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Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/iot/index

Editor(s)-in-Chief: Shancang Li and Der Jiunn Deng

EAI Endorsed Transactions on Internet of Things is open access, a peer-reviewed scholarly journal focused on all areas related to the technologies and application fields related to the Internet of Things. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications on a quarterly frequency. Authors are not charged for article submission and processing.

INDEXING: Scopus, DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

The scope of the Transactions encompasses latest advances in the growing field of the Internet of Things (IoT), with the topics of interest being:

  • Technologies & Protocols: sensors, energy harvesters, RFID, machine-type communications, medium access protocols, radio resource management, industrial control, real-time systems, embedded software design.

  • Architectures & Services: cloud-based platforms, big data processing engines, security, privacy, device management, CPS/SCADA platforms, innovative/open service architectures, data mining, service middleware, semantic service management.

  • Applications & Use Cases: smart cities, smart buildings, smart traffic, industrial internet of things, smart metering, smart agriculture, etc.

  • Standardisation Landscape: IEEE, IETF, 3GPP, ETSI, and various alliances.

  • Experimentation & Prototypes: real-world deployments at scale, industrial deployments, experiences with prototypes (including what didn’t work), etc.

Call for Papers: Special issue on IoT, Machine Learning and Data Analytics for Smart Environment (Manuscript submission deadline: 2022-08-15; Notification of acceptance: 2022-09-30; Submission of final revised paper: 2022-10-30; Publication of special issue (tentative): 2023-01-01)

Lead Guest Editor: Mourade Azrour (Moulay Ismail University, Morocco) Guest Editor: Azeem Irshad (Faculty of Computer Science, Asghar Mall College Rawalpindi, HED, Govt. of the Punjab, Pakistan) Guest Editor: Azidine Guezzaz (Cadi Ayyad University, Morocco) Guest Editor: Jamal Mabrouki (Mohammed V University in Rabat, Faculty of Science, Morocco) Guest Editor: Said Benkirane (Cadi Ayyad University, Morocco;)

  • Editors-in-Chief
  • Shancang Li, University of the West of England - UWE Bristol, UK
  • Der Jiunn Deng, National Changhua University of Education, Taiwan
  • Area Editors
  • Antonella Molinaro (University Mediterranea of Reggio Calabria, Italy)
  • Atta Quddus (University of Surrey, UK)
  • Joachim Sachs (Ericsson, Sweden)
  • Kan Zheng (BUPT, China)
  • Liljana Gavrilovska (University Sts. Cyril and Methodius, Macedonia)
  • Maria Rita Palattella (University of Luxembourg, Luxembourg)
  • Rose Qingyang Hu (Utah State University, USA)
  • Xuan Liu (Future Network Research Center, Southeast University, China)
  • Chien-Liang Chen (Overseas Chinese University, Taichung, Taiwan)
  • Yu-Liang Liu (Overseas Chinese University, Taiwan)
  • Thomas Watteyne (INRIA, France; Linear Technologies, USA)
  • Pietro Manzoni (Universitat Politècnica de València)
  • Ahmet Çağdaş Seçkin (Uşak University, Turkey)
  • Rafael Romero-Carazas (Research Directorate of the College of Public Accountants of Arequipa, Peru)
  • Xavi Vilajosana (UOC, Spain; Worldsensing, Spain)
  • Qindong Sun (Xi’an Jiaotong University, China)
  • Ripon Patgiri (National Institute of Technology Silchar, India)
  • Shanshan Zhao (University of West England, UK)
  • Kamran Ali (Beijing University of Posts & Telecommunications, China)
  • Muddesar Iqbal (London South Bank University, UK)
  • Yun Zhao (Shanxi Agricultural University, China)

  • An Improved & Smart Clock Synchronisation Model for Emblematic IoT Applications

    Appears in:
    IoT 25(1):

    Authors:
    Ashwani Kumar Dubey, Divya Upadhyay

    Published:
    18th Jun 2025

    Abstract:
    The Internet of Things (IoT) has revolutionized modern living by facilitating seamless data exchange between interconnected devices across diverse applications such as healthcare, smart cities, and industrial automation. These devices operate in dynamic and distributed environments, where accurate timekeeping is crucial for synchronizing processes, ensuring reliable communication, and maintaining data consistency. Clock synchronization plays a critical role in coordinating the activities of IoT entities, especially when processing and communication require precision. To address challenges associated with synchronization errors, this paper introduces a novel clock synchronization algorithm grounded in linear quadratic regression. By leveraging a linear model to estimate clock parameters such as skew and offset, the algorithm improves the reliability and accuracy of time synchronization in IoT networks. The effectiveness of the proposed algorithm was evaluated using key statistical metrics, including R-Square and Root Mean Square Error (RMSE). The results demonstrated the superiority of the algorithm, achieving an R-Square error value of 0.71% and an RMSE of 0.379%, outperforming traditional synchronization methods. Furthermore, the stability and robustness of the model were validated through a correlation coefficient analysis, which revealed a strong correlation of 86% between the variables. These findings underscore the algorithm's potential to significantly reduce synchronization errors, thereby enhancing the efficiency and reliability of IoT applications. By addressing a critical challenge in IoT communication, this research contributes to the advancement of time-sensitive applications and underscores the importance of innovative synchronization mechanisms in the growing IoT ecosystem.

  • Effective Object detection and Tracking using Attention-Driven YOLO v9 Model with Multi-Stage Cascaded Convolutional Model

    Appears in:
    IoT 25(1):

    Authors:
    P. V. N. Reddy, Krishna Mohan A, K. Satyma Prasad

    Published:
    11th Jun 2025

    Abstract:
    INTRODUCTION: Object detection and tracking are essential for computer vision, particularly for vehicle monitoring within digital images and video streams. Traditional methods, such as background subtraction and template matching, rely on heuristic algorithm and handcrafted features, which often struggles with diverse vehicle appearance and complex backgrounds. These techniques, while foundational, exhibit limitations in flexibility and scalability, resulting in lower accuracy and high computational costs. OBJECTIVES: In contrast, advanced Deep Learning (DL) approaches, particularly those utilizing Conventional Neural Network (CNNs), have revolutionized the field by enabling automatic feature extraction from large datasets.  Despite their advantages, existing DL models like You Only Look Once (YOLO) face challenges in detecting small or closely packed vehicles and can be computationally intensive. METHODS:  This study proposed an Attention Driven YOLO v9 architecture that integrates with a proposed mechanism combining spatial and channel attention to detect the small size vehicle accurately. RESULTS: Additionally the architecture incorporates multi stage cascaded convolution layers to enhance the feature extraction and robustness against occlusion and background noise. The model is trained using the UA-DETRAC dataset, providing a rich set of images for learning. CONCLUSION: Performance evaluation metric such as Mean Average Precision (mAP), precision, recall, and tracking accuracy demonstrating significant improvement over traditional methods and existing state of the art models. This research contributes to the field by addressing the limitations of previous studies through technique to speed and accuracy in vehicle detection and tracking.

  • Optimization of Deep Learning Technique for OFDM Receivers in 6G Wireless Communications

    Appears in:
    IoT 25(1):

    Authors:
    Kapula Kalyani Kalyani, Kasetty Lakshmi Narasimha, Anil Kumar R, Mummidi SubbaRaju, V. Saraswathi, M. Koteswara Rao

    Published:
    6th Jun 2025

    Abstract:
    INTRODUCTION: This paper presents an innovative deep learning-based optimization technique for orthogonal frequency division multiplexing (OFDM) receivers in wireless communication systems. OBJECTIVES: The proposed method utilizes an enhanced deep convolutional neural network (Enhanced DCNN) architecture with a time-frequency domain fusion mechanism to address the issues of interference and temporal information loss. The model incorporates attention mechanisms and causal convolutions to extract long-term dependencies within the received OFDM signals. It enables accurate channel estimation and signal recovery. METHODS: The methodology is validated using simulations based on 3GPP-defined channel models. It includes extended typical U (ETU), extended pedestrian A (EPA) and extended vehicular A (EVA) across varying signal-to-noise ratio (SNR) conditions. RESULTS: Results demonstrate that the proposed receiver significantly improves bit error rate (BER) performance compared to traditional Least Squares (LS) and LMMSE methods. Particularly, in scenarios with large delay spreads and high mobility. Additionally, the model has a lower computational complexity (CC) and thus is appropriate for real-time implementation. CONCLUSION: We view this work as a strong scheme to improve the performance of OFDM systems in future wireless networks.

  • Facial Recognition Enabled Smart Security Lock System Using Machine Learning Approach

    Appears in:
    IoT 25(1):

    Authors:
    J. Akilandeswari, M. Marimuthu, V. Sathiyapriya, G. Mohanraj

    Published:
    5th Jun 2025

    Abstract:
    INTRODUCTION: In today's modern world of networking and intelligent devices, there is an imperative need to upgrade everyday things and make them intelligent; additionally, this is not the era to unquestioningly trust old and traditional security measures, particularly when it comes to smart door lock systems. Mostly, every smart door lock system has a security access code or fingerprint access outside the door that makes it vulnerable. The password in a classic security system can be readily hacked with advanced technology and, therefore, is no longer suitable for today's real-time environment. OBJECTIVES: As a result, this paper intends to provide enhanced security for the user through facial recognition using a machine-learning approach with high accuracy and remote access via an Android application. Automated solutions leveraging machine learning have shown to be quite effective in security. METHODS: Machine Learning (ML) algorithms are used to train different sets of images to identify and classify various sorts of faces. The specific algorithm that is to be used is Dlib and Support Vector Machine (SVM); Dlib is utilized for face recognition along with HOG (Histogram of Oriented Gradients), whereas SVM is used for image classification, which is used to authorize the personnel. RESULTS: When compared to other cutting-edge methodologies, empirical results reveal that the proposed approach achieves 96% accuracy rate, with a recognition speed of 0.5 seconds per face in facia recognition which is more effective, reliable, and utilizes fewer resources. CONCLUSION: Real-time face recognition enables quick and secure identification, supporting multi-factor authentication and monitoring. The proposed smart lock system uses the HOG+SVM approach, achieving higher accuracy and faster face detection.

  • Enhanced security based on Context Aware Proactive Trust Aware Routing Protocol using secure Preemptive Verifiable Key Handover Policy for VANET Environment

    Appears in:
    IoT 25(1):

    Authors:
    R Nagaraj , K N Anupama

    Published:
    28th May 2025

    Abstract:
    INTRODUCTION: Road safety improvements through Vehicular Ad-hoc Networks (VANETs) occur because these networks enable vehicles to exchange messages with Road Side Units (RSUs). The secure exchange of data in VANET evolves as a fundamental challenge in practical systems because nature-based implementation problems combine with protection threats from both data failure and sabotage-based message releases. OBJECTIVES: This research develops a new routing protocol to strengthen VANET communication security processes by filling existing vulnerabilities and building reliable data transmission methods. METHODS: CAPTARP represents a routing protocol which integrates PVKHP as its Secure Preemptive Verifiable Key Handover Policy. Data from RSU communications first needs collection before the lookup environment can be established. The Payload Data Impact Rate (PDIR) detects both transmission defects and congestion through analysis. The Transmission Behavioral Delay Tolerance Rate detects the patterns of deliberate transmission anomalies. SRSU-FIS operates as a security enhancement mechanism for routing decisions while maintaining context-based security protection capabilities. RESULTS: Test simulations show that the proposed system delivers better results than current approaches by achieving 94.19% throughput alongside 96.03% packet delivery ratio while reaching 95.03% security performance and 95.24% authentication level and maintaining 18.06 ms end-to-end delay. CONCLUSION: The combination of CAPTARP protocol with PVKHP protocol effectively strengthens VANET security by providing better authentication coupled with trust validation and rapid communication procedures. The security performance surpasses traditional methods because this innovative approach represents a strong solution for protecting vehicular communication networks.

  • Advanced Attention-Enhanced BiLSTM-GRU Model for Real vs. Fake News Detection

    Appears in:
    IoT 25(1):

    Authors:
    B. Senthilkumaran , M. Arunkrishna

    Published:
    20th May 2025

    Abstract:
    INTRODUCTION: Social media has become one of the primary platforms for the rapid dissemination of both real and fake information. In today's digital society, individuals are often easily influenced by misleading content, making fake news a powerful tool for manipulation. A common strategy employed on social media is the intentional spread of false information with the aim of deceiving and misleading the public. OBJECTIVES: In some cases, this misinformation is so convincing that it significantly influences public opinion and behaviour, with long-term consequences. According to research, only 54% of people are capable of detecting deception without external assistance. To address this growing concern, the proposed model leverages Deep Learning techniques to identify fake news content in both Tamil and English languages. METHODS: The dataset for this study was sourced from an open-access repository on GitHub. After thorough pre-processing, the dataset was divided into training (80%) and testing (20%) subsets. Additionally, the proposed modified attention-based revamping weighted BiLSTM-GRU model is introduced and evaluated. RESULTS: The effectiveness of each model is measured using various performance metrics, including accuracy, precision, recall, F1-score, Bilingual Evaluation Understudy, Area under the ROC Curve, and the Receiver Operating Characteristic curve. Comparative analysis shows that the proposed model outperforms the existing methods across all evaluation parameters. CONCLUSION: A key advantage of the proposed approach is its capability to accurately detect fake text in both Tamil and English. Furthermore, the model demonstrates strong performance when tested on datasets generated using AI tools like ChatGPT, effectively identifying real and fake content with high precision.

  • Synergistic Integration of Quantum and Classical Machine Learning Models for High-Fidelity Asteroid Hazard Detection

    Appears in:
    IoT 25(1):

    Authors:
    Amol A. Bhosle, Kuldeep Vayadande, Geetanjali Yadav, Yogesh Bodhe, Ajit R. Patil, Preeti Bailke, Kavita Sultanpure, Jyoti Jayesh Chavhan, Amolkumar N. Jadhav

    Published:
    30th Apr 2025

    Abstract:
    This research investigates the use of quantum machine learning (QML) to classify asteroids into non-hazardous and hazardous groups, which yields successful results in detecting the hazard. In addition to the complexity involved in analyzing orbits and physical objects, QML performs better than traditional machine learning in modeling the relationship between data. This method involves data-intensive preprocessing steps, such as feature selection by removing unnecessary features and correlation analysis to find predictors. Quantum circuits are used for specification and classification, and the standard evaluation is based on accuracy, recall, F1 score, and precision. A strong and weak method is provided by cross-validation and hyperparameter tuning. The best classical model here is the decision tree, which is a good model for high-resolution and low-budget social benefit with 0.883 accuracy, 0.955 recovery rate, 0.981 F1 score, and 0.883 sensitivity. However, the quantum model has made great leaps. AMSGRAD QCNN (Adaptive Moment Estimation with Gradient Thresholding Quantum Convolutional Neural Networks) achieves a non-uniform accuracy of 0.997, which is 13% higher than the decision tree with 0.984 accuracy, 0.955 recovery rate, and 0.981 F1 score. The accuracy of SPSA QCNN(Simultaneous Perturbation Stochastic Approximation) is 0.993, the recall is 0.974, and the F1 score is 0.977. This improvement shows the excellent ability of the quantum model to better resolve correlation data and, more importantly, to reduce false negatives in detecting stars. These results demonstrate the ability of quantum computing to analyze complex data and provide the best results. Our future work will focus on identifying and repairing quantum circuits, as well as exploring hybrid quantum classical models to improve model accuracy and interpretation. The findings open the door to new ways to predict the simplest solutions and the most powerful and accurate way to date of estimating the danger zone.

