Vehicular networks face increasing challenges to ensure security, privacy, and efficiency in dynamic communication environments.Current solutions often lack adaptability to evolving threats and efficient mechanisms for preserving privacy and reducing computational overhead.This study proposes PrivNet, a framework that integrates generative AI with advanced cryptographic and trust mechanisms to address these limitations.The framework comprises the Quantum-Augmented Holographic Cryptographic System (QAHCS) for dynamic and secure key generation, the Neural Overlap Privacy System (NOPS) for adaptive pseudonym morphing and entropy-driven identity obfuscation, and the Self-Supervised Generative Anomaly Detection (SS-GAI) module for real-time threat modeling and counter-anomaly injection.The system also incorporates Hyperledger Mesh for energy-efficient and secure transaction validation.Simulations were performed using NSL-KDD, CICIDS2017, and Car-Hacking / VeReMi datasets for 300 minutes.The results demonstrate a 12% improvement in detection accuracy, a 23% improvement in energy efficiency, and a 22% reduction in resource utilization.

Abstract Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.

The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies.

Traffic problems continue to deteriorate because of increasing population in urban areas that rely on many modes of transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches, noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities because it helps reduce overall traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with Convolutional neural networks (CNN) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a traffic image is fed to a CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce fuel use.

Abstract Human faces contain useful information that can be used in the identification of age, gender, weight etc. Among these biometrics, body mass index (BMI) and body weight are good indicators of a healthy person. Motivated by the recent health science studies, this work investigates ways to identify malnutrition affected people and obese people by analyzing body weight and BMI from facial images by proposing a regression method based on the 50‐layers Residual network architecture. For face detection, Multi‐task Cascaded Convolutional Neural Networks have been employed. A system is created to evaluate BMI along with age and gender from human facial real‐time images. Malnutrition and obesity are commonly determined with the help of BMI. In the previous works, height, weight, and BMI estimation through automatic means have predominantly focused on full‐body images and videos of humans. The usage of facial images for estimating such traits have been given less importance. In order to facilitate the analysis, the dataset is cleaned along with metadata containing information about the persons height, weight, age, and gender. Gender‐based analysis is performed for the prediction of BMI. Finally, an email containing the persons picture along with their details is sent to the concerned health officer.

With the exponential increase of interconnected communicating devices which make up the Internet of Things (IoT), securing the network transmission and the fifth generation (5G) systems which is the bedrock for IoT concept actualization is becoming more and more challenging. One of the major attacks which poses a great risk to data transmission is the eavesdropper (Eve) attack which occurs in both single input, single output (SISO), multiple input and multiple output (MIMO) systems. Thus, in this study, our focus is to establish a secured connection in a multiple-antenna transmission when the channel state information (CSI) of Eve is unknown to the network users. Our model comprises a secure wireless communication standard where Eve performs either optimal matched filtering (OMF) or a basic matched filtering (BMF) while the transmitting IoT node employs smart jamming strategy in order to compromise the activities of Eve. With respect to this and in attempt to realize maximum privacy, we examined the design of optimal jamming parameters. In the end, the numerical analysis of our investigation indicates that a substantial privacy advantage is achievable while utilizing only full-duplex jamming against using artificial noise from the transmitter only. However, a joint performance of both results shows a higher privacy improvement.

The industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issue of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this article is on establishing efficient privacy in an IIoT-multiple-input–multiple-output–multiple-antenna eavesdropping communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as optimal counter-eavesdropping channel approximation technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times the number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET’s QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET’s overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET’s QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches.

Summary Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

Relay assisted Device-to-Device (D2D) communications have received an increase attention due to its capacity to enhance D2D and mitigate interference constraints in cellular communications. This paper analyzes energy efficiency (EE) of internet of things (IoT) and D2D communications assisted with underlaying relay technique. An outage probability constraint was employed to improve coexistence of IoT-D2D and cellular communications, and to enhance cellular transmissions from redundant performance degradation and interferences. An energy allocation technique was formulated from the derived outage probability interference constraint. The results show that Relay-assisted technique in IoT-D2D transmissions will be maximized when the energy efficiency requirement is guaranteed. Furthermore, the simulation results confirmed the performance of the proposed technique on the loT-D2D communications.

Low power operation of smart sensors is an area of extreme importance when considering the emerging Internet of Things (IoT) and Industry 4.0 technology. This is essential when one considers how smartly nodes will be linked to the internet and how they are remotely monitored. At the transmission distance, each device interconnects with adjacent devices which are densely distributed in a dynamic network topology. Thus, the network can experience frequent failures to transmit data packet to the base station or any other intended receiving device due to limited resources. This paper proposes an energy efficient routing algorithm (EERA) for sensed-loT network that selects shortest but best route for reliable and efficient data packet transmission to the destination device based on ant colony optimization and key management technology (ACO-KMT) framework, motivated by natural ant performance. The simulation results show and demonstrate that the proposed EERA exhibits a better performance when compared to other algorithms such as long hop first scheduling algorithm (LHSA) and energy efficient secured routing (EESR) protocol. The proposed EERA also exemplifies an excellent performance in energy efficiency and data packet transmission efficiency for a different number of loT network devices in consideration.