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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)

  • Real-Time Remote-Controlled Human Manipulation Medical Robot Using IoT Module

    Appears in:
    IoT 24(1):

    Authors:
    Bhawani Sankar Panigrahi, Priya Gupta, Susanta Kumar Sahoo, Swati Sucharita, R. Kishore Kanna, B Pravallika

    Published:
    5th Dec 2024

    Abstract:
    INTRODUCTION: Innovative robotics and advanced computer vision technology converge in the Human Manipulation-Controlled Robot, utilized for medical applications. The robot operates through human gestures, and includes a camera module for real-time visual feedback, enhancing its functionality and user interaction. OBJECTIVES: The primary goal of the research was to harness the natural expressiveness of human gestures to provide a more intuitive and engaging method of controlling medical robots. The focus is on enabling precise control through programmed responses to specific gestures, ensuring effective interaction with medical tasks. METHODS: The robot’s hardware configuration consists of a mobile platform with motorized components, an ESP32 module, gesture recognition sensors and a camera modules. The ESP32 module interprets signals from gesture recognition sensors to execute precise commands for the robot's movements and actions. Simultaneously, the camera module captures live footage, providing visual feedback through an intuitive interface for seamless interaction. RESULTS: The Human Manipulation-Controlled Robot has been successfully developed, featuring a fetch arm capable of autonomous movement and object manipulation. This research address critical needs in medical centers, demonstrating the feasibility of using only minimalistic EEG electrode wireless transmission to operate a robot effectively.  CONCLUSION: Through the provision of a more intuitive and engaging method of controlling and interacting with medical robots, this innovation has the potential to significantly improve user experience. It represents a most important development in medical robotic vehicles, enhancing user experience and operational efficiency through advanced human-robot interaction techniques.

  • A Mobile Lens: Voice-Assisted Smartphone Solutions for the Sightless to Assist Indoor Object Identification

    Appears in:
    IoT 24(1):

    Authors:
    V. Sivakumar, Talal Saleem

    Published:
    5th Dec 2024

    Abstract:
    Every aspect of life is organized around sight. For visually impaired individuals, accidents often occur while walking due to collisions with people or walls. To navigate and perform daily tasks, visually impaired people typically rely on white cane sticks, assistive trained guide dogs, or volunteer individuals. However, guide dogs are expensive, making them unaffordable for many, especially since 90% of fully blind individuals live in low-income countries. Vision is crucial for participating in school, reading, walking, and working. Without it, people struggle with independent mobility and quality of life. While numerous applications are developed for the general public, there is a significant gap in mobile on-device intelligent assistance for visually challenged people. Our custom mobile deep learning model shows object classification accuracy of 99.63%. This study explores voice-assisted smartphone solutions as a cost-effective and efficient approach to enhance the independent mobility, navigation, and overall quality of life for visually impaired or blind individuals.

  • A Self-Supervised GCN Model for Link Scheduling in Downlink NOMA Networks

    Appears in:
    IoT 24(1):

    Authors:
    Congsong Zhang, Fang Fang, Caiya Zhang

    Published:
    5th Dec 2024

    Abstract:
    INTRODUCTION: Downlink Non-Orthogonal Multiple Access (NOMA) networks pose challenges in optimizing power allocation efficiency due to their complex design. OBJECTIVES: This paper aims to propose a novel scheme utilizing Graph Neural Networks to address the optimization challenges in downlink NOMA networks. METHODS: We transform the optimization problem into an optimal link scheduling problem by modeling the network as a bipartite graph. Leveraging Graph Convolutional Networks, we employ self-supervised learning to learn the optimal link scheduling strategy. RESULTS: Simulation results showcase a significant enhancement in power allocation efficiency in downlink NOMA networks, evidenced by notable improvements in both average accuracy and generalization ability. CONCLUSION: Our proposed scheme demonstrates promising potential in substantially augmenting power allocation efficiency within downlink NOMA networks, offering a promising avenue for further research and application in wireless communications.

  • Privacy Preserving Authentication of IoMT in Cloud Computing

    Appears in:
    IoT 24(1):

    Authors:
    B. Hazela, B.K. Chaurasia, Garima Misra

    Published:
    5th Dec 2024

    Abstract:
    INTRODUCTION: The Internet of Medical Things (IoMT) blends the healthcare industry with the IoT ecosystem and enables the creation, collection, transmission, and analysis of medical data through IoT networking. IoT networks consist of various healthcare IT systems, healthcare sensors, and healthcare management software. OBJECTIVES: The IoMT breathes new life into the healthcare system by building a network that is intelligent, accessible, integrated, and effective. Privacy-preserving authentication in IoMT is difficult due to the distributed communication environment of heterogeneous IoMT devices. Although there has been numerous research on potential IoMT device authentication methods, there is still more to be done in terms of user authentication to deliver long-term IoMT solutions. However, password handling is one of the big challenges of IoMT. METHODS: In this paper, we present an IoMT-related online password-less authentication technique that is quick, effective, and safe. In order to offer cross-platform functionality, the article includes a simulation of FIDO2/WebAuthn, one of the most recent standards for a password-less authentication mechanism. RESULTS: This makes it easier to secure user credentials and improve them while preserving anonymity. The IoMT device authentication process and registration process delays are also assessed. CONCLUSION: Results and simulations show that the efficacy of the proposed mechanism with quick authentication on cloud servers may be accomplished with the fewest registration and authentication procedures, regardless of device setup.

  • Developing a Deep Learning-Based Multimodal Intelligent Cloud Computing Resource Load Prediction System

    Appears in:
    IoT 24(1):

    Authors:
    Ruey-Chyi Wu

    Published:
    5th Dec 2024

    Abstract:
    This study aims to predict the dynamic changes in critical cloud computing resource indicators, namely Central Processing Unit (CPU), Random Access Memory (RAM), hard disk (Disk), and network. Its primary objective is to optimize resource allocation strategies in advance to enhance overall system performance. The research employs various deep learning algorithms, including Simple Recurrent Neural Network (SRNN), Bidirectional Simple Recurrent Neural Network (BiSRNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Through experimentation with different algorithm combinations, the study identifies optimal models for each specific resource indicator. Results indicate that combining CNN, LSTM, and GRU yields the most effective predictions for CPU load, while CNN and LSTM together are optimal for RAM load prediction. For disk load prediction, GRU alone proves optimal, and BiSRNN emerges as the optimal choice for network load prediction. The training results of these models demonstrate R-squared values (R²) exceeding 0.98, highlighting their high accuracy in predicting future resource dynamics. This precision facilitates timely and efficient resource allocation, thereby enhancing system responsiveness. The study's multimodal precise prediction capability supports prompt and effective resource allocation, further enhancing system responsiveness. Ultimately, this approach significantly contributes to sustainable digital advancement for enterprises by ensuring efficient resource allocation and consistent optimization of system performance. The study underscores the importance of integrating advanced deep learning techniques in managing cloud computing resources, thereby supporting the robust and sustainable growth of digital infrastructure.

  • A Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT

    Appears in:
    IoT 24(1):

    Authors:
    Shreyansh Padarha, Vijayalakshmi S.

    Published:
    5th Dec 2024

    Abstract:
    Generative AI platforms like ChatGPT have leapfrogged in terms of technological advancements. Traditional methods of scrutiny are not enough for assessing their technological efficacy. Understanding public sentiment and feelings towards ChatGPT is crucial for pre-empting the technology’s longevity and impact while also providing a silhouette of human psychology. Social media platforms have seen tremendous growth in recent years, resulting in a surge of user-generated content. Among these platforms, Reddit stands out as a forum for users to engage in discussions on various topics, including Generative Artificial Intelligence (GAI) and chatbots. Traditional pedagogy for social media sentiment analysis and opinion mining are time consuming and resource heavy, while lacking representation. This paper provides a novice multifrontal approach that utilises and integrates various techniques for better results. The data collection and preparation are done through the Reddit API in tandem with multi-stage weighted and stratified sampling. NLP (Natural Language processing) techniques encompassing LDA (Latent Dirichlet Allocation), Topic modelling, STM (Structured Topic Modelling), sentiment analysis and emotional analysis using RoBERTa are deployed for opinion mining. To verify, substantiate and scrutinise all variables in the dataset, multiple hypothesises are tested using ANOVA, T-tests, Kruskal–Wallis test, Chi-Square Test and Mann–Whitney U test. The study provides a novel contribution to the growing literature on social media sentiment analysis and has significant new implications for discerning user experience and engagement with AI chatbots like ChatGPT.

  • FPGA implementation of sobel edge detection algorithm

    Appears in:
    IoT 24(1):

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

    Published:
    5th Dec 2024

    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.

  • Application of augmented reality in automotive industry

    Appears in:
    IoT 24(1):

    Authors:
    Javier Gonzalez-Argote, Adrián Alejandro Vitón-Castillo, Denis Gonzalez-Argote

    Published:
    5th Dec 2024

    Abstract:
    Introduction: Augmented reality is defined as a direct or indirect vision of a physically real environment, parts of which they are enriched with additional digital information relevant to the object that is being looked at. In the field of engineering design, there is a wide range of industries that use this technology, such as automotive, aircraft manufacturing, electronics, engineering; so that it has gained popularity in assembly, maintenance and inspection tasks. The objective was to characterize the use of augmented reality in the automotive industry. Methods: a total of 20 articles in Spanish and English were reviewed, from Scopus, Science and Dialnet; Using as keywords: augmented reality, automotive industry, manufacturing, being more than 50 % of the last five years. Result: its main advantage is considered its potential as an interactive and intuitive interface. It promises to provide the correct information to the human operator at the right time and place. If it is considered an ideal environment in which the RA is applied safely, in adequate balance between automated processes and human control over them; The level of production and its quality will be positively affected. Conclusions: Augmented reality is applied in areas of the automotive industry such as logistics design, assembly, maintenance, evaluation, diagnosis, repair, inspection, quality control, instruction and marketing; in order to guarantee better work performance, productivity and efficiency, mainly mediated by portable devices. Its degree of acceptance, although growing, is not yet clear.

  • Internet of Things and Health: A literature review based on Mixed Method

    Appears in:
    IoT 24(1):

    Authors:
    William Castillo-Gonzalez, Alfredo Javier Pérez Gamboa, Javier Gonzalez-Argote, Adrián Alejandro Vitón-Castillo, Verenice Sánchez Castillo, Ana Maria Chaves Cano

    Published:
    5th Dec 2024

    Abstract:
    The integration of technological advances into health sciences has promoted their development, but also generated setbacks and difficulties for digital transformation. In different areas, technology has modified the processes of diagnosis, teaching and learning, treatment and monitoring, which is why the study of new technologies and the models that support their introduction is essential. Internet of Things is one of these models, which, in turn, includes different models, devices and applications. Due to its breadth of exploitation options and benefits, in the health area this concept has been adopted and particularized as the Internet of Medical Things. With the purpose of achieving an approximation to the main trends and characteristics, a literature review study was conducted, based on mixed methods. Two studies were carried out with a sequential strategy, the first being bibliometric and the second a scoping review. The main results allowed us to describe the main trends in terms of bibliometric indicators, a thematic analysis in terms of areas, populations, benefits and limitations. It is concluded that there is a need for new interdisciplinary studies and lines for future research are presented.

  • Big Mart Sales Prediction using Machine Learning

    Appears in:
    IoT 24(1):

    Authors:
    Minnu Helen Joseph, Koh Ya Wen, V. Sivakumar

    Published:
    5th Dec 2024

    Abstract:
    INTRODUCTION: Sales prediction, also known as revenue forecasting or sales forecasting, refers to the process of accurately and timely estimating future revenue for manufacturers, distributors, and retailers, providing them with valuable insights. Sales prediction plays a crucial role in various industries, particularly in sectors such as retail, automotive leasing, real estate transactions, and other conventional businesses. OBJECTIVES: This paper focuses on developing a sales prediction model for Big Mart, a supermarket chain, using machine learning algorithms. The developed model aims to provide Big Mart with accurate sales forecasts, enabling better decision-making, improved profitability, and enhanced customer service. METHODS: The study utilises the CRISP-DM methodology and explores various machine learning algorithms, including Linear Regression, Decision Tree, Random Forest, XGBoost, Stacked Ensemble Model, and K-Nearest Neighbours (KNN). The dataset used for model development is sourced from Kaggle and includes information about products, stores, and sales. Pre-processing techniques are applied to handle missing data and feature engineering. RESULTS: The XGBoost Regression Model Tuned with RandomizedSearchCV outperforms the existing models with an RMSE of 1018.82 and an R² of 0.6181. CONCLUSION: This research contributes to the field of sales forecasting in the retail industry and provides insights for businesses looking to enhance their revenue prediction capabilities.

  • 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:
    2648

    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:
    1028

    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.

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

    Appears in:
    IoT 22(4): e1

    Authors:
    Kerem Kucuk, Sevval Solpan

    Downloads:
    570

    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.

  • 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:
    558

    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.

  • 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:
    499

    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.

  • 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:
    492

    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.    

  • 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:
    393

    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:
    327

    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.

  • Weed detection with Improved Yolov 7

    Appears in:
    IoT 23(3): e1

    Authors:
    Qiyuan Xue, Mingkang Peng, Fuzhong Li, Wuping Zhang, Jialiang Yuan, Peipu An

    Downloads:
    321

    Abstract:
    INTRODUCTION: An improved Yolo v7 model. OBJECTIVES: To solve the weed detection and  identification in complex field background. METHODS: The dataset was enhanced by online data enhancement, in which the feature extraction, feature fusion and feature point judgment of weed image were carried out by Yolov7 to predict the weed situation corresponding to the prior box. In the enhanced feature extraction part of Yolov7, CBAM, an attention mechanism combining channel and space, is introduced to improve the attention of the algorithm to weeds and strengthen the characteristics of weeds. RESULTS: The mean average precision (mAP ) of the improved algorithm reached 91.15%, which was 2.06% higher than that of the original Yolov7 algorithm. Compared with the current mainstream target detection algorithms Yolox, Yolov5l, Fster RCNN, Yolov4-tiny and Yolov3, the mAP value of the improved algorithm increased by 4.35, 4.51, 5.41, 19.77 and 20.65 percentage points. Weed species can be accurately identified when multiple weeds are adjacent. CONCLUSION: This paper provides a detection model based on Yolov7 for weed detection in the field, which has a good detection effect on weed detection, and lays a research foundation for intelligent weeding robot and spraying robot.

  • 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:
    313

    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
2023-11-27
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