International Journal of Intelligent Networks最新文献

{"title":"A hybrid deep reinforcement learning and genetic algorithm approach for optimized resource allocation and reconfigurable intelligent surface deployment in 6G holographic communication","authors":"Brice Jibia ,&nbsp;Richard Mbondjo Essomba ,&nbsp;Steyve Nyatte ,&nbsp;Jacques Matanga ,&nbsp;Jean-Francois Essiben ,&nbsp;Severin Nguiya","doi":"10.1016/j.ijin.2025.11.001","DOIUrl":"10.1016/j.ijin.2025.11.001","url":null,"abstract":"<div><div>The advent of 6G holographic communication demands extremely high data rates and ultra-low latency, creating unprecedented challenges for resource allocation and Reconfigurable Intelligent Surface (RIS) configuration. To address these requirements, we propose a novel hybrid framework that integrates deep neural networks (DNN), genetic algorithms (GA), and deep reinforcement learning (DRL) to jointly optimize resource allocation and RIS deployment. The DNN first predicts future channel states and holographic traffic requirements, enabling the GA to perform proactive multi-objective optimization. The DRL agent then refines these solutions in real time, adapting to dynamic channel and traffic variations. Simulation results demonstrate the superiority of the proposed method over benchmark approaches. The hybrid DNN-GA-DRL achieves an average throughput of 6.55 Gbps, a latency of 0.1 ms, energy efficiency of 9.5 × 10⁸ bits/Joule, and spectral efficiency of 6.0 bps/Hz, while maintaining near-perfect QoS satisfaction (99.8 % throughput, 99.9 % latency). Compared to state-of-the-art GA, DRL, and hybrid baselines, our framework consistently delivers higher efficiency, lower delay, and superior adaptability. These findings confirm the effectiveness of combining predictive intelligence, evolutionary search, and real-time reinforcement learning for enabling seamless, immersive holographic communications in future 6G networks.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"6 ","pages":"Pages 265-282"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579024","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":"Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems","authors":"Lei Gong, Yanhui Chen","doi":"10.1016/j.ijin.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.002","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"24 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139882379","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":"Personal internet of things networks: An overview of 3GPP architecture, applications, key technologies, and future trends","authors":"Fariha Eusufzai, Aldrin Nippon Bobby, Farzana Shabnam, S. Sabuj","doi":"10.1016/j.ijin.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.001","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139878827","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":"Research on secure Official Document Management and intelligent Information Retrieval System based on recommendation algorithm","authors":"Liang Xing","doi":"10.1016/j.ijin.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.003","url":null,"abstract":"","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"41 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884638","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":"Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique","authors":"M.G. Sumithra ,&nbsp;M. Suriya","doi":"10.1016/j.ijin.2024.05.003","DOIUrl":"10.1016/j.ijin.2024.05.003","url":null,"abstract":"<div><p>Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 286-292"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000228/pdfft?md5=cdc0b0f67bdd877ac91a21ff75bc3bee&pid=1-s2.0-S2666603024000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042987","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-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems","authors":"Lei Gong ,&nbsp;Yanhui Chen","doi":"10.1016/j.ijin.2024.02.002","DOIUrl":"10.1016/j.ijin.2024.02.002","url":null,"abstract":"<div><p>Wind power (WP) represents a Renewable Energy Source (RES) that has noticed substantial development as people continuously search for green energy sources. Utilizing predominantly Predictive Maintenance (PM) of Wind Turbines (WT), this research analyzes the potential benefits that could be generated by Wind Energy (WE) through the use of the Internet of Things (IoT) and Wireless Sensor Networks (WSN). This research recommends an Internet of Things-WSN model for PM comprised of three distinct phases: the primary phase is the collection of data via sensors, the second phase is the transmission of that data through a connection to the Internet, and the final phase is the implementation of data analytics on that data in the context of cloud computing. For PM analytics, this work introduces a Predictive Maintenance Convolutional Long Short-Term Memory (PM-<em>C</em>-LSTM) model that combines the spatial pattern recognition capabilities of a Convolutional Neural Network with the sequential data prowess of LSTM networks. The PM-<em>C</em>-LSTM model combines CNN for recognizing spatial patterns and LSTM networks for analyzing sequential data in a way that doesn't affect the accuracy of WT-PM. A Failure Sample Generator model is also fused into the study to measure soft failure and hard failure factors and improve the predictive accuracy of the Machine Learning (ML) model. Data became available over 16 months while the model was applied to a Wind Farm (WF) positioned on the Qinghai-Tibet Plateau. It has been demonstrated that the PM-<em>C</em>-LSTM model possesses enhanced PM capabilities by comparing its efficiency to other standard models using a selection of performance metrics. The result of the test indicates that there is a probability that the hybrid IoT and ML will improve PM methods in WT, which will subsequently help improve the effectiveness and sustainability of WE generation.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 133-144"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000083/pdfft?md5=b7b5ac54c0c8268a1260349ccf20f980&pid=1-s2.0-S2666603024000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139822786","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":"Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models","authors":"Yi Yang","doi":"10.1016/j.ijin.2024.05.