Resource-constrained Environments Research Articles

ZeroD-fender: A Resource-aware IoT Malware Detection Engine via Fine-grained Side-channel Analysis

In early 2023, cyberattacks experienced a significant rise due to unknown (zero-day) malware targeting Internet of Things (IoT) devices. To tackle the challenge of zero-day detection within a highly resource-constrained IoT environment, we propose a novel design that utilizes fine-grained power side-channel analysis with deep learning techniques. Our approach introduces an innovative concept called multiscale feature extraction to identify the most representative malware features across diverse architectures, thereby enhancing deep learning based detection performance against zero-day malware. Specifically, we employ a fine-grained power side-channel analysis of more than 120,000 honeypot-collected malware files across a hierarchy of commands , functions , and modules to identify the unique zero-day malware behaviors. With these identified features to train our model, ZeroD-fender’s performance in detecting zero-day malware has significantly improved. In pursuit of on-device detection, we present a resource-aware online inference customization framework. This framework features our lightweight network, ThingNetV2, which uses specialized 1-D depthwise separable convolution paired with h-swish activation, leading to significant resource savings. By applying the fine-grained power analysis, ZeroD-fender demonstrates a detection rate of 95.88% across various architecture zero-day malware, achieving detection speeds ranging between 16.083 ms and 23.961 ms , depending on the specific scenario.

Electrochemical detection of human papillomavirus DNA based on an ultrasensitive electrochemical sensing platform using N-graphene paper

Human papillomavirus (HPV) is the primary cause of cervical cancer, a major global health concern affecting women worldwide. Early detection of high-risk HPV genotypes is crucial for timely intervention and reducing disease burden. However, current detection methods often lack sensitivity, speed, or cost-effectiveness, especially in resource-limited settings. This work addresses this critical need by developing an ultrasensitive electrochemical biosensing platform based on nitrogen-doped graphene paper electrodes for quantifying high-risk HPV16 DNA sequences. The N-graphene nanohybrid, fabricated via simple solvothermal treatment of biomass-derived graphene oxide, demonstrated significantly enhanced electrical and electrocatalytic properties compared to undoped graphene. DNA detection was achieved through characteristic oxidation signals of guanine bases, which displayed a wide linear dynamic range (0.5–100 μg/mL), low detection limit (75 pg/mL), and excellent reproducibility (relative standard deviation <3.5 %). Detailed spectroscopic and electrochemical analyses revealed the key roles of pyridinic nitrogen defects in improving interfacial charge transfer kinetics and mitigating biofouling issues. This synergistic combination of high electrical conductivity and versatile surface functionality paves the way for equipment-free, point-of-care genosensor devices tailored for quantitative biomarker screening in resource-constrained environments.

Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition.

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

A lightweight visual mamba network for image recognition under resource-limited environments

The Vision Transformers (ViTs) models show great potential in recognition due to their excellent self-attention mechanisms. However, they often need more computational complexity, making achieving high performance in resource-constrained environments challenging. This paper proposes a lightweight visual model (called LightViM) to tackle these challenges, specifically devised for recognition challenges under resource-constrained environments. Considering the imperative of reducing the model’s computational complexity and resource consumption , we propose lightweight local–global feature fusion modules based on Mamba (LGF-Mamba) aimed at integrating spatially detailed local information with globally contextualized features while maintaining lower linear time complexity. In LGF-Mamba, Mamba sub-blocks are first designed to extract global information; then, for the rapid extraction of local features, LocalE module is designed and integrated into LGF-Mamba, exploring more comprehensive feature representation. This mechanism efficiently directs the network to integrate features from different spatial scales, thereby improving the recognition performance in resource-constrained environments. Experimental results indicate that, in comparison to other leading-edge lightweight methods, the proposed procedure simultaneously achieves superior recognition performance and minimal resource consumption.

Standardization through education of molecular pathology: a spotlight on the European Masters in Molecular Pathology.

