Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

The Internet of robotic things (IoRT) is the combination of different technologies including cloud computing, robots, Internet of things (IoT), artificial intelligence (AI), and machine learning (ML). IoRT plays a major role in manufacturing, healthcare, security, and transport. IoRT can speed up human development by a very significant percentage. IoRT allows robots to transmit and receive data to and from other devices and users. In this paper, IoRT is reviewed in terms of the related techniques, architectures, and abilities. Consequently, the related research challenges are presented. IoRT architectures are vital in the design of robotic systems and robotic things. The existing 3–7-tier IoRT architectures are studied. Subsequently, a detailed IoRT architecture is proposed. Robotic technologies provide the means to increase the performance and capabilities of the user, product, or process. However, robotic technologies are vulnerable to attacks on data security. Trust-based and encryption-based mechanisms can be used for secure communication among robotic things. A security method is recommended to provide a secure and trustworthy data-sharing mechanism in IoRT. Significant security challenges are also discussed. Several known attacks on ad hoc networks are illustrated. Threat models ensure integrity confidentiality and availability of the data. In a network, trust models are used to boost a system’s security. Trust models and IoRT networks play a key role in obtaining a steady and nonvulnerable configuration in the network. In IoRT, remote server access results in remote software updates of robotic things. To study navigation strategies, navigation using fuzzy logic, probabilistic roadmap algorithms, laser scan matching algorithms, heuristic functions, bumper events, and vision-based navigation techniques are considered. Using the given research challenges, future researchers can get contemporary ideas of IoRT implementation in the real world.

With the rapid advancements of the internet of things (IoT), several applications have evolved with completely dissimilar structures and requirements. However, the fifth generation of mobile cellular networks (5G) is unable to successfully support the dissimilar structures and requirements. The sixth generation of mobile cellular networks (6G) is likely to enable new and unidentified applications with varying requirements. Therefore, 6G not only provides 10 to 100 times the speed of 5G, but 6G can also provide dynamic services for advanced IoT applications. However, providing security to 6G networks is still a significant problem. Therefore, in this paper, a hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks. Initially, multimedia data are encrypted by using the proposed model. Thereafter, the encrypted data are then transferred over the 6G networks. Extensive experiments are conducted by using various attacks and security measures. A comparative analysis reveals that the proposed model achieves remarkably good performance as compared to the existing encryption techniques.

Logistics is defined as the process of transporting, storing, and delivering goods from the producer to the final user. In today’s globalized world, logistics have become increasingly complex, making it imperative to address challenges related to data integrity, transparency, and secure storage. The incorporation of IoT devices in logistics allows for real-time monitoring of goods, vehicles, and environmental conditions. However, this generates vast amounts of data, which necessitates a reliable and secure data storage and management system. These aforementioned issues can be addressed by deploying a blockchain-based solution. Blockchain is an innovative technology that operates on a decentralized database system and it has different applications which include finance, healthcare, and so on. This research proposed a blockchain-IoT-based model for enhancing the logistics process. The proposed model utilized the Interplanetary file system for the secured and efficient storage of logistics data on a distributed and decentralized network and the SHA-256 hashing algorithm to ensure the anonymity of users’ private information. The model also establishes rules by using smart contracts, which increases efficiency. The performance evaluation of the proposed model was done based on the security, latency, cost, and throughput of the transaction. The experimental results and performance evaluation show that the proposed model is more efficient and secure than the existing blockchain-based systems. Additionally, the proposed model offers the real-time monitoring of goods while in transit. The proposed model offers the solution to the security, storage, and interoperability challenges in the IoT logistics system. It also provides recommendations to logistics stakeholders to adopt blockchain technology. Despite the implications, the limitation of this study is that it was tested in a controlled environment.

The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (p < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (p > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (${\chi }^2$ (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation.