, by considering the numerous contributions of IoT technologies and DL as they help in pandemic preparedness and control, we provide a structured overview of the current scientific trends and open issues in this field. This review provides a thorough overview of the state-of-the-art routing approaches currently in use, as well as their limits and potential future developments, making it an invaluable resource for DL researchers and practitioners and it is a useful tool for multidisciplinary research. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]

This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities' various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitations against evolving attack strategies. Therefore, this study proposes an intrusion detection approach for a realistic IoT environment, employing various variants of deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The research tested three variants for each model: DNN1, DNN2, DNN3, CNN1, CNN2, CNN3, RNN1, RNN2, RNN3. All these variants are customized and tuned differently to analyze the efficacy of the suggested methodology. Likewise, notable observation highlights the significance of aligning training and validation accuracy curves that indicate controlled overfitting, validating the model's reliability in accurately predicting intrusion and benign network traffic in IoT settings. Results reveal that RNN1 achieves the best results: accuracy of 98.61%, precision of 98.55%, recall of 98.61%, and F1-score of 98.57% compared with other DNN and CNN architectures and benchmark papers. This study advances intrusion detection within IoT networks through a comprehensive evaluation of deep learning models and inspires ongoing research to enhance intrusion detection systems' resilience in dynamic IoT environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions. [ABSTRACT FROM AUTHOR]

In the relam of the Internet of Things (IoT), prevalence of missing data due to continuous data collection by smart devices necessitates the essential preliminary step of data imputation before engaging in information mining activities. IoT data exhibit robust interconnections in both spatial and temporal dimensions, surpassing the limitations of Euclidean space. Yet, prevailing machine learning and deep learning approaches often focus solely on temporal attributes or capture spatial features exclusively within a Euclidean framework. To address these challenges, this paper introduces a novel network named ST-Bi-LSTM-VAE (Spatio-Temporal Bidirectional Long Short-Term Memory based Variational Auto-Encoder). The architecture of ST-Bi-LSTM-VAE is primarily grounded in the Variational Auto-Encoder (VAE) framework. This innovative approach incorporates two distinct types of VAEs. The first type is dedicated to computing the adjacent matrix of the device network, a crucial input for the Graph Convolutional Network (GCN) essential in capturing intricate spatial relationships among devices. The second type of VAE is specifically tailored for data imputation, leveraging both global spatial and temporal dependencies. Empirical experiments conducted on diverse publicly available datasets substantiate the efficacy of ST-Bi-LSTM-VAE. The results obtained consistently demonstrate that proposed method surpasses baseline techniques in maintaining pattern, structure, and trend across datasets even at 50% missing gap for imputation task with 4.91% performance improvement in case of Intel Berkley Research Laboratory (IBRL) dataset and 3.5% on PRSA dataset. [ABSTRACT FROM AUTHOR]

In a variety of applications like military, healthcare, and industrial monitoring, Internet of Things (IoT) centered Wireless Sensor Networks (WSNs) are broadly utilized. The data is transmitted from the source Sensor Nodes (SNs) to the destination SNs. These are likely to be done in a secure way; also, in less time. In this paper, via proposing a Mutation grounded Multi-Layer Perceptron (MUMLP), energy and secure aware optimal routing are executed to provide secure data transmission along with reducing the transmission time. For Data Aggregation (DA), by proposing the Boltzmann Selection Probability-centric Gravitational Search Algorithm (BSP-GSA), the optimal Cluster Heads (CHs) are selected. These are done after node initialization in the network. After that, the non-cluster members are combined with an adjacent CH to form a cluster. The data are gathered by the CHs as of the non-cluster members; also, the gathered data, which are encrypted utilizing the Improved Elliptical Curve Cryptography (IECC) mechanism, are secured. Through the optimal route selected by a Deep Learning (DL) algorithm (MUMLP) with the consideration of a novel fitness function, the encrypted data is then dispatched to the Base Station (BS). For user access, the data in the BS is stored in a cloud server. The BlockChain (BC)-enabled authentication, which permits only the authorized user for data access, is executed to prevent unauthorized access. To evaluate the proposed approach's efficiency, simulations are performed. The outcomes display that when analogized to the prevailing mechanisms, the presented algorithm performs data transmission in a secure as well as energy-aware manner. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) applications have been helping society in several ways. However, challenges still must be faced to enable efficient and secure IoT operations. In this context, IoT profiling refers to the service of identifying and classifying IoT devices' behavior based on different features using different approaches (e.g., Deep Learning). Data poisoning and adversarial attacks are challenging to detect and mitigate and can degrade the performance of a trained model. Thereupon, the main goal of this research is to propose the Overlapping Label Recovery (OLR) framework to mitigate the effects of label-flipping attacks in Deep-Learning-based IoT profiling. OLR uses Random Forests (RF) as underlying cleaners to recover labels. After that, the dataset is re-evaluated and new labels are produced to minimize the impact of label flipping. OLR can be configured using different hyperparameters and we investigate how different values can improve the recovery procedure. The results obtained by evaluating Deep Learning (DL) models using a poisoned version of the CIC IoT Dataset 2022 demonstrate that training overlap needs to be controlled to maintain good performance and that the proposed strategy improves the overall profiling performance in all cases investigated. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a crucial role in monitoring and capturing information in various domains, including the Internet of Medical Things (IOMT). However, WSNs face significant security challenges, such as intrusion and potential malicious activities, due to their distributed and resource-constrained nature. This research provides a comprehensive analysis of the security challenges in WSNs enhanced by machine learning and deep learning technologies within the context of the IOMT. The research addresses these challenges by proposing practical countermeasures to mitigate security concerns in WSNs. It discusses the complexities, critical security issues, and vulnerabilities within WSNs, offering insights into potential fixes based on various approaches and theories. Furthermore, the integration of machine learning and deep learning in WSNs enables efficient communication, data analysis, and control, supporting areas like the IOMT, which includes monitoring prescription orders, tracking patients' movements, and remotely managing patients with chronic illnesses. By addressing the security challenges specific to WSNs in the IOMT environment, this research contributes to the advancement of secure and reliable wireless sensing systems in critical domains. The utilization of machine learning and deep learning technologies facilitates the development of robust methods for detecting and mitigating network attacks. The practical implications of the research findings are demonstrated through tangible examples within the IOMT context, emphasizing the potential impact on improving the security and reliability of wireless sensing systems. [ABSTRACT FROM AUTHOR]