High quality sputtered crystalline AlN piezoelectric films on Mo buffered substrates for MEMS applications

A hash-based signature scheme with layer-specific configuration for secure boot in IoT devices

The rapid expansion of IoT devices in maternal health monitoring enables continuous data collection and improved clinical assessment; however, it also introduces significant security and privacy concerns due to the sensitivity of maternal health information. This study investigates how artificial intelligence (AI) and machine learning (ML) can enhance both analytical performance and data protection in IoT-based maternal monitoring systems. The proposed framework employs Random Forest, Decision Tree, Support Vector Machine, and a stacking–bagging ensemble to improve maternal risk prediction and anomaly detection. Privacy-preserving techniques are integrated to secure physiological parameters: homomorphic encryption ensures data confidentiality during processing, while differential privacy limits information leakage from model outputs. Experimental results show that the stacking classifier combined with Random Forest achieved the highest accuracy of 82.3%, demonstrating greater robustness than traditional algorithms. Although differential privacy strengthened data protection, it reduced precision and F1-score, highlighting a trade-off between privacy and accuracy. Overall, integrating ensemble learning with privacy-preserving methods improves the security, accuracy, and reliability of IoT-driven maternal health monitoring systems.

MARINE-Transformer: A General-purpose framework for multivariate ocean time series analysis.

PSUL-SG : A provably secure ultra-lightweight mutual authentication and key agreement protocol for smart-grid IoT devices

This paper presents the design of an IoT-based flood pump control system utilizing currently developed IoT devices and technologies. The system employs a smart switch (DIT AI-SM01) to control and monitor flood pumps. Experimental results confirm the effectiveness of the proposed system in automating flood pump operations. Equipment status can be monitored and controlled remotely via a smartphone or PC with the appropriate IoT application, simplifying the control system and significantly reducing response time within the flood pump control range.

Now a days Cutting edge Technology in Healthcare organization is in demand. Introduction of smart control instruments in the hospitals is vital as the present way of supervision becomes obsolete. This flow of patients’ information between the systemscausesanexplosionoftime-gapsandthedecision-making process becomes complex. The prompt has driven scientists to study compare present Internet technology including cloud and IoT functions with an advance smart managing system at the applicationdomainofhealth.Criticalcriteriasuchasintegration, expandability and security, accessibility and performance have been evaluated. It has been identified with significant flaws inthe current technology, such as synchronization issues between devices,unavailabilityofrelevantanalyticsandabilitytorespond efficientlytocriticalsituations.Itcanbeconcludedthatinvention in healthcare supervision is mandatory. With the support of IoT device and related analysis tool, this problems could be solved effectively

Accurate device-type identification (DI) is critical for ensuring the security and stability of large-scale Internet of Things (IoT) deployments in smart grids. However, existing traffic-based DI methods often struggle in dynamic environments, as they fail to capture the temporal evolution of device behaviors, overlook complex inter-device dependencies, and lack robustness to the sparse or incomplete data common in practice. To address these challenges, we propose DRID, a novel Spatiotemporal Dynamic Relational Framework for Robust IoT Device Identification. DRID jointly captures structural communication patterns and multi-scale temporal dynamics via a structure-time interaction mechanism and multi-scale temporal modeling, while leveraging a differentiation-aware adaptive learning strategy to selectively enhance discriminative features under sparse or noisy traffic conditions. Extensive evaluations on two public IoT traffic datasets demonstrate that DRID consistently outperforms state-of-the-art baselines across diverse sampling scenarios. By effectively fusing structural and temporal information while maintaining robustness under data scarcity, DRID provides a scalable and accurate solution for IoT device identification, advancing secure and intelligent management of critical smart grid infrastructures.

The Smart Gate system is an advanced IoT-enabled solution designed for automated vehicle entry and real-time monitoring in residential complexes, offices, parking areas, and restricted zones. The system combines RFID technology, microcontroller intelligence, and cloud-based IoT connectivity to replace traditional manual access methods. Each vehicle is assigned a unique RFID tag that carries identification information. When a vehicle approaches the gate, the RFID reader scans the tag and sends the data to the ESP8266 or ESP32 microcontroller. The microcontroller verifies the vehicle information against a pre-stored database of authorized entries. Upon successful verification, the gate motor is activated via a relay module, allowing the vehicle to enter or exit. Simultaneously, the vehicle count and status are updated to the cloud server using MQTT or HTTP protocols, enabling real-time monitoring via a web or mobile dashboard. Unauthorized vehicles trigger an alert system to notify administrators, ensuring enhanced security. By integrating IoT, the system allows administrators to access real-time data from any location, reducing dependency on on-site security personnel and manual logbooks. The automated approach minimizes congestion, increases accuracy, and ensures fast and reliable operation even during peak hours. Multiple gates can be managed simultaneously without interference, ensuring scalability for large complexes. Historical data, such as vehicle entry and exit timestamps, is maintained in the cloud for reporting and analytics. Sensor-based triggers and scheduling rules can automate gate closing, reducing energy consumption and enhancing operational efficiency. Security mechanisms, including encrypted communication, device authentication, and secure MQTT topics, prevent unauthorized access and hacking attempts. The system’s modular design allows easy expansion, maintenance, and integration with additional IoT devices. By eliminating human error, the solution ensures accuracy in vehicle management and enhances safety. Administrators can track occupancy, generate automated reports, and optimize traffic flow based on historical data analytics. The system is cost-effective, reliable, and suitable for modern smart city implementations. It leverages low-power microcontrollers, lightweight communication protocols, and cloud platforms to deliver a high-performance, efficient, and user-friendly smart gate solution. This IoT-based system provides a comprehensive vehicle management platform that enhances convenience, security, and efficiency for all stakeholders. It integrates seamlessly with existing infrastructure and supports future expansion, including integration with automated parking systems, RFID-enabled smart cards, and cloud analytics tools. Overall, the Smart Gate system represents a significant improvement over traditional methods, offering a fully automated, scalable, and secure solution for vehicle access and real-time monitoring in modern facilities.

Biogas is a gas produced by the anaerobic decomposition of organic matter with the aid of microorganisms; it primarily consists of methane (CH₄), which has a high calorific value and holds potential as an alternative energy source. Biogas production is carried out using a biogas reactor as the fermentation medium. The use of household waste as feedstock aims to generate economical and environmentally friendly energy while reducing waste and water pollution caused by leachate. The development of alternative energy is becoming increasingly relevant amid global energy supply instability, including the impact of geopolitical dynamics in the Strait of Hormuz region, which serves as a major global oil distribution route. With technological advancements, innovations in smart biogas reactors based on the Internet of Things (IoT) enable real-time monitoring and control of process parameters to enhance methane gas production efficiency. This study employs an experimental method to evaluate the effectiveness of a biogas reactor design utilizing IoT devices, with a focus on real-time monitoring of temperature, pH, and methane gas concentration parameters. Based on the test results, the methane gas concentration increased from 14.08 ppm on the first day to 16.79 ppm on the ninth day. Fermentation conditions indicated an increase of 19.25%. The developed system design utilizes an ESP32 microcontroller integrated with DHT11, PH-4502C, and MQ-4 sensors, and employs Blynk and ThingSpeak for data visualization. The selection of sensors was based on considerations of suitability for the application’s needs, ease of integration with the microcontroller, market availability, and cost-effectiveness of implementation. Test results indicate that the reactor design and system can consistently transmit and present data within an optimal communication range, thereby contributing to improved efficiency, explosion safety, gas leak prevention, and control over the fermentation process in the constructed reactor. During testing, the results showed that the detected gas leak level was 0% throughout the entire observation period. Thus, the system demonstrated a 100% success rate in leak prevention. The implementation of this reactor design supports the development of a more modern, effective, and sustainable renewable energy processing system.

In this paper, a compact dual-band meander line antenna backed by a metamaterial-based artificial magnetic conductor (AMC) is presented for IoT and wearable biomedical applications such as WBAN and health monitoring systems. The proposed antenna operates at 2.45 and 5.0 GHz Wi-Fi/WLAN bands, which are commonly used ISM bands for IoT and wearable devices. The dual resonant behavior in a highly compact structure is designed with the help of a folded meander-line radiating structure, which is backed by an AMC reflector behind the antenna to enhance broadside gain and minimize the radiation toward the human body to reduce Specific absorption rate (SAR). The deep learning-based surrogate modeling and inverse optimization framework is used to design the AMC unit cell, which efficiently predicts reflection phase i.e. 0o phase at both the operating frequencies. The use of AMC backing ensures the enhancement of the gain of the proposed low-profile antenna of approximately 3.4 dB at 2.45 GHz and 3.1 dB at 5.0 GHz. The equivalent circuit modeling and parametric analysis of the antenna and AMC unit cell are analyzed to get further insights into the design. Full wave simulation of AMC-backed antenna in the presence of Phantom confirms the impedance matching at both the operational bands and directional radiation patterns with improved front-to-back ratio. The proposed AMC-backed antenna design confirms the potential of a combination of deep learning-driven AMC unit cell design in the enhancement of gain and reduction in SAR for IoT and Wireless Body Area Network (WBAN) applications.

Abstract In order to enhance data management operations and data security, Healthcare (HC) systems have increasingly adopted Internet of Things (IoT) and Blockchain (BC) technologies in recent years. Nevertheless, prior work has failed to address cross-chain verification for patient data sharing between BCs, leading to difficulties in accessing patient data and potential data mismatches. The process of securely validating and synchronising HC data across multiple independent BC networks is referred to as cross-chain verification. Hence, this paper proposes an IoT- and BC-enabled secure and smart Healthcare System (HCS) through efficient cross-chain verification using Spectral Dispersion Entropy Hash-based Message Authentication Code (SDE-HMAC). Primarily, patients and doctors register in their independent BC network. During registration, their quasi-identifiers are privacy-preserved and stored in the respective BC networks. Once the patient's login is successful, data is collected from the patient’s IoT devices while preserving privacy. After that, the data are stored in BC by creating a Message Authentication Code (MAC) using SDE-HMAC. In the meantime, the doctor requests access to patient data through a login and a MAC creation. Now, among independent BC networks, cross-chain bridge creation and verification are performed. After successful verification, intrusions in patients’ data are detected using the Zoneout Gated Fourier Recurrent Unit (ZGFRU) with 99.52% accuracy. Heart disease is concurrently detected in the patient's normal data. Eventually, in BC, the disease details are stored. Therefore, the proposed framework outperforms other approaches.

Designing and deploying highly secure and efficient encryption algorithms for Internet of Things (IoT) devices, particularly resource-constrained medical devices, poses a significant challenge. Chaotic systems, with their sensitivity to initial conditions and capacity to generate pseudorandom signals, offer a promising solution to address the limitations of image encryption in IoT devices. Based on a novel memristive multi-attractor Hénon map model (MMHM) that is able to generate hyperchaotic signals, this article proposes a lightweight image encryption scheme for medical Internet of Medical Things (IoMT) devices, effectively addressing numerous deficiencies in existing approaches. To overcome the leakage risks present in existing schemes during key distribution, the initial key is encrypted using an asymmetric encryption algorithm. A key update mechanism is also employed, guaranteeing that each encrypted image is assigned a unique key, which effectively nullifies the risk of differential attacks. Additionally, a geometric transformation-based disruption algorithm is introduced, achieving exceptionally high levels of chaos with minimal computational overhead while providing robust resistance to cropping attacks. Notably, the solution is deployed on a digital circuit platform based on the STM32 microcontroller. Experimental results demonstrate that this method significantly outperforms traditional approaches in resisting typical attacks. It effectively compensates for vulnerabilities in the key distribution and key update mechanisms of conventional schemes, substantially enhancing both cryptographic security and efficiency.

The rapid expansion of Internet of Things (IoT)-based systems has introduced unprecedented opportunities for automation, real-time monitoring, and data-driven decision-making across diverse sectors. This study presents a comprehensive analysis of advanced cybersecurity techniques for protecting IoT-based systems, focusing on a multi-layered security architecture integrating device-level protection, network security, edge intelligence, and cloud-based resilience mechanisms. The proposed framework emphasizes a holistic approach where lightweight encryption and authentication protocols secure resource-constrained IoT devices, while intrusion detection systems (IDS) and secure communication protocols enhance network-level protection. This integration of decentralized security with intelligent analytics enhances both short-term threat response and long-term data reliability. Unlike traditional single-layer security approaches, the proposed architecture leverages cross-layer integration to address the interconnected nature of IoT environments. By combining AI-driven intrusion detection, edge computing, and blockchain-based security mechanisms, the framework provides a scalable and adaptive solution to emerging cyber threats. The study also highlights key challenges, including scalability, interoperability, and energy efficiency, while proposing future directions such as federated learning and zero-trust architectures. Overall, this research contributes to the development of resilient and intelligent cybersecurity frameworks for next-generation IoT systems

Natural and man-made disasters such as floods, fires, earthquakes, gas leakages, landslides, and industrial acci- dents cause severe damage to human life, infrastructure, and the environment. Traditional disaster management systems mainly depend on manual monitoring and delayed communication mechanisms, which often create challenges in disaster preparedness, emergency response efficiency, and public safety. Due to the increasing occurrence of disasters and the rapid advancement of smart technologies, there is a growing need for intelligent systems capable of monitoring environmental conditions and generating emergency alerts in real time. Recent advancements in Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and wireless communication technologies have enabled the development of smart disaster monitoring systems. Modern IoTbased disaster management platforms allow continuous monitoring of environmental con- ditions using smart sensors and real-time communication sys- tems. These technologies improve disaster detection accuracy, reduce emergency response delays, and enhance rescue coordi- nation efficiency. However, many existing disaster management systems mainly focus on basic monitoring functionalities and lack advanced capabilities such as AI-based predictive analysis, centralized cloud monitoring, GPSbased emergency tracking, and scalable IoT infrastructures. The proposed AI-Enabled Smart Disaster Management System using IoT Devices is designed as a centralized environmental monitoring and emergency response platform that integrates IoT sensors, Artificial Intelligence, cloud communication technologies, GPS tracking, and automated emergency alert mechanismsinto a single system. The system uses smart sensors such as temperature sensors, smoke sensors, gas sensors, vibration sensors, and water level sensors for continuously monitoring environmental conditions. IoT devices such as ESP32 and Raspberry Pi process sensor data and communicate with cloud servers through wireless communication technologies.c Whenever dangerous environmental conditions exceed pre- defined safety thresholds, the system automatically generates emergency alerts through SMS notifications, email services, mobile applications, and GPSbased location sharing systems. The proposed system provides a secure, scalable, reliable, and cost-effective solution for improving disaster preparedness, public safety, emergency response coordination, and smart city disaster management operations

Currently available cryptographic algorithms such as the RSA, Diffie-Hellman and the ECC are used in securing most Internet communications across the Internet. Theoretical quantum algorithms (and the Shor algorithm in particular) were demonstrated to be able to efficiently break that type of classical cryptography as soon as large-scale quantum computers are developed. To handle this new emerging security problem, PQC has been proposed as a class of cryptographic algorithms that is resistant to both quantum and classical attackers. It is a research paper offering and experimentally implementing a hybrid PQC-TLS protocol in a heterogeneous network architecture which includes edge computing devices and cloud computing and IoT devices. The suggested architecture integrates both lattice-based CRYSTALS-Kyber key encapsulation scheme and CRYSTALS-Dilithium digital signature scheme in the TLS 1.3 handshake without being interoperable with the older ECC-based key exchange schemes. Five measures of performance that included handshake latency, CPU consumption, memory consumption, bandwidth overhead and energy consumption were analyzed in detail through experimentation. The experiments were conducted using in Iot, edge and cloud nodes under controlled conditions that were repeated a number of times to give reliability in the statistics. The results indicate that the integration of PQC has moderate overhead (mean increase in latency about 1517) and decent computational and energy requirements. Another method of resisting popular attacks on networks like as the man in the middle, replay, and downgrade attacks, is security validation. The findings indicate that quantum resilient secure communication migration to a heterogeneous distributed infrastructure can be practically and scalably provided using hybrid PQC-TLS protocols. Statistical analysis including standard deviation and 95% confidence intervals was performed to validate the reliability of the results.

Technology is currently spreading at an exponential rate. more accessibility, use, and application of this technology across all sectors and industries have been made possible by technological advancements, more computing power, and lower costs. Traditionally labour-intensive data analysis tasks can now be completed rapidly and effectively thanks to the development of smart and autonomous technologies like artificial intelligence and machine learning. Previously isolated datasets and data lakes are increasingly being used and linked. AI, digital twins, the metaverse, and virtual technologies are permeating every industry and more significantly merging with people to the point where it seems impossible to distinguish between the actual and virtual worlds. However, a fantastic backbone and capacity to transmit data, as well as immediate delivery at high speed and security, are necessary for the successful use of these incredible and new technologies. In order for 6G to be properly onboarded and executed in a logical manner, 5G, which is now in its deployment, must accomplish its goals. The European Commission is requesting money for important projects like Horizon 2020 and has 5G goals. 5G and 6G have enormous advantages for everyone, but only if they are implemented in a way that reduces the risk they may pose to security, privacy, and trust that are the fundamental pillars that must be upheld. Smart cities will allow for the analysis of acquired data, which could endanger national security if it falls into the wrong hands. A strong governance plan and method for managing 5G and 6G must be in place in order to guarantee success, given how many IoT and e-IoT devices are present in smart cities and how intertwined technologies are engaging with people. The background, risks, and advantages of 5G and 6G are explained in this chapter, which also emphasizes the necessity of strong governance

Fog computing has emerged as a promising paradigm to extend cloud services closer to IoT devices, improving response times & reducing network congestion. However, efficient load balancing in fog computing is essential to maximize performance, reduce costs, ensure energy efficiency, & maintain a high quality of service, ultimately supporting the demands of latency-sensitive & resource-intensive IoT applications. The primary objective of task mapping in computing environments such as cloud, fog, & edge computing is to allocate tasks across available resources in an efficient & effective manner, particularly in fog computing, where resources are distributed & closer to end devices. This paper presents a hybrid approach that integrates Genetic Algorithm (GA) & Deep Q-Learning (DQL) for task mapping in fog computing environments. The objective is to minimize makespan & computational costs while maintaining high resource utilization. Our approach leverages a GA to perform initial task allocation by exploring a broad solution space, thereby enhancing convergence toward optimal scheduling patterns. This solution is refined using DQL, which adapts to dynamic environments by learning from continuous feedback and enabling real-time decision-making. By combining the exploration strengths of GA with the adaptive capabilities of DQL, the proposed method effectively manages task allocation & resource utilization. Experimental results show that our hybrid approach outperforms baseline methods, significantly reducing makespan & operational costs.

With the rapid development of the Internet of Things (IoT) and the increase in the frequency of cyberattacks, accurate identification of IoT end devices is critical to their security. Existing identification methods are based on raw, statistical, and deep features of network traffic, each with their own advantages and disadvantages. Raw feature-based methods have difficulty performing feature extraction and insufficient information. As such, the recognition accuracy of statistical feature-based methods is limited by the distinguishment machine learning classifiers, and the deep feature-based methods do not take into account the problem of large differences in traffic samples, which leads to low recognition accuracy in some devices. For this reason, this paper proposes the IoTDI-ImbS method. The method selects the network traffic payload information as the original features and converts them into grayscale images; uses a generative adversarial network-based IoT terminal devices traffic generation (NTGAN) algorithm to generate traffic samples for devices with fewer samples through generative adversarial network to solve the sample imbalance problem; and constructs a ResNet18-BiLSTM model, mining spatial features with ResNet18 and extracting temporal features with BiLSTM to improve recognition accuracy. The experimental results on different sizes of IoT terminal device datasets show that IoTDI-ImbS has performance advantages over other methods in recognition accuracy, better leverages the sample imbalance problem in the dataset, and provides a more effective solution for IoT device recognition. Experimental results on the UNSW and IoT Sentinel dataset demonstrate that IoTDI-ImbS significantly outperforms baseline methods. Specifically, on the UNSW dataset, our method achieves an overall accuracy of 99.1% and an F1-score of 0.985. After integrating the NTGAN module, the identification accuracy for minority classes improved by approximately 3.5%. On the IoT Sentinel dataset, the model maintains a high precision of 98.7%, proving its robustness in diverse IoT environments.

Sustainability and performance trustworthiness of IoT monitoring software architectures in the Edge