Things Devices Research Articles

Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis

In recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount.

Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments

This article presents a comprehensive analysis of edge computing and analytics for Internet of Things (IoT) devices, addressing the growing need for real-time processing and reduced latency in IoT applications. We explore the evolution of IoT architectures, from cloud-centric models to edge-enabled systems, and examine the key components and data flows in edge computing environments. Through a detailed comparison of cloud and edge processing, we demonstrate significant latency reductions across various IoT scenarios, with improvements from hundreds of milliseconds to mere milliseconds. The article delves into crucial aspects of data management in edge computing, including local versus cloud processing trade-offs, data synchronization strategies, and privacy considerations. A case study of an edge analytics pipeline in a smart factory setting showcases practical implementations, revealing substantial improvements in anomaly detection speed, bandwidth utilization, and overall system efficiency. The case study achieved a 92.5% reduction in anomaly detection latency and an 85% decrease in bandwidth usage. Finally, we discuss ongoing challenges and future directions in edge computing, including scalability issues, standardization efforts, and the integration of emerging technologies such as 5G and AI accelerators. This article contributes to the growing body of knowledge on edge computing in IoT, offering insights into its transformative potential for creating more responsive and intelligent systems across diverse applications.

An Innovative Honeypot Architecture for Detecting and Mitigating Hardware Trojans in IoT Devices

The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.

Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware

This paper provides an overview of several secure boot architectures with a focus on key rotation. It expands on a practitioner note that the authors submitted to the 2023 IEEE Secure Development Conference. Key rotation is important due to the frequency of lost signing keys and the difficulty of managing secret keys for the long lifetimes of Industrial Internet of Things (IIOT) devices. Key rotation is not simple for IIOT due to limited resources during a secure boot process and the constraints of the firmware utilities that come from the chip vendors. This paper reviews and compares five common architectures for a secure boot that are seen across the IIOT community. For each architecture, it provides some key strengths and weaknesses associated with that architecture. The paper then provides a detailed comparison and analysis of the architectures to convince the IIOT community to move towards a strong use of certificates (instead of the traditional use of raw public keys). The intent of this paper is to provide a practitioner’s perspective on these challenges and the tradeoffs in hopes of inviting comments from chip vendors and the broader community.

Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning

With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing often encounters challenges such as network instability and dynamic resource variations, which can lead to task interruptions or failures. To address these issues, developing a fault-tolerant scheduling mechanism is crucial to ensure that a system continues to operate efficiently even when some nodes experience failures. In this paper, we propose an innovative fault-tolerant scheduling model based on asynchronous graph reinforcement learning. This model incorporates a deep reinforcement learning framework built upon a graph neural network, allowing it to accurately capture the complex communication relationships between computing nodes. The model generates fault-tolerant scheduling actions as output, ensuring robust performance in dynamic environments. Additionally, we introduce an asynchronous model update strategy, which enhances the model’s capability of real-time dynamic scheduling through multi-threaded parallel interactions with the environment and frequent model updates via running threads. The experimental results demonstrate that the proposed method outperformed the baseline algorithms in terms of quality of service (QoS) assurance and fault-tolerant scheduling capabilities.

A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security.

Analysis of Soil Viability Monitoring System for In-House Plantation Growth Using an Internet of Things Approach

Houseplant cultivation has become increasingly popular, allowing individuals to bring nature into their homes. However, successful indoor gardening requires careful monitoring of soil parameters to ensure optimal plant growth. To address this need, sensor technology and Internet of Things (IoT) devices are utilized to monitor soil temperature and moisture levels, which play crucial roles in plant growth. Various soil factors are sensed and collected using an IoT-based microcontroller, with data transmission facilitated by a Message Queue Telemetry Transport (MQTT) broker. Visualization of the data is achieved through the Node-RED programming tool, simplifying dashboard creation for easy monitoring. Furthermore, the collected data is stored in a MySQL server, enabling further analysis through SQL queries. The day is divided into four quarters with six-hour intervals, allowing for soil data collection using temperature and moisture sensors. The resulting information on the dashboard facilitates informed decision-making to enhance soil conditions for optimal indoor plant growth. Experimentation has revealed a reduction in soil temperature of 3°C during daytime due to air conditioning operation, while soil moisture content remains consistently between 60 to 65% during early mornings and late evenings. Additionally, emphasis is placed on remote management using IoT systems, enabling monitoring of plant growth even when access is limited. Overall, monitoring soil factors using IoT technology offers a promising approach to optimizing indoor gardening practices and minimizing environmental resource consumption.