Despite advancements in precision medicine, many cancer patients globally, particularly those in resource-constrained environments, face significant challenges in accessing high-quality molecular testing and targeted therapies. The considerable heterogeneity in molecular testing highlights the urgent need to harmonize practices across Europe and beyond, establishing a more standardized and consistent approach in MP laboratories. Professionals, especially molecular pathologists, must move beyond traditional education to cope with this heterogeneity. This perspective addresses critical issues in molecular pathology (MP), such as limited access to high-quality molecular testing, leading to disparities in cancer treatment, and the consequences of inconsistent practices. Recognizing the necessity for a standardized framework for education to address these issues, educational programs play a pivotal role in updating professionals' skills to achieve standardization in MP. European experts from the Steering Committee, the Pathology Section of the European Union of Medical Specialists, and the European Society of Pathology have proposed creating a comprehensive Master's degree program called the "European Masters in Molecular Pathology" (EMMP). This program emerges as a strategic response to the demand for a specialized and standardized framework for education in MP, catering to professionals who concurrently work and study. The program's design aligns with evidence-based education methods, ensuring effective learning and engagement while integrating computational pathology to analyze complex molecular data, enhance diagnostic accuracy, and improve treatment outcomes. EMMP's structured curriculum, strategic partnerships, and regular updates underscore its significance in standardizing MP practices. Exploring future developments, this perspective delves into technology integration and interdisciplinary collaboration, anticipating ongoing advances and harmonization. Challenges and future directions in MP education are discussed, emphasizing the necessity for dynamic curriculum updates, seamless technology integration, and interdisciplinary cooperation. This perspective underscores EMMP's pivotal role in preparing pathologists for this dynamic field, advocating continuous advancements in education and training to uphold excellence in MP practices and maintain the highest patient care standards.

Disruptive innovation capability in resource-constrained environments: The role of strategic posture and human capital

The disruptive innovation framework has been suggested to enhance new venture sustainability in resource-constrained contexts. This paper explores the role of human capital and strategic posture on disruptive innovation capability in low-income and resource-constrained environments. Using a generic qualitative inquiry strategy, we develop propositions tested by 20 in-depth interviews with entrepreneurs and industry role players in the South African startup incubation sector. Data from the interviews were analyzed through thematic analysis with constant comparison. The findings suggest that adopting an emerging market orientation and taking strategic risks assists in identifying the right consumer target market. Innovativeness is linked to effective product development processes, and a learning orientation contributes to the development of the skills and capabilities required for successful innovation in resource-constrained environments. Additionally, the findings emphasize prior founder knowledge and industry experience in shaping the strategic posture of entrepreneurs. Resource-constrained contexts vary significantly across regions. Our study contributes to the body of knowledge on disruptive innovations and small business sustainability in resource-constrained environments by investigating entrepreneurial dynamics in South Africa.

Design and Implementation of Energy-Aware Integration Mechanisms for IoT-Cloud Architectures: Enhancing Efficiency in Resource-Constrained Environments

This study paper suggests a way for the Internet of Things (IoT) and Cloud computing to work together that is smart about energy use. The goal is to solve the problem of IoT-Cloud integration's energy efficiency, which is important for making sensor nodes last longer and making sure that data transfer continues over time. The first step in the process is to plan the IoT nodes and give each sensor node a unique ID and a starting energy level. To make data safer, a homomorphic encryption method is used to make public and private keys. The integration process uses a good Cluster Head Selection Technique that has Random, Energy-Based, Distance-Based, and Density-Based methods to make sure that the sensor nodes are grouped together in the best way possible. The sensor nodes send data to the base station through cluster heads, which use homomorphic encryption to make the connection safe. The public key is used to hash each data packet before it is sent to the cluster head. The cluster head then sends the packets to the base station through the Cloud. At the base station, the data hash is checked one at a time, and then the data is decrypted. This makes sure that the data is correct and safe while it is being sent. Comparing time and energy use across different systems and node numbers is part of the performance assessment. The results show that System 4 did better than the others in terms of how much energy it used and how efficiently it transferred data. This shows that the proposed system can effectively handle IoT-Cloud integration. System 4 kept 28,848 units of energy after starting with 29,442 units, showing that it was much more energy efficient. The results show that using techniques that are aware of energy in IoT-Cloud systems can greatly improve performance. This study adds to the growing body of research on integrating IoT and the cloud by showing a safe, effective, and energy-conscious method that sends data while using the least amount of energy possible.

LightDPH: Lightweight Dual-Projection-Head Hierarchical Contrastive Learning for Skin Lesion Classification

Effective skin cancer detection is crucial for early intervention and improved treatment outcomes. Previous studies have primarily focused on enhancing the performance of skin lesion classification models. However, there is a growing need to consider the practical requirements of real-world scenarios, such as portable applications that require lightweight models embedded in devices. Therefore, this study aims to propose a novel method that can address the major-type misclassification problem with a lightweight model. This study proposes an innovative Lightweight Dual Projection-Head Hierarchical contrastive learning (LightDPH) method. We introduce a dual projection-head mechanism to a contrastive learning framework. This mechanism is utilized to train a model with our proposed multi-level contrastive loss (MultiCon Loss), which can effectively learn hierarchical information from samples. Meanwhile, we present a distance-based weight (DBW) function to adjust losses based on hierarchical levels. This unique combination of MultiCon Loss and DBW function in LightDPH tackles the problem of major-type misclassification with lightweight models and enhances the model’s sensitivity in skin lesion classification. The experimental results demonstrate that LightDPH significantly reduces the number of parameters by 52.6% and computational complexity by 29.9% in GFLOPs while maintaining high classification performance comparable to state-of-the-art methods. This study also presented a novel evaluation metric, model efficiency score (MES), to evaluate the cost-effectiveness of models with scaling and classification performance. The proposed LightDPH effectively mitigates major-type misclassification and works in a resource-efficient manner, making it highly suitable for clinical applications in resource-constrained environments. To the best of our knowledge, this is the first work that develops an effective lightweight hierarchical classification model for skin lesion detection.

The role of teamwork and mentorship in pediatric and congenital heart surgery.

Pediatric and congenital cardiac surgery, characterized by its complexity and high-stakes nature, demands superior technical expertise and multidisciplinary teamwork. With limited surgeons worldwide, the role of continuous skill refinement and collaborative practice is crucial for patient safety and successful outcomes. A literature search was performed using the PubMed, Cochrane, SCOPUS and Web of Science databases for studies published until September 2024. Articles were analysed to summarize findings on the impact of mentorship and teamwork within pediatric and congenital cardiac surgery. This article explores the dual importance of teamwork and mentorship in shaping the next generation of pediatric and congenital cardiac surgeons. The study examines the impact of mentorship on reducing learning curves and improving surgical outcomes whilst highlight the unique challenges faced by surgeons in low-income countries (LICs). Effective mentorship can serve as a bridge between knowledge and practice, ensuring that surgeons in resource-constrained environments can confidently adapt to challenges and improve patient outcomes despite the limited resources. By emphasizing a culture of innovation and collaborative teamwork, pediatric and congenital cardiac surgery continues to achieve increasingly better patient outcomes on a global scale. This study highlights how continuous mentorship and teamwork not only enhance technical proficiency but also drive advancements in surgical techniques and overall care, contributing to the ongoing global progress in pediatric and congenital cardiac surgery.

Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework

Context:Federated Learning (FL) has gained prominence as a solution for preserving data privacy in machine learning applications. However, existing FL frameworks pose challenges for software engineers due to implementation complexity, limited customization options, and scalability issues. These limitations prevent the practical deployment of FL, especially in dynamic and resource-constrained edge environments, preventing its widespread adoption. Objective:To address these challenges, we propose EdgeFL, an efficient and low-effort FL framework designed to overcome centralized aggregation, implementation complexity and scalability limitations. EdgeFL applies a decentralized architecture that eliminates reliance on a central server by enabling direct model training and aggregation among edge nodes, which enhances fault tolerance and adaptability to diverse edge environments. Methods:We conducted experiments and a case study to demonstrate the effectiveness of EdgeFL. Our approach focuses on reducing weight update latency and facilitating faster model evolution on edge devices. Results:Our findings indicate that EdgeFL outperforms existing FL frameworks in terms of learning efficiency and performance. By enabling quicker model evolution on edge devices, EdgeFL enhances overall efficiency and responsiveness to changing data patterns. Conclusion:EdgeFL offers a solution for software engineers and companies seeking the benefits of FL, while effectively overcoming the challenges and privacy concerns associated with traditional FL frameworks. Its decentralized approach, simplified implementation, combined with enhanced customization and fault tolerance, make it suitable for diverse applications and industries.

Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

Innovative approaches to the maintenance and repair of biomedical devices in resource-limited settings

This review paper examines innovative approaches to maintaining and repairing biomedical devices in resource-limited settings, emphasizing the critical role these devices play in healthcare delivery. It addresses the challenges healthcare facilities face in these regions, such as the lack of spare parts, limited technical expertise, and harsh environmental conditions. The paper highlights several innovative strategies, including using locally sourced materials, developing low-cost, adaptable tools, remote support systems, and targeted training programs for local technicians. These approaches significantly improve device functionality, reduce downtime, and enhance the reliability and availability of essential medical equipment. By fostering collaboration between local technicians and external experts and building local capacity, these strategies contribute to better patient care and overall healthcare delivery. The paper also discusses prospects and recommendations for sustaining these innovations to strengthen healthcare systems in resource-constrained environments.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

Detection of Porcine Circovirus (PCV) Using CRISPR-Cas12a/13a Coupled with Isothermal Amplification.