  • Variation of Production Line Energy Consumption: Stochastic Process Models for Single and Multiple Machine Systems

    Appears in:
    IoT 25(1):

    Authors:
    Hao-Chu Lin, Hui Hsin Chin, Po-Jung Lai

    Published:
    15th Apr 2025

    Abstract:
    The energy consumption of production lines can vary across different machines due to diverse factors such as varying processing rates, machine conditions, product characteristics, and the compatibility between products and machines. Additionally, identical products on the same type of machine may experience temporal variations in energy consumption due to real-time equipment status and operational activities. Accurately understanding these influencing factors can aid in developing more efficient energy management strategies. This study first analyzes the energy consumption of single machines processing single products, establishing a probabilistic model for the stochastic process of single-machine energy consumption over time. Mathematical tools, including convolution, are then employed to develop a stochastic process model for energy consumption across multiple machines and products within the entire plant. The overall stochastic process model is optimized, leading to the creation of a "Peak Energy Consumption Alert" and an "Energy Efficiency Alert" system to mitigate the risks of peak energy consumption and overall energy inefficiency. By utilizing the proposed optimization model for machine component energy consumption and the production scheduling module, the likelihood of exceeding contracted power capacity is significantly reduced. This approach also enhances the energy efficiency of production scheduling, thereby reducing the total energy consumption of the entire plant. Furthermore, while meeting energy planning goals, the model considers non-energy-related production indicators (e.g., completion time, order delivery dates), ultimately improving the operational efficiency of production lines.

  • A Secure and Efficient Blockchain-Based Framework for Smart Cities Using Physics-Informed Neural Networks

    Appears in:
    IoT 25(1):

    Authors:
    R. Sasikala , Mohd. Asif Gandhi , N. Noor Alleema , Deepak Sundrani , M.P. Indumathi , Atul D Narkhede

    Published:
    14th Apr 2025

    Abstract:
    The massive scale and extensive implementation of the Internet of Things (IoT) makes it difficult to provide secure and private communications over it. Privacy and decentralisation have been made easier using blockchain technology. Unfortunately, these solutions aren't practical for the majority of IoT uses because of how much time and computing power they require. Secure and private IoT that makes efficient use of available resources is proposed in this study. With the use of Physics Informed Neural Networks, the technique takes advantage of the computing power available in IoT settings like smart cities. This solution examines the reliability of the blockchain-based Smart Cities Architecture with respect to accessibility, privacy, and integrity. When weighed against the security and privacy advantages our system offers, our simulation findings reveal that the overheads (distribution, processing time, and energy usage) are negligible.

  • Interlink Platform for School, Higher and Technical Education in India: Design Platform

    Appears in:
    IoT 25(1):

    Authors:
    Saloni Rangari, Pratham Chopde, Mansi Choudhari, Pratham Badge, Nitesh Ghodichor

    Published:
    13rd Apr 2025

    Abstract:
    This research paper aims to fill the knowledge gap in understanding the factors that contribute to academic performance and dropout rates in the Indian education system. The study proposes a “Interlinked platform for school, higher and technical education in India”, a unified digital space for students, educators, administrators and government agencies. The platform is designed to track students' academic performance across different educational levels and visualize dropout rates. The research uses a comprehensive methodology that integrates data analysis techniques and visualization frameworks and uses Python libraries such as NumPy, Pandas, Matplotlib and Scikit-Learn. Student academic outcomes are analyzed using linear regression and K-means clustering, and dropout rates are predicted using logistic regression. The aim of the research is to provide institutions with valuable insights to understand the factors that contribute to dropout rates and to develop targeted interventions to address potential triggers of dropout.

  • Flood Monitoring and Early Warning Systems – An IoT Based Perspective

    Appears in:
    IoT 23(2): e4

    Authors:
    Dr Tasneem Ahmed, Mohammed Siddique, Mohammad Shahid Husain

    Downloads:
    3692

    Abstract:
    One of the most frequently occurring calamities around the world is the flood. For flood prone areas or countries, an essential part of their governance is flood management. The necessity to continuously review and analyse the adverse or ambient environmental conditions in real-time demands developing a monitoring system so that floods could be detected beforehand. This paper discusses different Internet of Things (IoT) based techniques and applications implemented for efficient flood monitoring and an early warning system and it is observed that in future, the combination of IoT and Synthetic Aperture Radar (SAR) data may be helpful to develop robust and secure flood monitoring and early warning system that provides effective and efficient mapping during natural disasters. The emerging technology in the discipline of computing is IoT, an embedded system that enables devices to gather real-time data to further store it in the computational devices using Wireless Sensor Networks (WSN) for further processing. The IoT based projects that can help collect data from sensors are an added advantage for researchers to explore in providing better services to people. These systems can be integrated with cloud computing and analyzing platforms. Researchers recently have focussed on mathematical modeling based flood prediction schemes rather than physical parametric based flood prediction. The new methodologies explore the algorithmic approaches. There have been many systems proposed based on analog technology to web-based and now using mobile applications. Further, alert systems have been designed using web-based applications that gather processed data by Arduino Uno Microcontroller which is received from ultrasonic and rain sensors. Additionally, the machine learning based embedded systems can measure different atmospheric conditions such as temperature, moisture, and rains to forecast floods by analyzing varying trends in climatic changes.

  • Smart Cane: A Low Cost Assistive Device for the Visually Impaired

    Appears in:
    IoT 23(4): e5

    Authors:
    Md. Rakibuzzaman , Taslima Jannat Limu, Wohiduzzaman Kazi

    Downloads:
    1669

    Abstract:
    Vision impairment or low vision is the disability to see any objects clearly. It might be caused by old age, an accident, or any other neurological condition. People find it difficult to carry out their regular activities, regardless of the cause of their vision impairment or blindness. As medical researchers are working to find a cure for blindness, contrariwise researchers are using technology to make a vision impairment person’s life easier in an affordable way. As a result, a smart cane was developed, although it is expensive for most of blind people. The main objective of this paper is to introduce an affordable smart cane for every visually impaired person. The ultrasonic sensors in this walking stick are used to identify impediments. This system is using an arduino to reduce the processing time between receiving and delivering signals created by ultrasonic sensors. The speech output will guide a blind person to take a safe path making the stroll more comfortable and secure.

  • POX and RYU Controller Performance Analysis on Software Defined Network

    Appears in:
    IoT 23(3): e5

    Authors:
    Naimullah Naim, Amir Ullah Khan, Shabir Khan, Muhammad Bilal Afzal, Muhammad Abul Hassan, Muhammad Imad

    Downloads:
    1024

    Abstract:
    From the last decades different types of network schemes are pitched to enhance the user performance. Software Defined Networks (SDN) is also considered as important factor for different network schemes and its proper administration or management. Due to major deployment in today’s networking era SDN are further sub divided in to commercial and open-source controllers. Commercial and open-source controllers are utilized in different type of businesses. According to our knowledge considerable amount of literature is available on these controllers but did not provide or analyse performance of these controllers on different network parameters. This paper evaluates and compares the performance of two well-known SDN open-source controllers POX and RYU with two performance assessments. The first assessment is the implementation of optimal path by using Dijkstra's algorithm from source to destination. Second assessment is the creation of a custom topology in our desired tool (MiniNet emulator). Then, the performance in terms of QoS parameters such as Jitter, throughput, packet loss, and packet delivery ratio are computed by two end hosts in each network. After the assessments, the performance of POX are optimal as compare to the RYU and best suited to be deployed in any scenario.

