Species-specific isothermal nucleic acid amplification assay targeting Internal Transcribed Spacer (ITS) for rapid authentication of the medicinal crop Cirsium japonicum and Cirsium setosum in herbal markets.

Geographical indications (GIs) certify the link between coffee origin and quality, enabling premium pricing and protecting producers. However, the high market value of GI coffees increases their vulnerability to fraud, underscoring the need for reliable and practical authentication methods. Portable near-infrared (NIR) spectroscopy represents a rapid and non-destructive alternative, but its capability to discriminate Brazilian GI coffees requires systematic assessment. This study compared NIR transmittance spectra of aqueous coffee extracts with NIR reflectance spectra of the corresponding ground samples for the authentication of four Brazilian GIs from southeastern Brazil: Cerrado Mineiro, Mogiana Paulista, Mantiqueira de Minas, and Matas de Minas. Distinct spectral signatures were observed in the 900-1650 nm range. Data-driven soft independent modeling of class analogy (DD-SIMCA) was employed, resulting in excellent classification performance. Although both acquisition modes showed satisfactory performance during calibration and validation, reflectance consistently outperformed transmittance in the prediction of a test set, achieving accuracies ranging from 97% to 100%. The superior performance of reflectance-based models was attributed to the preservation of chemically informative features in the solid coffee matrix, including lipids and other compounds poorly extracted into water, whereas aqueous extracts were dominated by water absorption and exhibited greater intraclass variability. Portable NIR spectroscopy, particularly in reflectance mode combined with DD-SIMCA, provides a fast, non-destructive, and highly reliable approach for authenticating Brazilian GI coffees. These findings highlight its potential as a practical tool to protect producers and consumers against fraud and to ensure the integrity of products bearing protected geographical indications. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Meat adulteration poses serious economic, regulatory, and ethical challenges worldwide, creating an urgent need for rapid, on-site, and multiplex authentication methods. Although polymerase chain reaction (PCR) is the gold standard for nucleic acid analysis, its reliance on laboratory infrastructure and skilled operators limits field deployment, while current isothermal amplification platforms still lack sufficient automation and integration. Here, we present Magtect, a fully automated magnetofluidic system for rapid, multiplex identification of meat adulteration in sheep products. The system integrates magnetic bead-based nucleic acid extraction, ultrasound-assisted washing, magnetic array-guided bead distribution, and parallel multiplex recombinase polymerase amplification (RPA) detection within a single chip. By combining silicone oil with a thermally controllable wax isolation layer, Magtect enables physical spatial separation of multi-step reagents and on-demand mixing during heating, effectively preventing premature reagent contact and cross-interference. Notably, the wax barrier isolates the elution buffer from the RPA master mix, eliminating bead-induced amplification inhibition commonly observed in conventional magnetofluidic designs. Using this platform, adulteration of sheep meat with duck, chicken, or pork components can be fully automatically identified within 30 min. The system achieves a detection limit of 0.1 copies/μL and reliably detects adulteration levels as low as 1%. These results demonstrate that Magtect provides a robust, sensitive, and field-deployable solution for on-site meat authenticity verification, representing a significant step forward in automated food integrity monitoring.

Cloud computing has emerged as a fundamental technology for delivering scalable, flexible, and costefficient services, with REST (Representational State Transfer) APIs acting as the primary communication interface between clients and cloud platforms. Despite their advantages, REST APIs are increasingly targeted by cyber threats such as unauthorized access, data breaches, injection attacks, and denial-of-service attacks. These vulnerabilities are often caused by improper implementation of security mechanisms, lack of standardization, and the dynamic nature of cloud environments. This paper focuses on evaluating and checking the security properties of cloud service REST APIs to ensure secure data exchange and reliable system performance. The key security properties analyzed include authentication, authorization, confidentiality, integrity, and availability. The study reviews existing systems and identifies critical limitations such as weak authentication methods, insufficient monitoring, and lack of automated security testing. To overcome these challenges, a comprehensive security framework is proposed that integrates modern security techniques such as OAuth 2.0, JSON Web Tokens (JWT), HTTPS/TLS encryption, API gateways, and automated vulnerability assessment tools. Additionally, machine learning-based anomaly detection is incorporated to identify suspicious activities and potential threats in real time. This multi-layered approach enhances the overall security posture of REST APIs in cloud environments.

