The rapid advancement of technology has set higher standards for the next generation of wireless communication networks, known as 6G. These networks go beyond the simple task of connecting devices and aim to establish a self-sustaining system within society. One of the key factors in achieving this goal is the integration of AI services and apps through the Internet of Things (IoT), which will be made possible with the support of 6G technology. The advancement of artificial intelligence (AI) will play a crucial role in enhancing the protocols, architectures, and operations of 6G networks. To achieve collaborative AI in IoT applications, Federated Learning (FL) has emerged as a popular method. FL enables AI training without the need for data sharing, ensuring privacy and security. However, FL also faces challenges, such as the presence of malicious data and the risk of single-point failure. To address these concerns, blockchain technology (BCT) offers a secure and efficient solution. By leveraging blockchain, these issues can be effectively tackled, providing a reliable framework for implementing FL-IoT applications.

Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after the first 20 weeks of pregnancy and is marked by proteinuria and hypertension. It can affect pregnant women and limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% of pregnancies worldwide are affected by hypertensive disorders during pregnancy. In this review, we discuss the machine learning and deep learning methods for preeclampsia prediction that were published between 2018 and 2022. Many models have been created using a variety of data types, including demographic and clinical data. We determined the techniques that successfully predicted preeclampsia. The methods that were used the most are random forest, support vector machine, and artificial neural network (ANN). In addition, the prospects and challenges in preeclampsia prediction are discussed to boost the research on artificial intelligence systems, allowing academics and practitioners to improve their methods and advance automated prediction.

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.

The progress made in Information and Communication Technologies (ICT) has played a crucial role in turning the Internet of Things (IoT) into a reality. IoT is an emerging technology that refers to networks of interconnected and Internet-enabled objects equipped with sensors, processors, and actuators that interact with each other to create significant collaboration and interaction environments. The field of education is one of the areas where IoT can be applied. However, the implementation of IoT poses security and privacy risks, such as unauthorized access, denial-of-service (DoS) attacks, and interference with wireless signals where IoT devices collect a significant amount of data, including user’s personal information like identity, location, and daily behavior. Therefore, it is crucial to protect users’ privacy in IoT applications. The innovative Ubiquitous Learning Environments (ULEs) have been created by ubiquitous computing technologies (mobile, wireless, network), which provide learners with learning experiences beyond the traditional classroom in both the real and virtual worlds. Ubiquitous learning (U-learning) is an emerging technology as a result of the tremendous technological revolution of ICT. U-learning is a novel learner-centered approach that aims to enhance learning, motivation, and creativity by utilizing innovative technology and IoT. U-learning enables learners to access the appropriate learning content, collaborate with the right learning partners, and engage in self-learning at the right time and place in a ubiquitous computing environment. To support learners in developing their social skills, in this study a framework for implementing the ULE based on the Internet of Things is designed, which consists of three main layers: perception, network, and application. The article explores the effects of IoT on education and how U-learning, which incorporates IoT to enhance learning experiences, has the potential to replace traditional classroom learning. Furthermore, the article addresses privacy preservation measures for different layers within the IoT environment and ULE. A framework for implementing the ULE model is in progress, which is a part of our future work.

Intelligent transportation systems (ITS) are pivotal in modern urban development by enhancing mobility and transit efficiency. However, the challenges of accurate short-term traffic prediction persist due to real-time traffic data’s dynamic, nonlinear, and non-stationary nature. In this study, the author addresses these challenges and proposes a novel approach to improve traffic flow prediction. Our research introduces a hybrid model, combining Long-Short Term Memory (LSTM) and the Kalman filter-based Rauch-Tung-Striebel (RTS) noise reduction technique, tailored to mitigate the limitations of low market penetration of connected vehicles and data availability. This paper aims to provide a robust traffic prediction solution with direct applications in smart cities and real-time traffic management. To evaluate our model’s efficacy, The author conducted an empirical case study using the Enhanced Next Generation Simulation (NGSIM) dataset, which offers highly granular vehicle trajectory data. The author rigorously analyses our methodology and algorithms to assess the quality of traffic predictions. Our results show that the proposed model outperforms traditional LSTM models. In particular, the Bi-LSTM/RTS model achieved a remarkable accuracy of 99% in predicting traffic patterns during sunny weather conditions, signifying a significant advancement in short-term traffic prediction accuracy. The evaluation metrics demonstrate that Bi-LSTM outperforms LSTM by a wide margin, with a coefficient of determination value of 0.99 against 0.97. The trend anticipated by Bi-LSTM (8.6%) is more in line with the actual trend than that projected by LSTM (8.9%), as seen by the smaller MAPE. In conclusion, Bi-LSTM outperforms LSTM at predicting traffic patterns.

The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs’ wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations.