IoT Applications Research Articles

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

An Evolving Multivariate Time Series Compression Algorithm for IoT Applications

An Evolving Multivariate Time Series Compression Algorithm for IoT Applications

Optimized Graph Sample and Aggregate‐Attention Network‐Based High Gain Meta Surface Antenna Design for IoT Application

ABSTRACTThis paper presents a High Gain Metasurface (HGM) antenna design optimized for IoT applications. The antenna operates at a 5.8 GHz frequency and utilizes a quarter‐wavelength unit cell structure. The design employs a Graph Sample and Aggregate‐Attention Network (GSAAN) to enhance the transmission characteristics of the metasurface. To optimize the performance of GSAAN, the Giza Pyramids Construction Algorithm (GPCN) is applied to adjust the weight parameters, leading to significant improvements in radiation efficiency, bandwidth, gain, directivity, and return loss. The proposed HGM antenna is simulated in MATLAB 2023b, where it demonstrates substantial performance gains over existing methods. Specifically, the proposed design achieves 28.34% higher gain compared to the MSA‐RHCOA method, 24.47% improvement over HGM‐AD‐MATCS, 26.34% better gain than BR‐HGA‐PGM, and a 32.12% gain increase relative to MSA‐Hyb‐ADSA‐HMSOA. These enhancements make the proposed antenna design a competitive solution for high‐gain applications in wireless communication, satellite communication, and radar systems. The integration of GSAAN with GPCN optimization in the HGM antenna design enables the creation of highly directive radiation patterns, making it well‐suited for IoT applications that require high performance and reliability. The results of this study demonstrate the effectiveness of the proposed approach in achieving superior antenna performance, positioning it as a strong alternative to existing metasurface antenna designs.

Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends

Federated learning (FL) is creating a paradigm shift in machine learning by directing the focus of model training to where the data actually exist. Instead of drawing all data into a central location, which raises concerns about privacy, costs, and delays, FL allows learning to take place directly on the device, keeping the data safe and minimizing the need for transfer. This approach is especially important in areas like healthcare, where protecting patient privacy is critical, and in industrial IoT settings, where moving large numbers of data is not practical. What makes FL even more compelling is its ability to reduce the bias that can occur when all data are centralized, leading to fairer and more inclusive machine learning outcomes. However, it is not without its challenges—particularly with regard to keeping the models secure from attacks. Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. As edge computing continues to grow, FL’s decentralized approach could play a key role in shaping how we handle data in the future, moving toward a more privacy-conscious world. This study identifies ongoing challenges in ensuring model security against adversarial attacks, pointing to the need for further research in this area.

MOTO-MASSA: multi-objective task offloading based on modified sparrow search algorithm for fog-assisted IoT applications

MOTO-MASSA: multi-objective task offloading based on modified sparrow search algorithm for fog-assisted IoT applications

A Low-Power 5-Bit Two-Step Flash Analog-to-Digital Converter with Double-Tail Dynamic Comparator in 90 nm Digital CMOS

Low-power portable devices play a major role in IoT applications, where the analog-to-digital converters (ADCs) are very important components for the processing of analog signals. In this paper, a 5-bit two-step flash ADC with a low-power double-tail dynamic comparator (DTDC) using the control switching technique is presented. The most significant bit (MSB) in the proposed design is produced by only one low-power DTDC in the first stage, and the remaining bits are generated by the flash ADC in the second stage with the help of an auto-control circuit. A control circuit produced reference voltages with respect to the control input and mid-point voltage (Vk). The proposed design and simulations are carried out using 90 nm CMOS technology. The result shows that the peak differential non-linearity (DNL) and integral non-linearity (INL) are +0.60/−0.69 and +0.66/−0.40 LSB, respectively. The signal-to-noise and distortion ratio (SNDR) for an input signal having a frequency of 1.75 MHz is found to be 30.31 dB. The total power consumption of the proposed design is significantly reduced, which is 439.178 μW for a supply voltage of 1.2 V. The figure of merit (FOM) is about 0.054 pJ/conversion step at 250 MS/s. The present design provides low power consumption and occupies less area compared to the existing works.

Balancing Security and Efficiency: A Power Consumption Analysis of a Lightweight Block Cipher

This research paper presents a detailed analysis of a lightweight block cipher’s (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications.