Implementation of Chaotic Neural Key Generation Algorithm For IoT Devices

This paper presents a new method for generating encryption keys for Internet of Things (IoT) devices. This method combines chaos theory with neural networks to create secure and efficient keys. We use the Lorenz chaotic system to generate complex patterns and a Convolutional Neural Network (CNN) to learn and predict these patterns. This approach is designed to address the unique security challenges of IoT devices, which often have limited computing power. We tested our method with different key sizes and evaluated its performance using accuracy, loss, entropy, and correlation metrics. The results show that key sizes between 256 and 512 bits offer the best balance between model performance and security for IoT devices. We also conducted Diehard statistical tests, which our key generation method passed successfully, demonstrating its ability to produce high-quality random keys.

Powerful graph neural network for node classification of the IoT network

Internet of Things (IoT) devices are increasingly used in various applications in our daily lives. The network structure for IoT is heterogeneous and can create a complex architecture depending on the application and geographical structure. To efficiently process the information within this diverse and complex relationship, a robust data structure is needed for network operations. Graph neural network (GNN) technology is emerging as a capable tool for predicting complex data structures, such as graphs. Graphs can be employed to mimic the structure of IoT network and process information from IoT nodes using GNN techniques. In this paper, our goal is to explore the effectiveness of GNN in performing the node classification task for a given IoT network. We have generated three different IoT networks with varying network sizes, number of nodes, and feature sizes. We then test 12 different GNN algorithms to evaluate their performance in IoT node classification. Each method is examined in detail to observe its training behavior, testing behavior, and resilience against noise. In addition, time complexity and generalization ability of each model have also been studied. The experimental results show that some methods exhibit high resilience against noisy data for IoT node classification accuracy.

Energy-efficient trajectory optimization algorithm based on K-medoids clustering and gradient-based optimizer for multi-UAV-assisted mobile edge computing systems

The mobile edge computing system supported by multiple unmanned aerial vehicles (UAVs) has gained significant interest over the last few decades due to its flexibility and ability to enhance communication coverage. In this system, the UAVs function as edge servers to offer computing services to Internet of Things devices (IoTDs), and if they are located distant from those devices, a significant amount of energy is consumed while data is transmitted. Therefore, optimizing UAVs’ trajectories is an indispensable process to minimize overall energy consumption in this system. This problem is difficult to solve because it requires multiple considerations, including the number and placement of stop points (SPs), their order, and the association between SPs and UAVs. A few studies in the literature have been presented to address all of these aspects; nevertheless, the majority of them suffer from slow convergence speed, stagnation in local optima, and expensive computational costs. Therefore, this study presents a new trajectory optimization algorithm, namely ITPA-GBOKM, based on a newly proposed transfer-based encoding mechanism, gradient-based optimizer, and K-Medoids Clustering algorithm to tackle this problem more accurately. The K-medoid clustering algorithm is used to achieve better association between UAVs and SPs since it is less sensitive to outliers than the K-means clustering algorithm; the transfer function-based encoding mechanism is used to efficiently define this problem’s solutions and manage the number of SPs; and GBO is utilized to search for the best SPs that could minimize overall energy consumption, including that consumed by UAVs and IoTDs. The proposed ITPA-GBOKM is evaluated using 13 instances with several IoTDs ranging from 60 to 700 to show its effectiveness in dealing with the trajectory optimization problem at small, medium, and large scales. Furthermore, it is compared to several rival optimizers using a variety of performance metrics, including average fitness, multiple comparison test, Wilcoxon rank sum test, standard deviation, Friedman mean rank, and convergence speed, to show its superiority. The experimental results indicates that this algorithm is capable of producing significantly different and superior results compared to the rival algorithms.

A lightweight dual-link accelerated authentication protocol based on NLFSR-XOR APUF

Physical Unclonable Function (PUF) circuits, as a lightweight hardware security primitive, can provide reliable authentication for resource-constrained Internet of Things (IoT) devices. However, in real-time environments and systems, authentication of embedded devices has strict requirements on resources and low latency. Therefore, this paper proposes a lightweight dual-link accelerated authentication protocol design based on NLFSR-XOR APUF, through the study of Non-Linear Feedback Shift Registers (NLFSR) and XOR APUF circuits. First, the scheme utilizes the symmetric path delay deviation characteristics of APUF and the complexity of NLFSR state transitions to form a nonlinear output function that changes with the challenge signal. Then, a lightweight and attack-resistant authentication protocol is established by combining the random probability of shuffling array bits with the XOR confusion mechanism. Finally, the advantages of GPU parallel computing and AES T-table reconfiguration scheme are used to achieve an accelerated and side-channel attack-resistant authentication protocol. Experimental results show that the PUF circuit can effectively resist various modeling attacks, including logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). The security of the protocol has been formally verified, and the prototype has been implemented on the Xilinx xc100T development board, effectively resisting deception attacks, physical attacks, and modeling attacks. The protocol's area overhead in terms of LUT and encryption time is reduced by 58.6 % and 67.8 %, respectively, compared to similar protocols.

Implementing Loss Prevention by Identifying Trends and Insights to Help Policyholders Mitigate Risks and Reduce Claims

Loss prevention is a critical focus for both insurers and policyholders, as rising claim volumes lead to increased premiums and financial losses. This paper explores the implementation of loss prevention strategies through the identification of trends and insights derived from historical data, predictive analytics, and emerging technologies. By leveraging these insights, insurers can recommend proactive risk mitigation measures to policyholders, reducing the occurrence of claims and overall risks. The study begins by analyzing historical data trends across various sectors such as auto, property, and health insurance, identifying key factors that drive claims. Predictive analytics is then applied to forecast future risks, allowing insurers to develop personalized strategies for mitigating those risks. Emerging technologies like Internet of Things (IoT) devices and artificial intelligence (AI) are highlighted for their role in providing real-time data and early warning systems, which help to prevent incidents before they escalate into claims. This paper also outlines practical loss prevention strategies for policyholders, such as installing smart home devices to monitor potential hazards, encouraging safe driving habits through telematics, and promoting health and wellness programs to lower medical claims. These risk mitigation strategies are shown to provide significant return on investment (ROI) for policyholders by reducing claims frequency and severity. Through case studies and data analysis, the paper demonstrates that proactive loss prevention not only benefits policyholders by lowering their risk exposure but also helps insurers reduce claims costs and enhance profitability. In conclusion, this research highlights the importance of trend identification, predictive analytics, and emerging technologies in the future of loss prevention, offering a path forward for insurers and policyholders to work collaboratively in mitigating risks.

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

A systematic review on artificial intelligence approaches for smart health devices

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

An Overview of Software Sensor Applications in Biosystem Monitoring and Control

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

BCIDS‐IoT: A Binary Classification Intrusion Detection Scheme for Internet of Things Communication

ABSTRACTThe integration of Internet of Things (IoT) devices into daily life has exponentially increased the amount of data. In an IoT computing environment, like Smart Homes, Internet of Medical Things, Industrial Internet of things, Internet of Vehicles, and Smart Agriculture, there is a significant volume of data being exchanged between devices, servers, and users. This gives attackers a chance to launch malicious attacks on devices and associated resources. In this article, we have addressed this issue and proposed a machine learning‐based malware detection technique for the secure communication of IoT (BCIDS‐IoT). The proposed BCIDS‐IoT employs numerous algorithms for efficient detection. The benchmark UNSW‐NB15 dataset is utilized for the analysis. BCIDS‐IoT lowers false positives, maintains high detection rates, and allows for large‐scale network traffic without compromising performance. The various models, such as logistic regression, decision trees, random forests, extra trees, K‐nearest neighbors, and artificial neural network (ANNs), are utilized in the proposed BCIDS‐IoT. Metrics like precision, recall, and F1‐score are also calculated alongside accuracy. ANN surpassed all other models with an accuracy of . Finally, the proposed BCIDS‐IoT is also compared with different closely related schemes, indicating its outperformance among all.