  • DDS-XRCE Standard Performance Evaluation of Different Communication Scenarios in IoT Technologies

    Appears in:
    IoT 22(4): e1

    Authors:
    Kerem Kucuk, Sevval Solpan

    Downloads:
    920

    Abstract:
    Although the increasing number of technological products brings many solutions for Internet of Things (IoT) applications, it also causes some drawbacks, such as whether the product in question would run accordingly to a system structured to enable high-performance like Data Distribution Service (DDS). Therefore, the capabilities of the products must be defined to say that they are compatible enough. This paper aims to evaluate the performance of the DDS-XRCE standard while observing its working mechanism. As test scenarios, we benefit from three DDS-XRCE deployments that occurred due to the kind of receiver and sender, the path that packets follow, and the protocols used. Test conditions were set by switching stream modes, transport profiles, and limiting packet deliveries. We obtained the test environment by creating the DDS and DDS-XRCE objects using several eProsima implementations and tools for the standards. We monitored the network messages in two ways: 1) Using multiple Gnome Terminator terminals for observation via the human eye during testing. 2) Using Wireshark to save the information of the packets for further examination. We conducted 36 experiments focusing on latency, throughput, and packet loss. As a result of our study, the DDS-XRCE standard is deemed suitable for Internet of Things applications.

  • Cyber Attacks Classification on Enriching IoT Datasets

    Appears in:
    IoT 23(3): e2

    Authors:
    Meltem Kurt Pehlivanoglu, Nassima Yousef Saleem Al Zubaidi, Alend Hasan Jarjis

    Downloads:
    893

    Abstract:
    In the era of the 5.0 industry, the use of the Internet of Things (IoT) has increased. The data generates from sensors through IoT industrial systems, any fault in those systems affects their performance and leads to real disaster. Protecting them from any possible attacks is an essential task. to secure any system, it needs to predict in the first place possible attacks and faults that could happen in the future. Predicting and initiating the attack type and the accuracy of these predictions can be done with machine learning models nowadays on the datasets produced with IoT networks. This paper classifies several attacks type based on several criteria and techniques to enhance the performance of machine learning (ML) models such as Voting techniques beside six ML models; Random Forest (RF), Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) using Enriching IoT dataset. The results showed that 100% accuracy was achieved in estimating process with the XGBoost model.    

  • FPGA implementation of sobel edge detection algorithm

    Appears in:
    IoT 24(1):

    Authors:
    R. Logesh, K. Navinkumar, Ananthalakshmi A.V., P. VishnuBabu

    Downloads:
    793

    Abstract:
    Sobel Edge detection algorithm is used to extract the edges (region of maximum variation) from an image. It is based on the concept that the edges of an image contains maximum information whose computation depends on multipliers and square root. As multipliers consume more logic, a modified sobel edge detection algorithm which does not employ multipliers and square root function is proposed. A mathematical model of the proposed sobel edge algorithm was first developed and MATLAB was used to verify the model. On comparing with the original model, the proposed model has a SSIM of 96.43%. To analyse the hardware complexity, Verilog model of the modified sobel edge detection algorithm was developed using Quartus II. The chosen evaluation board is Cylone III FPGA EP3C120F780. The performance metrics such has Logic Elements utilization, Power dissipation and Maximum Operating Frequency were obtained. Open-Source toolchain (Yosys, OpenVPR, and Google Skywater 130nm PDK) was used to obtain the RTL Netlist and Synthesis reports. Verilog Modules for the Camera (CMOS OV7670) interface and FIFO Buffer were synthesized. The modified algorithm was integrated with them. An HSMC (HSMB) breakout board was connected to the FPGA Development board to increase the number of I/O ports. Thus in real time, the proposed modified Sobel Edge detection system can be used as a pre-processor to reduce the amount of computations and power consumption.

