Internet Technology Letters最新文献

{"title":"Space-to-tree: Architectural framework for real-time monitoring of pines in natural and historical park","authors":"Costanza Fiorentino,&nbsp;Mohamed A. E. AbdelRahman,&nbsp;Paola D'Antonio,&nbsp;Francesco Toscano,&nbsp;Maura Sannino,&nbsp;Nicodemo Abate,&nbsp;Domenico Conte,&nbsp;Rosa Lasaponara,&nbsp;Nicola Masini","doi":"10.1002/itl2.574","DOIUrl":"https://doi.org/10.1002/itl2.574","url":null,"abstract":"<p>The conservation and promotion of natural and cultural heritage, including landscapes, constitutes a subject of great economic and social importance. In recent times, there has been an increasing emphasis on the debate around strategies for developing an integrated approach to environmental and cultural heritage. The United Nations Educational, Scientific and Cultural Organization (UNESCO) has highlighted these challenges in its conventions, emphasizing the need for proactive measures to protect cultural heritage. The European Space Agency project “From space to Tree” (S23) aimed to develop an alert system for real-time monitoring of trees stability. The experimental site was the Archaeological Park of Colosseum in Rome. Inside the park there are countless pine trees of historical-cultural interest. The System and Service Architecture (SSA) describes the structure of the pilot system, detailing the high-level architecture and its constituent components. The monitoring system follows an integrated multi-scale approach and combines the health status of trees monitored by multi-temporal Sentinel-2 remote sensing images, the movements of the trees in response to wind stress, monitored by four inertial measurement units (IMUs) installed at different heights on each individual tree to detect its movement in response to wind stress, a weather station equipped with an ultrasonic anemometer and the in-field surveys and analyzes carried out by expert forestry agronomists. The acquired data is transmitted in real time to a dedicated server, by using the 5G network. Results show that data transmission system becomes more complex as the number of monitored trees increases, consequently the transmission system was designed taking this criticality into account. The data are displayed and analyzed in a dedicated Web-GIS platform. The experimentation is still ongoing, during the first experimental year, the analysis algorithm was trained in the first 6 months. During the test period, an alert is generated when changes are found in the behavior of the pines, based on remote sensing images and trees' response to wind stress analysis, compared to the training period.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/itl2.574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven implementation of workflow optimization in cloud computing for IoT applications","authors":"Md Khalid Jamal,&nbsp;Mohammad Faisal","doi":"10.1002/itl2.571","DOIUrl":"https://doi.org/10.1002/itl2.571","url":null,"abstract":"<p>The optimization of workflow scheduling in Internet of Things (IoT) environments presents significant challenges due to the dynamic and heterogeneous nature of these systems. Traditional techniques must often adapt to fluctuating network conditions and varying data loads. To address these limitations, we propose a novel approach that leverages Automated Machine Learning (AutoML) integrated with cloud computing to optimize workflow scheduling for IoT applications. Our solution automates machine learning model selection, training, and tuning, significantly enhancing computational efficiency and adaptability. Through extensive experimentation, we demonstrate that our AutoML-driven approach surpasses conventional algorithms across several key metrics, including accuracy, computational efficiency, adaptability to dynamic environments, and communication efficiency. Specifically, our method achieves a scheduling accuracy improvement of up to 25%, a reduced computational overhead by 30%, and a 40% enhancement in adaptability under dynamic conditions. Furthermore, the scalability of our solution is critical in cloud computing contexts, enabling efficient handling of large-scale IoT deployments by leveraging cloud resources for distributed processing. This scalability ensures that our approach can effectively manage increasing data volumes and device heterogeneity inherent in modern IoT systems.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing cluster head selection for energy efficiency in wireless sensor networks: A hybrid algorithm combining grey wolf and enhanced sunflower optimization","authors":"Indra Kumar Shah,&nbsp;Neha Singh Rathaur,&nbsp;Yogendra Singh Dohare,&nbsp;Tanmoy Maity","doi":"10.1002/itl2.567","DOIUrl":"https://doi.org/10.1002/itl2.567","url":null,"abstract":"<p>In this letter, we introduce a novel cluster head selection algorithm namely mixed grey wolf and improved sunflower optimization algorithm (MGWISFO). This algorithm leverages both energy requirements and inter-node distances to select cluster heads (CH). Within this algorithm, the Grey Wolf Optimizer facilitates exploration, offering a broader search, while the improved Sunflower Optimization focuses on exploitation, delivering a narrower search. This balance between exploration and exploitation leads to the identification of the optimal CH node, thereby enhancing network performance. To validate its effectiveness, the proposed algorithm is benchmarked against existing strategies such as particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), and sunflower optimization (SFO) across various performance parameters including throughput, the number of live and dead nodes, and residual energy. Simulation results unequivocally establish the unparalleled performance of our proposed algorithm, surpassing the capabilities of existing algorithms.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143186955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust phase extraction from complex signals/fringe patterns for optical communications in 5G and beyond through continuous wavelet ridge technique","authors":"Divya Haridas,&nbsp;Jyoti Singh,&nbsp;J. Roopa Jayasingh,&nbsp;D. Deepa,&nbsp;K. C. Ramya,&nbsp;Ankush D. Tharkar","doi":"10.1002/itl2.568","DOIUrl":"https://doi.org/10.1002/itl2.568","url":null,"abstract":"<p>The rapid progress in high-precision optical instruments necessitates sophisticated image processing techniques for extracting vital information from generated images. Most of these instruments output images comprising RGB pixels. Deciphering these pixel values into phase information poses a significant challenge, especially in the presence of background noise. This study focuses on utilizing Two-Dimensional Continuous Wavelet Transforms (2-D CWT) for analyzing fringe patterns with varying noise levels and fringe alignments, crucial for high-precision optical systems pivotal in enhancing the performance and reliability of optical communication systems in advanced 5G networks. The simulation results demonstrate that 2-D CWT efficiently extracts phase information from complex and highly noisy fringes while requiring less computational time. Furthermore, the algorithm effectively handles noise disturbances with a commendable degree of accuracy, ensuring robust performance in 5G-enabled optical systems critical for supporting ultra-high-speed data transmission and low-latency communication requirements. This research contributes to optimizing image analysis techniques for 5G optical systems, facilitating their integration into next-generation communication networks.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative resource allocation mechanism for optimizing 5G multi user-massive multiple input multiple output system","authors":"P. Leela Rani,&nbsp;N. Devi,&nbsp;AR. Guru Gokul","doi":"10.1002/itl2.569","DOIUrl":"10.1002/itl2.569","url":null,"abstract":"<p>5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)-massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi-objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Internet of Things security-based E parking framework for smart city application using Lora","authors":"Shuvendra Kumar Tripathy,&nbsp;Gopinath Palai","doi":"10.1002/itl2.566","DOIUrl":"10.1002/itl2.566","url":null,"abstract":"<p>Finding an accessible parking spot using 5G technology can be considered as time and fuel expenses. In this manner, it might make drivers disappointed in the parking zone. This will prompt awful traffic around the parking spot and may likewise prompt a mishap. That is the reason this task proposes a Smart Parking framework that utilizes cameras which will be associated with a Raspberry Pi and it will likewise have an Android application as an interface to help book or view accessible spaces. E Parking framework for security empowerment in 5G can be characterized as the utilization of trend setting innovations for the effective activity, checking, and the board of parking inside an urban versatility technique. This task will help tackle issues referenced by permitting clients to see and select accessible space in the parking, which will keep clients from driving around. You Only Look Once (YOLO) algorithm, Adaptive Background Learning and also pre-trained Mask-RCNN are used for finding the nearest free parking slot. Currently, Raspberry Pi will be utilized as the connection between the Cameras and the Server, by moving information gathered from the Raspberry Pi to an online server in order to process the information and empower the Android application to get outcome. In an end, this venture will help in decreasing the measure of time a driver needs to spend around the parking just to locate an accessible spot, lessening the measure of traffic, diminishing contamination, expanding the security using 5G technologies and furthermore better monetizing the parking spot. The proposed system detects vehicles in indoor as well as outdoor parking fields accurately.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Basketball detection and trajectory prediction using IoT for assisting physical training","authors":"Xianhui Liang","doi":"10.1002/itl2.565","DOIUrl":"https://doi.org/10.1002/itl2.565","url":null,"abstract":"<p>With the development of the Internet of Things (IoTs) and 5G technologies, more and more smart applications are emerging. This paper designs an IoTs-based college basketball teaching system which can automatically detect basketball and predict its trajectory for auxiliary teaching. The difficulties include low-latency video processing and a smart algorithm for automatic basketball detection and its trajectory prediction. For the former issue, the basketball videos are collected using a 5G camera and transmitted to the Jetson TX2 platform through a 5G network. For the latter issue, an end-to-end deep learning framework is proposed and deployed on the Jetson TX2 platform. First, a pre-trained YOLOv5 is used to obtain high-confidence candidate regions; then, the local dependencies are disclosed using a spatial graph convolutional layer; lastly, a multi-head self-attention (MSA) mechanism is used to improve the modeling of long-distance dependencies. The proposed system is evaluated on a self-built basketball dataset and the results show its effectiveness for basketball detection and trajectory prediction.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embedded system controlled switching system for reconfigurable antennas","authors":"Naresh Kumar,&nbsp;Pradeep Kumar,&nbsp;Manish Sharma","doi":"10.1002/itl2.561","DOIUrl":"https://doi.org/10.1002/itl2.561","url":null,"abstract":"<p>A multiband and reconfigurable antenna is the one of the main aspects in the communication system which has an extremely important role. An embedded based solution for the fast operation of a reconfigurable antenna to work in a specific operating frequency band is the solution of future technologies. Embedded based solution increases the speed by avoiding the dependence on manual control of the RF switching elements. In this paper, the proposed embedded system-controlled switching mechanism operate the reconfigurable antenna in any one of the operating bands by a single command to be send from a remote location. The embedded system receives the command and configure a set of PIN diodes as per the modes. The proposed solution consists an Arduino UNO board as motherboard, SIM900A as GSM module to receive the command from user, biasing circuit to control the operation of PIN diode, PIN diode to reconfigure the patch pattern and a user mobile for sending AT command.The proposed model is successful and tested on proteus software 8.0. The model is also deployed on the newly designed multiple-input-multiple-output antenna where the result shows the successful operation to achieve multiband frequency operation.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed edge analytics in edge-fog-cloud continuum","authors":"Satish Narayana Srirama","doi":"10.1002/itl2.562","DOIUrl":"https://doi.org/10.1002/itl2.562","url":null,"abstract":"<p>To address the increased latency, network load and compromised privacy issues associated with the Cloud-centric IoT applications, fog computing has emerged. Fog computing utilizes the proximal computational and storage devices, for sensor data analytics. The edge-fog-cloud continuum thus provides significant edge analytics capabilities for realizing interesting IoT applications. While edge analytics tasks are usually performed on a single node, distributed edge analytics proposes utilizing multiple nodes from the continuum, concurrently. This paper discusses and demonstrates distributed edge analytics from three different perspectives; serverless data pipelines (SDP), distributed computing and edge analytics, and federated learning, with our frameworks, MQTT based SDP, CANTO and FIDEL, respectively. The results produced in the paper, through different case studies, show the feasibility of performing distributed edge analytics following the three approaches, across the continuum.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"8 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An amalgamated correlation and regression based feature selection with ensemble learning approach for IoT network attack detection","authors":"Mir Shahnawaz Ahmad,&nbsp;Shahid Mehraj Shah","doi":"10.1002/itl2.564","DOIUrl":"https://doi.org/10.1002/itl2.564","url":null,"abstract":"<p>The advancements in the field of Information and Communication Technology (ICT) have led to the development of the Internet of Things (IoT), where ordinary things (e.g. smart meters, sensors etc.) can be connected and controlled over the internet. However, an IoT network is vulnerable to many malicious network attacks due to its inherent properties like limited computational abilities, heterogeneity, massive connectivity, etc. This paper presents a feature selection technique, which uses an amalgamation of LASSO regression and correlation based techniques, to find appropriate features for IoT network attack detection. The effectiveness of the proposed mechanism was evaluated on benchmark IoT network datasets using different machine learning techniques. The results revealed that the Gradient Boosting ensemble learning model achieved a maximum attack detection accuracy of 99.98%, and outperformed the other studied models.</p>","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":"7 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}