Iris biometric systems are recognized as secure alternatives to conventional authentication methods, yet challenges such as variable illumination, noise, and intricate iris textures persist. To address these issues, our study presents a novel hybrid iris recognition framework that integrates advanced deep learning with a pioneering application of Multivariate Ensemble Empirical Mode Decomposition (MEEMD) for feature extraction—a method not previously applied in this context. Our framework first employs MEEMD to extract statistical features that capture the iris’s nonlinear and nonstationary variations. We then combine global semantic information from two pretrained convolutional neural networks—VGG16 and ResNet-152—with local micro-texture details encoded by Local Binary Patterns (LBP) to form a comprehensive feature representation. An efficient pre-processing and segmentation stage precisely isolates the iris region, and the resulting features are refined through dimensionality reduction techniques to yield a robust, compact representation. These features are subsequently classified using multiple models, each rigorously tuned via hyperparameter optimization. Experimental validation on benchmark datasets—including IITD, CASIA, and UBIRIS.v2—shows that our model achieves recognition rates of up to 98% on IITD, 97% on CASIA, and 97.30% on UBIRIS.v2, surpassing existing approaches. This work not only enhances iris recognition performance but also establishes a novel method that bridges advanced deep learning with innovative feature extraction for high-security applications.

Face recognition has become a widely adopted biometric authentication method due to its convenience, non-intrusiveness, and ability to authenticate users in realtime. However, the widespread adoption of face recognition systems has also led to a surge in spoofing attacks, where attackers attempt to bypass security by using printed photographs, digital images, replayed videos, or even 3D masks. Traditional face recognition algorithms are unable to differentiate between genuine live faces and these spoofing attempts, which poses a critical risk to security systems in banking, mobile authentication, border security, and other sensitive applications. Therefore, ensuring that the detected face is live, a process known as face liveness detection, has become a crucial requirement for secure biometric authentication. Recent advancements in deep learning have provided powerful tools for solving complex image-based recognition tasks. Among these, Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in image classification, object detection, and feature extraction tasks due to their ability to learn hierarchical features from raw images without manual feature engineering. This paper proposes a CNN-based approach for face liveness detection, designed to distinguish between live and spoofed faces with high accuracy and robustness under varied conditions, including different lighting environments, backgrounds, and facial orientations. The proposed system leverages CNN’s hierarchical feature extraction to capture subtle differences between real and fake faces that are often imperceptible to human observation, such as texture inconsistencies, reflection patterns, and pixel-level variations. The proposed method was evaluated using widely recognized datasets, including CASIA-FASD and Replay-Attack, which provide diverse scenarios and various types of spoofing attacks. Preprocessing steps include face detection, resizing, and normalization, along with data augmentation techniques such as rotations, flips, and brightness adjustments to improve the model’s generalization. The CNN architecture consists of multiple convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. The model is trained using the Adam optimizer with categorical crossentropy loss, and its performance is evaluated in terms of accuracy, precision, recall, and F1-score.

Quantum computing challenges the long-term security assumptions of blockchain systems that rely on classical public-key cryptography, motivating the adoption of post-quantum cryptography and quantum key distribution (QKD). This review maps research fronts at the intersection of blockchain and quantum-safe security, linking threat assumptions to post-quantum mechanisms, blockchain layers, and QKD positioning. Records were retrieved from Scopus and Web of Science using a two-block query and filtered through a PRISMA-guided workflow for bibliometric mapping. The final corpus comprises 648 journal articles and shows accelerated publication growth after 2023, with scientific production concentrated in a small set of leading countries. Keyword structures indicate that IoT-centric deployments dominate the semantic backbone, where authentication and intelligent methods co-occur with blockchain security primitives, while post-quantum and privacy-preserving constructs form a cohesive technical stream. QKD appears as a distinct but more specialized theme, typically discussed at the system level and shaped by infrastructure and scalability constraints. Overall, the literature is moving from conceptual risk articulation toward engineering integration; however, progress is limited by inconsistent reporting of threat models, post-quantum parameter sets, and ledger-level cost trade-offs, highlighting the need for auditable and reproducible evaluation.

