Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

In a world where wireless communications are critical for transferring massive quantities of data while protecting against interference, the growing possibility of financial fraud has become a significant concern. The ResNeXt-embedded Gated Recurrent Unit (GRU) model (RXT) is a unique approach precisely created for real-time financial transaction data processing. Motivated by the need to address the rising threat of financial fraud, which poses major risks to financial institutions and customers, our technique takes a systematic approach. We initiate the process with data input and preprocessing, addressing data imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). Feature extraction uses an ensemble approach that combines autoencoders and ResNet (EARN) to reveal critical data patterns, while feature engineering further enhances the model’s discriminative capabilities. The core of our classification task lies in the RXT model, fine-tuned with hyperparameters using the Jaya optimization algorithm (RXT-J). Our model undergoes comprehensive evaluation on three authentic financial transaction datasets, consistently outperforming existing algorithms by a substantial margin of 10% to 18% across various evaluation metrics while maintaining impressive computational efficiency. This pioneering research represents a significant advancement in the ongoing battle against financial fraud, promising heightened security and optimized efficiency in financial transactions. In defense against wireless communication interference, our work aims to strengthen security, data availability, reliability, and stability against cyber warfare attacks within the financial industry.

The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data’s privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users’ privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.

The major cause of death globally is heart disease and it is increasing day by day. It is difficult to diagnose the heart disease at early stages until a cardiac issue occurs. Huge amount of heart disease data is available in the health care sector such as in clinics, hospitals etc. However, this data is not intelligently handled to extract useful knowledge. Machine learning techniques help in turning this medical data into useful knowledge. Machine learning is used to design such decision support systems (DSS) that can learn and improve from their past experiences. Recently, deep learning has gained the interest of industry and academics. This research paper focuses on diagnosis of heart disease with high accuracy. In the proposed approach, Keras based deep learning model is used for the computation of results using dense neural network. The proposed model is tested with different combination of hidden layers in dense neural network, started from 3layers to 9layers. Each hidden layer uses 100 neurons and activation function as Relu. Multiple benchmark heart disease datasets have been used for the analysis. Individual as well as Ensemble models are evaluated on all heart disease datasets. Moreover, dense neural network is also evaluated on all datasets using accuracy, sensitivity, specificity and f-measure. Different combination of layers performed well on different datasets due to different categories of attributes. This extensive experimentation is performed to show the effectiveness of proposed model. The analysis of results shows that proposed model based on deep learning performed significantly better in terms of accuracy, sensitivity and specificity as compared to individual and other ensemble techniques on all heart disease datasets.

Fifth-generation (5G)-enabled vehicular fog computing technologies have always been at the forefront of innovation because they support smart transport like the sharing of traffic data and cooperative processing in the urban fabric. Nevertheless, the most important factors limiting progress are concerns over message protection and safety. To cope with these challenges, several scholars have proposed certificateless authentication schemes with pseudonyms and traceability. These schemes avoid complicated management of certificate and escrow of key in the public key infrastructure-based approaches in the identity-based approaches, respectively. Nevertheless, problems such as high communication costs, security holes, and computational complexity still exist. Therefore, this paper proposes an efficient certificateless authentication called the ECA-VFog scheme for fog computing with 5G-assisted vehicular systems. The proposed ECA-VFog scheme applied efficient operations based on elliptic curve cryptography that is supported by a fog server through a 5G-base station. This work conducts a safety analysis of the security designs to analysis the viability and value of the proposed ECA-VFog scheme. In the performance ovulation section, the computation costs for signing and verification process are 2.3539 ms and 1.5752 ms, respectively. While, the communication costs and energy consumption overhead of the ECA-VFog are 124 bytes and 25.610432 mJ, respectively. Moreover, comparing the ECA-VFog scheme to other existing schemes, the performance estimation reveals that it is more cost-effective with regard to computation cost, communication cost, and energy consumption.