  • Water Quality Estimation and Anomaly Detection: A Review

    Appears in:
    IoT 23(4): e2

    Authors:
    Seda Balta Kaç, Musa Balta, Süleyman Eken, Deniz Balta

    Downloads:
    742

    Abstract:
    Critical infrastructures that provide irreplaceable services are systems that contain industrial control systems (ICS) that can cause great economic losses, security vulnerabilities and disruption of public order when the information in it is corrupted. These ICSs, which were previously isolated, have now become systems that contain online sensors, wireless networks and artificial intelligence technologies. This situation has also increased the scope of attacks by malicious people who intend to carry out industrial espionage and sabotage these systems. In this study, water quality estimation systems and anomaly detection are comprehensively examined. In this direction, the statistics of the studies in the literature, the methods for water quality anomaly detection, the existing data sets, and the difficulties encountered in the water systems to achieve better water management are discussed. Principle findings of this research can be summarized as follows: (i) new methodologies and architectures have improved water quality assessment through anomaly detection, (ii) different datasets including multi-modal information have been presented, and (iii) remaining challenges and prospects have been investigated.

  • Comprehensive survey study on fifth-generation wireless network and the internet of things

    Appears in:
    IoT 23(3): e3

    Authors:
    Arthur Nathaniel Mwang'onda, Menard Phiri

    Downloads:
    605

    Abstract:
    Internet of Things is the interconnection of different components so that it should be able to communicate to each other to achieve a common goal. Therefore, for these components to communicate there will be a need of the communication Technologies. With the coming in of 5G wireless Network which provide more ability over the 2G,3G, 4G and other communication technologies. In this survey paper focuses much on what have been done before with 5G wireless Network in relation to Internet of Things, Application of 5G IoT, it also describes the various challenges which may arise when implementing it and the research questions. The next-generation protocol that will help to alleviate the current challenges which have been encountered. This paper will analyze the comprehensive survey on fifth-generation wireless network and Internet of Things from different papers and this will help to map the way forward on what best can apply 5G and other protocols in Internet of things.

  • Robustness of NMF Algorithms Under Different Noises

    Appears in:
    IoT 23(1): e4

    Authors:
    Zheng Han, Mengyao Kang, Jiawei Zhao

    Downloads:
    567

    Abstract:
    In machine learning, datasets are often disturbed by different noises. The Nonnegative Matrix Factorization (NMF) algorithm provides a robust method to deal with noise, which will significantly improve the efficiency of machine learning. In this investigation, the standard NMF algorithm and L2,1-Norm Based NMF algorithm are studied by designing experiments on different noise types, noise levels, and datasets. Furthermore, Relative Reconstruction Errors (RRE), accuracy, and Normalized Mutual Information (NMI) are used to evaluate the robustness of the two algorithms. In this experiment, there is no significant difference in performance between the two algorithms, while L2,1-Norm Based NMF algorithm shows relatively small advantages.

  • Implementation of Beeman's algorithm to calculate execution time on GPU using CUDA

    Appears in:
    IoT 22(4): e4

    Authors:
    Abdelkader Hadjoudja, Youness Rtal

    Downloads:
    562

    Abstract:
    Graphics processing units (GPUs) are microprocessors designed to the operation of display and manipulation of graphics data. . Currently, these graphics processor are found on all graphics hardware and have become very important instruments for parallel computing. GPUs are practical tools for the development of several fields like decoding and encoding, solving differential equations. Their advantages are increase in performance, faster data processing and reduced power consumption. It is simple to program a GPU with CUDA C to run parallel calculations. But it is necessary to have an understanding of the architectural aspects of the GPU and CUDA C. This paper, we will describe and implement Beeman's algorithm on GPU and CPU using CUDA C to solve the differential equation of charged particles in an electromagnetic field. Our goal is to evaluate the performances of the implementation on GPU and CPU processors and to deduce the efficiency of the use of GPUs.
Publisher
EAI
ISSN
2414-1399
Number of Volumes
10
Last Published
2024-01-01
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