Design and Fabrication Development of Slotted T-Shaped Patch Antenna with Symmetrical CPW Feed for IOT and ITU Applications

Design and Fabrication Development of Slotted T-Shaped Patch Antenna with Symmetrical CPW Feed for IOT and ITU Applications

Dual-circular slotted low-profile ultrawideband textile MIMO antenna with ground slot stub for Sub-6G, ISM, and IoT applications

Dual-circular slotted low-profile ultrawideband textile MIMO antenna with ground slot stub for Sub-6G, ISM, and IoT applications

A Blockchain assisted fog computing for secure distributed storage system for IoT Applications

A Blockchain assisted fog computing for secure distributed storage system for IoT Applications

TaPaFuzz: Hardware-accelerated RISC-V bare-metal firmware fuzzing using rapid job launches

Fuzz testing serves as a key technique in software security aimed at identifying unexpected program behaviors by repeatedly executing the target program with auto-generated random inputs. Testing is integral to IoT device security but is hampered by the minimal observability features of typical in-market IoT devices. Moreover, the slow nature of a RISC-V software emulation on x86 host CPUs and the inaccuracies introduced by compiling IoT applications to a different ISA for execution on host systems pose significant challenges. Our software-hardware co-design surmounts these hurdles. Fuzzing jobs are prepared and evaluated on a host computer, while the actual execution with high-throughput tracing is performed on an FPGA. Advances in the host-to-FPGA interface together with an accelerated reset procedure between Fuzzer jobs effectively hide the costly host-FPGA communication, increasing the single-thread fuzzing performance by up to factor 11.7x that of the leading QEMU-based fuzzer AFL++ running on a very fast x86 CPU. We demonstrate practical usability by evaluating our framework on a collection of bare-metal applications.

Accelerating DTLS on SoC FPGA for secure IoT applications

Accelerating DTLS on SoC FPGA for secure IoT applications

Empowering sports facilities and sports physiotherapy performance with Integrated Management Information System (IMIS): Enhanced user experience and service quality

This study investigates the development of Integrated Management Information Systems (IMIS) to improve user experience and service quality in sports facilities. This research conducts a systematic review (2018-2023) of 7 articles from Scopus, Google Scholar, and Semantic Scholar to explore how IMIS can address evolving industry needs. The system facilitates user access to information, online transactions, and feedback mechanisms while enabling personalization and recommendations. However, the review also highlights the potential of IMIS to revolutionize sports physiotherapy through enhanced athlete care, data-driven treatment plans, and streamlined operations. Successful IMIS implementation requires careful planning, stakeholder involvement, and continuous adaptation to emerging technologies like AI and IoT. By overcoming challenges and embracing opportunities, IMIS can usher in a new era of personalized, effective, and efficient athlete care. IMIS implementation involves evaluating facility needs, staff training, and introducing the system to users. Data security and privacy are paramount. Future integration of AI, IoT, and mobile applications holds promise for further enhancing IMIS effectiveness. Overall, IMIS implementation has demonstrably improved user experience and service quality. By prioritizing user experience and adapting to technological advancements, sports facilities can remain competitive in a digital age.

Edge-Cloud Continuum: Integrating Edge Computing and Cloud Computing for IOT Applications

Abstract: This research will use a mixed-method approach with qualitative understanding and the quantitative analysis of data to examine how edge and cloud computing blend into the frame of an edge-cloud continuum for IoT applications. It adopts an exploratory and descriptive approach to research in carrying out a holistic assessment of the edge-cloud continuum in efficiency, scalability, and performance. The data required for collection was obtained through simulations using the IoTSim-Osmosis framework and also through the use of other case studies and literature published elsewhere before preparing this document. The data is usual to what has been used in several IoT deployments, including smart cities, the health sector, and industrial automation. Based on those KPIs - latency, 70.83%; bandwidth utilization, 40% reduced; energy consumption, 25% reduced; and task completion rate improved by 18.75% - the edge-cloud continuum significantly outperforms traditional cloud-only systems. Especially a very big reduction in latency is significantly important for real-time applications as it would drive the potential ability to improve responsiveness in those applications, like autonomous cars and smart healthcare. The current study demonstrates that the edge-cloud continuum can efficiently enhance resource allocation and enhance the effectiveness of complex IoT systems. This has wide implications for designing and implementing IoT solutions across industry domains

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid moth flame-shark smell optimization-based secure wireless communications for IoT applications

Hybrid moth flame-shark smell optimization-based secure wireless communications for IoT applications

A comprehensive review of different antennas for IoT applications

A comprehensive review of different antennas for IoT applications

Cyber-Physical Systems and IoT: Transforming Smart Cities for Sustainable Development

Smart cities CPS/ IoT are impacting sustainable development in modern cities. This research explore how CPS and IoT integration into infrastructure systems in cities can improve sustainability in aspects such as energy, waste, and environment. Finally, taking quantitative data on energy and pollution saving and resource utilization from the smart cities around the world, the paper quantifies the case analysis. The research method entails retrieving data from existing smart city projects and then using statistical tools to evaluate the differences in sustainability targets with and without CPS and IoT integration. The main discoveries indicate that places implementing CPS and IoT technologies have recorded reduction in energy use by up to 25 % and enhanced waste management by up to 30% thus exhibiting a reduced carbon footprint. The originality of this research is based on the application of CPS- IoT in analysing their practical implications for urban sustainability with relevant recommendations for policymakers, urban planners and technologists. This study not only serves as the research for the academic literature but also advises cities into how they would be able to build upon their sustainable initiatives through the adoption of advanced technology. The outcomes underscore the possibility of the strategic application of CPS and IoT to enhance the creation of smarter urban contexts.

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.