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.001","url":null,"abstract":"<div><p>Through smart cities, Intelligent Transportation Systems (ITS), the agricultural sector, and wearable devices, the Internet of Things (IoT) has revolutionized several human interests. Through the development of new cluster tasks, the Decision-Making System (DMS) of Cluster Heads (CHs), and improving the accuracy of traffic prediction and reliability of transportation, the present study intends to improve the energy depletion of IoT devices. The paper explores the subject of data flow optimization using Fuzzy Assisted Cuckoo Search Optimization (FACSO), traffic flow using Gaussian Process Regression (GPR), and CH prediction using the Stochastic Optimization Algorithm (SOA). Optimizing network lifetime while minimizing Energy Consumption (EC) is feasible through the practical application of the SOA, GPR, and FACSO models. Increasing End-to-End Delay (EED), Network Throughput (NT), and energy efficiency can be rendered feasible through a real-time DMS regarding routing employing a novel approach referred to as FACSO. This approach has enhanced the efficacy and reliability of Wireless Sensor Networks (WSN). With up to 500 nodes and an EC of 0.3451 <em>J</em>, the experiment's findings demonstrate that a proposed SOA-FACSO model achieves superior EED.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 204-211"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000204/pdfft?md5=c394d35467f0128cd144f6b6466e3280&pid=1-s2.0-S2666603024000204-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946946","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":"Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain","authors":"Megha Kuliha,&nbsp;Sunita Verma","doi":"10.1016/j.ijin.2024.03.001","DOIUrl":"10.1016/j.ijin.2024.03.001","url":null,"abstract":"<div><p>Electronic Health Records (EHRs) have become an increasingly significant source of information for healthcare professionals and researchers. Two technical challenges are addressed: motivating federated learning members to contribute their time and effort, and ensuring accurate aggregation of the global model by the centralized federated learning server. To overcome these issues and establish a decentralized solution, the integration of blockchain and federated learning proves effective, offering enhanced security and privacy for smart healthcare. The proposed approach includes a gamified element to incentivize and recognize contributions from federated learning members. This research work offers a solution involving resource management within the Internet of Medical Things (IoMT) using a newly proposed trust decentralized loop federated learning consensus blockchain. The obtained raw data is pre-processed by using handling missing values and adaptive min-max normalization. The appropriate features are selected with the aid of hybrid weighted-leader exponential distribution optimization algorithm. Because, data with multiple features exhibits varying levels of variation across each feature. The selected features are then forwarded to the training phase through the proposed pyramid squeeze attention generative adversarial networks to classify the EHR as positive and negative. The proposed classification model demonstrates high flexibility and scalability, making it applicable to a wide range of network architectures for various computer vision tasks. The introduced model provides better outcomes in terms of 98.5% in the training accuracy and 99% in the validation accuracy over Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which is more efficient than the other traditional methods.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 161-174"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000162/pdfft?md5=30ce7cc6f43f1c29923858b571a1b08f&pid=1-s2.0-S2666603024000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140403754","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":"AOF: An adaptive algorithm for enhancing RPL objective function in smart agricultural IoT networks","authors":"Abubakar Wakili,&nbsp;Sara Bakkali","doi":"10.1016/j.ijin.2024.09.001","DOIUrl":"10.1016/j.ijin.2024.09.001","url":null,"abstract":"<div><p>Within the Internet of Things (IoT) ecosystem, the Routing Protocol for Low-Power and Lossy Networks (RPL) serves as a foundational element for network communication. The protocol's effectiveness depends on its Objective Function (OF), which orchestrates route selection based on predefined criteria. However, traditional OFs often struggle to adapt to the dynamic nature of IoT environments. This paper presents the Adaptive Objective Function (AOF), an innovative algorithm designed to dynamically adjust the OF in real-time, responding to fluctuating network conditions and application requirements. AOF comprises: a Network Monitor, an OF Selector, an OF Switcher, and an Event Handler, all working in concert to enhance network performance, reliability, and energy efficiency. Through simulations, AOF has demonstrated superior performance over legacy OFs, achieving a 10 %–20 % reduction in End-to-End Delay (EED), a 1 %–2 % increase in Packet Delivery Ratio (PDR), a 10 %–20 % improvement in Network Lifetime (NLT), and a substantial 50 %–80 % decrease in Control Overhead (COH). The paper also presents a smart agriculture case study that illustrates AOF's practical application in optimizing sensor network data routing—a testament to its versatility and practicality. Future endeavours will concentrate on further refining AOF and broadening its application across various IoT domains.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 325-339"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000320/pdfft?md5=bf0e841f7517d2e4a59787401fa56ed6&pid=1-s2.0-S2666603024000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238819","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":"Blockchain-based electronic voting systems: A case study in Morocco","authors":"Tarik Chafiq ,&nbsp;Rida Azmi ,&nbsp;Ouadoud Mohammed","doi":"10.1016/j.ijin.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.01.004","url":null,"abstract":"<div><p>This research examines the feasibility of implementing blockchain-based electronic voting systems in Morocco to enhance electoral transparency and integrity. The study employs a methodology that combines Distributed Permission Ledger Technology (DPLT) and the Solana blockchain, resulting in a multilayered system. The main findings highlight the effectiveness of blockchain technology in mitigating electoral fraud and manipulation when implemented with precision, underscoring the importance of meticulous design and execution. These findings contribute significantly to discussions surrounding the modernization of electoral processes in the digital age and support the hypothesis that blockchain can address vulnerabilities in traditional voting methods. Moreover, the study marks a significant step toward modernizing elections, preserving democratic principles, and reinforcing the role of technology in addressing persistent electoral challenges, ultimately enhancing accessibility, security, and transparency in elections and strengthening democracy in the digital era.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 38-48"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000046/pdfft?md5=d2bed8b75db2d74cedaa1fab9e937566&pid=1-s2.0-S2666603024000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709303","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}