The increasing reliance on web applications across domains such as e-commerce, banking, healthcare, and enterprise systems has significantly amplified the importance of secure and reliable authentication mechanisms. Traditional password-based authentication methods are increasingly vulnerable to modern cyber threats, including phishing, brute force attacks, and credential stuffing, thereby necessitating the adoption of more advanced and robust authentication strategies. This study presents a comprehensive comparative analysis of modern authentication mechanisms in web applications, including Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), and biometric-based authentication systems. The research evaluates these mechanisms based on critical parameters such as security strength, usability, scalability, performance, and implementation complexity. It further examines architectural models, token-based frameworks, and federated identity systems that support secure user authentication in distributed environments. Additionally, the study reviews common vulnerabilities, threat mitigation techniques, and real-world deployment scenarios to assess the effectiveness of each approach. The findings highlight a growing shift toward hybrid authentication models that integrate multiple techniques to achieve enhanced security and user experience. However, challenges remain in balancing security with usability, ensuring interoperability across platforms, and maintaining privacy in decentralized systems. The paper concludes by identifying future research directions focused on passwordless authentication, zero-trust security models, and adaptive authentication systems for next-generation web applications.Keywords : Authentication mechanisms, Web application security, Multi-Factor Authentication (MFA), OAuth 2.0, JSON Web Tokens (JWT), Single Sign-On (SSO), Biometric authentication, Passwordless authentication, Zero Trust security, Cybersecurity

CAPTCHAs are widely used as authentication methods in mobile applications and web-based services to prevent AI bots from gaining unauthorized access. Early CAPTCHAs relied on image-based techniques, distorting text within images to make it difficult for bots to decipher. However, modern AI-powered optical character recognition (OCR) systems can now easily bypass these measures. To address this limitation, image-reasoning CAPTCHAs were introduced. Basic versions of these rely solely on object detection, rendering them vulnerable to advanced AI attacks. In response, we propose a novel image-reasoning CAPTCHA, called IReCAPTCHA, which integrates image understanding, noise mitigation, and mathematical reasoning. Unlike traditional object detection-based CAPTCHAs, our approach challenges users with tasks that require the comprehension of multiple objects, counting, and color recognition. Additionally, we apply noise mitigation techniques to obscure visual information, thereby enhancing resistance to AI-based deciphering. To evaluate the effectiveness of our CAPTCHA, we developed a comprehensive dataset specifically designed to test its robustness against AI attacks. The results are highly promising, demonstrating significantly greater resistance compared to existing image-reasoning CAPTCHAs. These findings suggest that our approach has strong potential to enhance online security by effectively deterring automated access attempts by malicious bots.

Purpose The increasing popularity of generative artificial intelligence (GenAI) in higher education has raised question marks about its implications for skills including 21st-century skills. While 21st-century skills, particularly the “four Cs” (critical thinking, creativity, communication and collaboration), remain vital, the existing literature lacks a synthesis of how GenAI reshapes their relevance and assessment. Therefore, this study examines GenAI’s implications for higher education students' 21st-century skills’ relevance and assessment methods in evolving digital learning environments. Design/methodology/approach This study used a systematic review methodology and analyzed 62 publications from 2020 to 2024, sourced from Google Scholar and Web of Science. Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the GenAI epistemology, pedagogy and assessment (GenAI EPA) analytical framework guided methodological processes including inclusion/exclusion, screening, coding and presentation of findings. Findings This study revealed three key findings. First, 21st-century skills, such as the four Cs, remain highly relevant in the GenAI era, although they have evolved to suit GenAI needs (e.g. critical evaluation of AI outputs). Second, traditional assessment methods (e.g. standardized tests) are inadequate in GenAI contexts, whereas alternative approaches, including digital portfolios, have proven to be more effective in capturing 21st-century skills. Third, stakeholders (especially educators) emphasize hybrid assessment models that combine process-oriented and outcome-oriented strategies to balance the disruption of GenAI integration with the maintenance of academic integrity. Practical implications This study highlights four key implications for higher education. First, institutions must integrate GenAI tools into curricula to develop and assess 21st-century skills. Second, ethical concerns (e.g. bias and privacy) necessitate clear AI use policies. Third, traditional assessments should shift toward dynamic, authentic methods (e.g. AI-assisted portfolios). Finally, educator-industry collaboration is vital, including co-designed curricula, workshops and internships for real-world AI readiness. Originality/value This study contributes to ongoing AI in education scholarship by mapping the interplay between GenAI and 21st-century skills, offering evidence-based recommendations for rethinking assessment paradigms. It highlights the urgency for institutional policies and practices that align pedagogical innovation with labor-market demands, ensuring graduates thrive in an AI-driven future.

Behavioral authentication has become increasingly popular as a natural method for authentication in Virtual Reality (VR). However, existing studies often overlook the fact that users may perform behavioral authentication in different postures (i.e., sitting, standing, reclining) during VR use. Therefore, understanding how posture variations affect classification accuracy is crucial for designing posture-robust systems. In this study, we conducted a controlled experiment (N = 30) to investigate the impact of posture on classification accuracy during a target-selection task. We collected behavioral trajectory data and analyzed it using multivariate time series classification algorithms, addressing authentication performance under three different postures. In a within-posture authentication, reclining took longer but achieved the highest classification accuracy, with an interaction effect between posture and target vertical layout. In cross-posture authentication, transfers from sitting to standing/reclining were more effective than direct transfers between standing and reclining, with vertical layout crucial for classification accuracy. In mixed-posture training, the cross-posture classification accuracy increased, particularly when standing and reclining data were combined to help the model indirectly learn features of sitting posture. These findings provide valuable insights for designing tasks and data collection strategies that support the development of robust cross-posture authentication systems.

The use of passwords for end-user authentication has been fraught with issues for decades, making passwordless authentication (PLA) systems a needed alternative for password-based authentication (PBA). PLA systems involve any methods that help identify a user without the use of a password – methods that are often rolled out with trade-offs in security, privacy, and convenience as means of innovation. Meanwhile, successful implementation of these systems is dependent on their acceptance, and data on the views of the users have not yet fully covered the wide range of approaches and contexts. The current study explored the perceptions of the users ranging from occasional information technology (IT) users to professional IT developers through a cross-sectional survey, assessing their views across different authentication methods (one-time passcode (OTP), fingerprint (FP), voice recognition (VR), personal identification number (PIN), finger swipe (FS), and authentication of choice (AoC)) in different contexts (low-risk account login and payment confirmation). One hundred seventy participants, aged 18–65 years and representing five different levels of IT experience contributed to the survey. The results shed light on the perceptions, concerns, and preferences of the users on PLA and PBA through quantitative and qualitative data, suggesting that in both use scenarios, OTP, fingerprint and PIN formed a cluster of favourites, followed by AoC; common reasons were convenience, usability, reliability and accuracy. However, knowledge gaps and misconceptions were present, highlighting the importance of carefully designed, adjusted, and targeted user information. Future research could extend the investigation to larger samples and more narrow-focused and refined survey items to further explore, for example, the finer differences between OTP, FP, and PIN, and on the other hand, VR and FS. The current findings are expected to benefit the industry involved in end-user authentication by providing empirical evidence on the views of corporate and end users on these authentication systems; by influencing the choice of use cases and methods deployed by software and system developers; and by enhancing the knowledge of professionals and industry experts specialising in user experience design and identity and access management.

The geographical origin of Fritillariae Thunbergii Bulbus (FTB) is a critical determinant of its quality and efficacy, representing a long-standing challenge in quality control and market standardization. This persistent issue necessitates the development of reliable analytical methods for accurate origin authentication. To address this, we developed an integrated analytical strategy utilizing liquid chromatography coupled with mass spectrometry (LC-MS). This strategy synergistically combines targeted and non-targeted analytical approaches with multivariate statistical analysis to accurately differentiate between FTB produced in Zhejiang province (FPZ) and that from other regions (non-Zhejiang-produced FTB, FNZ). Moreover, a key innovation of this study is the integration of modern chemical profiling with traditional morphological assessment in a synergistic manner. This integrated approach establishes a direct correlation between externally observable traits of the plant and its intrinsic quality parameters, providing a more comprehensive and scientifically grounded framework for quality evaluation. This comprehensive strategy concurrently evaluates both external morphological characteristics and internal chemical profiles, and is executed through a dual-platform analytical workflow. First, ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QQQ-MS) was used to precisely quantify three key alkaloids: verticine, verticinone, and peimisine. Concurrently, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS), integrated with chemometric analysis, was utilized for non-targeted alkaloid profiling to identify characteristic components distinguishing FPZ from FNZ. Structural characterization and semi-quantitative analysis of the differential components identified 11 alkaloids as characteristic chemical markers. Notably, the relative levels of these markers showed significant correlations with key external morphological features. Collectively, these findings provide a scientific foundation for optimizing the standardized cultivation of FTB and for ensuring the consistency of its clinical efficacy.

