Bandar M. Alghamdi

The rapid increase in Internet of Things (IoT) devices has significantly transformed modern technological environments. However, this growth has also introduced numerous security vulnerabilities and various attacks. Traditional Machine Learning (ML) and Deep Learning (DL) have been intensively utilized in Intrusion Detection Systems (IDSs) implementation to detect and classify cyberattacks in an IoT environment. ML relies on a feature engineering approach for selecting optimal features, and DL requires high computational resources to achieve adequate classification accuracy. Therefore, this study proposes an IDS model implemented based on lightweight Bidirectional Encoder Representations from Transformers (BERT) models integrated with a meta‐classifier. This is accomplished by employing three lightweight models named DistilBERT, MobileBERT, and TinyBERT as base models, with their outputs combined by a Random Forest (RF)-based meta-classifier to achieve robust detection and classification accuracy while using a lightweight architecture. The proposed model was evaluated on three diverse IoT datasets: ToN_IoT, Edge-IIoTset, and X-IIoTID to demonstrate its effectiveness in detecting and classifying cyberattacks in IoT environments. In addition, the results were compared with existing benchmark IDS models. The experiments confirm the robustness of integrating lightweight BERT models with a meta-classifier for the IoT IDS field.

American scientific publishing group

This study presents a simulation-driven methodology for the design and optimization of a lightweight drone frame. Starting with a CAD model developed in SolidWorks, finite element analysis (FEA) and computational fluid dynamics (CFD) which are used to evaluate stress, deformation, fatigue behavior, and aerodynamic performance. Topology optimization is then applied to reduce non-critical material and enhance the stiffness-to-weight ratio. CFD-informed refinements further help to minimize drag and improve airflow uniformity. The final design is fabricated using fused deposition modeling (FDM) with PLA, enabling rapid prototyping and experimental validation. Future work will explore advanced materials to improve fatigue resistance and structural durability.

The Internet of Things (IoT) is an important component of the smart environment, which produces a large volume of data that is considered challenging to handle. In addition, the IoT architecture is vulnerable to many cyberattacks that can target operational devices. Therefore, there is a need for monitoring IoT traffic to analyze, detect malicious activity, and classify cyberattack types. This research proposes a security framework to monitor asymmetrical network traffic in an IoT environment. The framework offers a network intrusion detection system (NIDS) to detect and classify cyberattacks, implemented using a machine learning (ML) model residing in the middleware layer of the IoT architecture. A dimensionality reduction technique known as principal component analysis (PCA) is utilized to facilitate data transmission, which is intended to be sent from the middleware layer to the cloud layer with reduced complexity and fewer unnecessary inputs without compromising the information content. Therefore, the reduced IoT traffic data are sent to the cloud and the PCA data are retransformed to approximate the original data for visualizing the IoT traffic. The NIDS is responsible for reporting the attack type to the cloud in the event of an attack. Our findings indicate that the proposed framework has promising results in classifying the attack type, which achieved a classification accuracy of 98%. In addition, the dimension of the IoT traffic data is reduced by around 50% and it has a similarity of around 90% compared to the original data.

Smart electronic devices and telemedicine are widely used in humans' daily lives. This provides remote treatment of patients using information and communications technology. New telemedicine technologies, such as the Internet of Medical Things (IoMT), make it easier for medical and computing devices to communicate regularly and effectively. Critical motivations for adapting the IoMT are reduced cost, increased quality of life, and timely medical intervention. IoMT is significant because it enables continuous, real-time patient monitoring during routine everyday activities using a variety of wearables and sensors. With big data, the IoMT technology makes excellent use of Artificial Intelligence (AI) to support disease detection and health condition prediction, alerting patients and healthcare providers. Many research studies have been conducted to explore various aspects of IoMT and its applications in the real world. However, it is challenging to comprehend all the techniques and solutions proposed by the research community. Therefore, this survey sheds light on some crucial aspects of IoMT technologies and explores the potential research gaps and directions the research community could tackle. The survey examines and discusses the characteristics of IoMT standards, protocols, and types. It then delves into the layers of IoMT and distinguishes them into Fog and edge. The studies published under each type were explored, and the limitations of these works were highlighted. The research gaps and directions on IoMT approaches and technologies were also highlighted. With such findings and research directions, further research endeavors could be carried out to address the issues and existing limitations in the IoMT.

Studies have shown that in today’s urban areas, drivers lose a significant amount of time searching for available on-street parking spaces. Cruising drivers cause numerous problems, such as wasting gasoline and emitting gasses that lead to air pollution. To solve this issue, the parking industry and academia have made great efforts to lessen cruising drivers’ problems by providing on-street parking management solutions that can help enhance the efficient use of limited free on-street parking spaces. However, these solutions have two main limitations, scalability and privacy. This paper proposes a systematic literature review that examines the on-street parking management solutions that are currently in use, with a particular focus on their scalability and privacy limitations. According to the findings, there is a growing interest in on-street parking management solutions; however, the scalability of the systems used is a significant challenge since the servers that collect and manage parking availability have to perform intensive computation and communication with the drivers. Additionally, privacy concerns are a major issue, as the solutions often collect and store personal information such as drivers’ locations. The review concludes with recommendations for future research and development of these solutions to address both limitations and promote their widespread adoption.