Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability.

Internet of Things (IoT) applications are switching from general to precise in different industries, e.g., healthcare, automation, military, maritime, smart cities, transportation, logistics, and many more. In the healthcare domain, these applications have demonstrated an incredible improvement in patient assessment, monitoring, prescription, etc., with ease of access through the Internet. Despite its benefits, this technology also offers several security challenges for the research community and healthcare stakeholders, because of its wireless communication and open-area deployment. To explore, patient wearable devices and other networking entities follow unstructured communication format to share their accumulated data in the network, which makes them susceptible to manifold security threats. Considering the significance of these applications, data acquisition, processing, storage, and assessment on client and remote sides need a high standard of secure communication infrastructure. Therefore, security of these applications is one of the major obstacles that prevent their widespread use in different healthcare domains. To discuss different security constraints, in this article, we present a comprehensive survey of the theoretical literature from 2015 to 2023 to highlight the unresolved security problems of this emerging technology. Based on the evaluated literature pros and cons, we determine the security requirements and challenges of Healthcare-IoT (HC-IoT) applications. Following this, we demonstrate future research directions that could be useful for the researchers and industry stakeholders working in this domain. To demonstrate the uniqueness of this work and claim its contribution, we compare our work section-wise with previously published papers to answer the question of reviewers, editors, students, and readers, why this review article is required in the presence of already published review articles.

Unnamed Ariel Vehicle-assisted-Internet of Things (UAV-assisted IoT) applications have emerged as a powerful integrated technology, showcasing remarkable results in many domains with numerous advantages. However, this technology encounters several challenges, and security is one of them. Given that, authentication and verification of legitimate devices with data privacy pose key concerns in a wireless communication environment. Even though, the literature highlights the growing security threats of this technology, where attackers can easily compromise the existing authentication and data preservation schemes. Therefore, it is crucial for all involved stakeholders to address these concerns using Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) algorithms, as they offer cost-effective solutions. While some algorithms have been used in the literature to accurately and effectively predict, detect, and prevent vulnerabilities in this technology, they may not adequately handle modern or advanced security threats. Therefore, in this article, we provide a comprehensive survey of the existing literature from 2014 to 2022, specifically focusing on AI, ML, DL, and RL-enabled prototypes. Our goal is to highlight the contributions and limitations of the considered articles. Based on our observations, we will emphasize on the open challenges to set the stage for future research to enhance the security of this emerging technology. Moreover, to bridge this gap of all aspects of this technology, we will discuss layer-wise security threats and countermeasure schemes following the TCP/IP stack. Finally, we will compare our work with existing review articles to demonstrate its novelty, uniqueness, and potential usefulness for the people working in this field.

Noninvasive sensors capable of measuring weak biopotential signals, such as electrocardiogram (ECG) and EEG, and communicating results wirelessly to a host computer are developing rapidly. Some of them utilize capacitively coupled electrodes in an attempt to place the sensor at a distance from the skin. This article demonstrated the fabrication and development of a capacitively coupled ECG electrode prototype using custom high specific capacitance electrodes and custom high-performance electronics. Two ultrathin capacitive electrodes were fabricated on a flexible polyimide substrate (2 × 2 in) protected by a guard ring to reduce noise. The detection and amplification circuitry consisted of operational amplifiers (OpAmps) that filtered and conditioned the ECG signal. R-peaks in the ECG were readily detected and quantified using both simulated signals from ECG databases and real signals from human subjects. Heart rates and heart rate variability calculated from our monitor measurements were comparable with commercial rigid wearable sensors, including a smartwatch and an ECG monitor that uses standard clinical ionic electrodes. The prototype monitor was tested on human subjects during rest and moderate exercise and showed appropriate responses. The challenge of high-gain low-noise amplification was met by the development of highly thinned OpAmps whose operation was shown to be equivalent to commercially available rigidly packaged OpAmps, demonstrating that high-performance Si-electronics can be used to produce high-fidelity signals from weak biopotentials.

Machine Learning based data classification is a widely used data mining technique. By learning massive data collected from the real world, data classification helps learners discover hidden data patterns. These hidden data patterns are represented by the learned model in different machine learning schemes. Based on such models, a user can classify whether the new incoming data belongs to an existing class; or, multiple entities may test the similarity of their datasets. However, due to data locality and privacy concerns, it is infeasible for large-scale distributed systems to share each individual's datasets for classifying or testing. On the one hand, the learned model is an entity's private asset and may leak private information, which should be well protected from all other non-collaborative entities. On the other hand, the new incoming data may contain sensitive information which cannot be disclosed directly for classification. To address the above privacy issues, we propose an approach to preserve the model privacy of the data classification and similarity evaluation for distributed systems. With our scheme, neither new data nor learned models are directly revealed during the classification and similarity evaluation procedures. Based on extensive real-world experiments, we have evaluated the privacy preservation, feasibility, and efficiency of the proposed scheme.

In recent years, biometrics (e.g., fingerprint or face recognition) has replaced traditional passwords and PINs as a widely used method for user authentication, particularly in personal or mobile devices. Differing from state-of-the-art biometrics, heart biometrics offer the advantages of liveness detection, which provides strong tolerance to spoofing attacks. To date, several authentication methods primarily focusing on electrocardiogram (ECG) have demonstrated remarkable success; however, the degree of exploration with other cardiac signals is still limited. To this end, we discuss the challenges in various cardiac domains and propose future prospectives for developing effective heart biometrics systems in real-world applications.