The impact of porcine circovirus (PCV) on the worldwide pig industry is profound, leading to notable economic losses. Early and prompt identification of PCV is essential in managing and controlling this disease effectively. A range of detection techniques for PCV have been developed and primarily divided into two categories focusing on nucleic acid or serum antibody identification. The methodologies encompass conventional polymerase chain reaction (PCR), real-time fluorescence quantitative PCR (qPCR), fluorescence in situ hybridization (FISH), loop-mediated isothermal amplification (LAMP), immunofluorescence assay (IFA), immunohistochemistry (IHC), and enzyme-linked immunosorbent assay (ELISA). Despite their efficacy, these techniques are often impeded by the necessity for substantial investment in equipment, specialized knowledge, and intricate procedural steps, which complicate their application in real-time field detections. To surmount these challenges, a sensitive, rapid, and specific PCV detection method using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas12a/13a coupled with isothermal amplification, such as enzymatic recombinase amplification (ERA), recombinase polymerase amplification (RPA), and loop-mediated isothermal amplification (LAMP), has been developed. This novel method has undergone meticulous optimization for detecting PCV types 2, 3, and 4, boasting a remarkable sensitivity to identify a single copy per microliter. The specificity of this technique is exemplary, with no observable interaction with other porcine viruses such as PEDV, PRRSV, PRV, and CSFV. Its reliability has been validated with clinical samples, where it produced a perfect alignment with qPCR findings, showcasing a 100% coincidence rate. The elegance of merging CRISPR-Cas technology with isothermal amplification assays lies in its on-site testing without the need for expensive tools or trained personnel, rendering it exceptionally suitable for on-site applications, especially in resource-constrained swine farming environments. This review assesses and compares the process and characteristics inherent in the utilization of ERA/LAMP/RPA-CRISPR-Cas12a/Cas13a methodologies for the detection of PCV, providing critical insights into their practicality and effectiveness.

Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation

This paper presents an improved genetic algorithm focused on multi-threshold optimization for image segmentation in digital pathology. By innovatively enhancing the selection mechanism and crossover operation, the limitations of traditional genetic algorithms are effectively addressed, significantly improving both segmentation accuracy and computational efficiency. Experimental results demonstrate that the improved genetic algorithm achieves the best balance between precision and recall within the threshold range of 0.02 to 0.05, and it significantly outperforms traditional methods in terms of segmentation performance. Segmentation quality is quantified using metrics such as precision, recall, and F1 score, and statistical tests confirm the superior performance of the algorithm, especially in its global search capabilities for complex optimization problems. Although the algorithm’s computation time is relatively long, its notable advantages in segmentation quality, particularly in handling high-precision segmentation tasks for complex images, are highly pronounced. The experiments also show that the algorithm exhibits strong robustness and stability, maintaining reliable performance under different initial conditions. Compared to general segmentation models, this algorithm demonstrates significant advantages in specialized tasks, such as pathology image segmentation, especially in resource-constrained environments. Therefore, this improved genetic algorithm offers an efficient and precise multi-threshold optimization solution for image segmentation, providing valuable reference for practical applications.

Optimized Design of Direct Digital Frequency Synthesizer Based on Hermite Interpolation

To address the issue of suboptimal spectral purity in Direct Digital Frequency Synthesis (DDFS) within resource-constrained environments, this paper proposes an optimized DDFS technique based on cubic Hermite interpolation. Initially, a DDFS hardware architecture is implemented on a Field-Programmable Gate Array (FPGA); subsequently, essential interpolation parameters are extracted by combining the derivative relations of sine and cosine functions with a dual-port Read-Only Memory (ROM) structure using the cubic Hermite interpolation method to reconstruct high-fidelity target waveforms. This approach effectively mitigates spurious issues caused by amplitude quantization during the DDFS digitalization process while reducing data node storage units. Moreover, this paper introduces single-quadrant ROM compression technology to further diminish the required storage space. Experimental results indicate that, compared to traditional DDFS methods, the optimization scheme proposed in this work achieves a ROM resource compression ratio of 1792:1 and a 14-bit output Spurious-Free Dynamic Range (SFDR) of −88.134 dBc, effectively enhancing amplitude quantization precision and significantly lowering spurious levels. This significantly improves amplitude quantization precision and reduces spurious levels. The proposed scheme demonstrates notable advantages in both spectral performance and resource utilization efficiency, making it highly suitable for resource-constrained embedded systems and high-performance applications such as radar and communication systems.

Comparative analysis of lattice-based cryptographic schemes for secure IoT communications

This paper presents comparative analysis of lattice-based cryptographic schemes, focusing on their runtime and memory performance to evaluate their suitability for secure IoT communications. Specifically, analyzing FrodoKEM, Kyber, and sntrup761, assessing their key generation, encapsulation, and decapsulation operations across various IoT processors. Kyber schemes, particularly Kyber512 and Kyber1024, demonstrate the highest efficiency in terms of battery utilization and computational speed, making them ideal for highly constrained IoT devices. FrodoKEM variants, while providing robust security, show moderate performance, suitable for devices with more processing capacity. sntrup761, however, is less efficient, highlighting the need for careful selection of cryptographic schemes based on specific IoT requirements. The findings underscore the importance of balancing security, speed, and resource usage in PQC implementations, with Kyber schemes emerging as the most promising candidates for secure, resource-constrained IoT environments.