Inpainting-assisted reversible authentication method for demosaiced image with enhanced recoverability

e-commerce has rapidly expanded over recent years, and the volume of this particular type of commerce has contributed to a dramatic rise in the amount of parcel deliveries both to households and business premises. Nevertheless, a problem of missed deliveries, unavailability of receivers, and a chance of theft of a parcel are frequent issues in traditional methods of parcel delivery. These problems reveal the necessity of the safe and automatic system of parcel reception that can be stable without the constant control of a person. The project is dedicated to the design and implementation of a Smart Parcel Receiving System based on the Internet of Things (IoT) technology with the help of secure authentication methods. Proposed system combines ESP32 microcontroller, electromagnetic locking system, OTP verification system implemented in keypad and face recognition system to offer dual-level security system. The system helps deliver personnel to leave the packages safely via a one-time password (OTP), and the family members with authorization could access the parcel box both with face recognition and OTP authentication.

Diagnostic reasoning, the cognitive process of interpreting clinical information to arrive at a diagnosis, is a critical competency in medical education, and has been taught using authentic methods such as standardized patients and peer role-playing that simulate real clinical encounters. Although effective, these approaches require substantial time, space, and cost. To address the limitations of traditional approaches, AI-powered virtual patient chatbots were developed between April 21-28, 2025, with a chatbot builder that incorporates natural language processing. The design was guided by two core cognitive models of diagnostic reasoning-the dual process model and the memory model. The chatbots simulated authentic physician-patient interactions across three clinical scenarios: fatigue, stomachache, and memory loss. Students can perform differential diagnoses based on various clinical data and received immediate feedback on diagnostic accuracy. The patient chatbots were evaluated through surveys and interviews with faculty (May 2025) and students (November 2025). Findings consistently indicated that the chatbots functioned as an effective (4.50/5) and usable tool (4.46/5) for diagnostic reasoning practice. Participants perceived that the chatbots supported 'core cognitive processes of diagnostic reasoning' by prompting active hypothetico-deductive reasoning, while repeated exposure to different cases was viewed as facilitating pattern recognition. Faculty highlighted the chatbots' cost-efficiency and scalability within curricula, while students emphasized authentic "from-zero" clinical reasoning and the value of reviewing dialogue logs for reflection. Overall, the results demonstrate that the chatbots can provide a feasible and educationally meaningful environment for practicing diagnostic reasoning in medical education. Future work should refine AI-powered virtual patient chatbots by incorporating tiered case complexity, patient-centered language, and equity-oriented design. Effective use will require systematic instructional design and learner preparation for emotionally challenging scenarios. Larger empirical studies using both outcome and interaction data are needed to evaluate educational impact and guide integration into clinical training.

Secure and transparent electronic voting remains a major challenge due to vulnerabilities in centralized infrastructures and insufficient voter authentication methods. Existing systems frequently depend on weak biometric checks or static credential verification, making them susceptible to spoofing, identity duplication, and manipulation of centralized databases. AuraVote introduces a next-generation decentralized voting framework that integrates real-time artificial intelligence with blockchain-based immutability to guarantee voter authenticity and system integrity. The proposed model adopts a robust architecture including RetinaFace for face localization, ArcFace for generating highly discriminative 512-dimensional embeddings, and cosine-similarity-based identity verification. Alongside facial verification, the system executes multi-modal liveness detection incorporating blink dynamics, natural micro-movement estimation, and texture-frequency analysis to counter photo, video, and digital screen presentation attacks. A FastAPI-based verification backend processes live video streams from the browser and communicates verification results to a Next.js decentralized application. Verified users cast votes on an Ethereum Proof-of-Authority (PoA) blockchain using MetaMask, ensuring tamper-proof vote recording and cryptographically signed voter participation. This paper details the architecture, AI algorithms, blockchain workflow, system security properties, and implementation strategy, demonstrating how combining modern deep-learning authentication with decentralized ledgers establishes a secure, scalable, and trustworthy e-voting ecosystem.

The rise of wearables such as fitness trackers and smartwatches has increased the need for strong security to protect personal data. Although two-factor authentication methods improve security, they often require additional user input, making them inconvenient. Recently, hardware flaws in accelerometers and WiFi interfaces have been leveraged to create low-effort two-factor authentication methods. However, these hardware-based device credentials are static, necessitating device replacement if the credentials are compromised. In this study, we introduce an innovative device authentication system that identifies wearables using vibration-based credentials. By utilizing built-in vibration motors and motion sensors (i.e., accelerometers and gyroscopes), our system establishes a unique communication channel to capture the distinct characteristics of each device. Unlike existing methods, our vibration-based credentials are reprogrammable and user-friendly. We develop advanced data processing techniques to minimize the impact of noise, body motion artifacts, and wearing position. We design a lightweight convolutional neural network for feature extraction and device authentication, with a majority vote mechanism to improve identification robustness. Extensive experiments with five different smartwatches demonstrate that our system achieves an average precision of 98% and a recall of 94% under various attacks, demonstrating that including gyroscope data significantly improves performance across different wearing poses and watch orientations.

Objective: To document the prevalence of PSD and to identify associated clinical and demographic factors in a tertiary care setting.Methodology: We carried out a cross-sectional study among 179 stroke survivors at a tertiary care hospital in Larkana during 15 June 2025 to 15 December 2025. The clinical features along with demographic details were recorded and the depression symptoms were analyzed with authentic methods. Chi-square tests were used to determine the correlation of depression with stroke patient’s features. Ethical approval and written informed consent were secured.Results: 40.1% population of patients experience depressive disorder. Patients within the age bracket of 40–60 years experienced more symptoms of depression than the older ones, however, this contrast was not significant statistically. It was also observed that the within six months of the stroke episode, the patients were more prone to experience depres-sive disorder as compared to those having relatively longer duration since the episode of stroke (48.7% vs. 33.0%; p=0.03). Moreover, clinical depression was found more prevalent in patients suffering from diabetes mellitus type II (61.9% vs. 32.8%; p=0.01) as well as amongst individuals with consistent smoking habits (p=0.04). No significant asso-ciation as regards gender or hypertension was observed.Conclusion: Stroke is a substantive risk factor for developing clinical depression among the survivors. Thorough screen-ing along with adequate follow up may significantly address the neurological and psychiatric needs of these patients, resulting in improved quality of life. Key words: Beck Depression Inventory, Diabetes mellitus; post-stroke depression; Stroke; Risk factors; Hypertension

The Internet of Drones (IoD) is experiencing significant growth across military, commercial, and civilian applications due to its unique attributes, including high mobility and three-dimensional movement. However, the reliance on unencrypted wireless communication and the limited computational capabilities of drones makes them vulnerable to various cyber-attacks. These vulnerabilities expose IoD networks to threats such as man-in-the-middle attacks, impersonation, credential leakage, GPS spoofing, and drone hijacking. To address these challenges, the development of a robust and highly secure protocol is essential. This paper presents an innovative approach for detecting malicious drones utilizing the Conversation to Handshake Authentication (CTHA) method. Our proposed solution employs sequential procedures and deep learning techniques to distinguish between legitimate and malicious drones effectively. Furthermore, we incorporate blockchain technology and federated learning to enhance data integrity and resilience against attacks, such as Denial of Service (DoS) attacks. The objective of our solution is to bolster the security and operational integrity of IoD systems. Extensive experimentation validates the effectiveness and accuracy of our proposed method. The findings of this research contribute to the evolving field of UAV security, establishing a foundation for proactive defense mechanisms against malicious activities within the IoD ecosystem.