International Journal of Intelligent Networks最新文献

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.

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.

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-C-LSTM) model that combines the spatial pattern recognition capabilities of a Convolutional Neural Network with the sequential data prowess of LSTM networks. The PM-C-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-C-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.

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 J, the experiment's findings demonstrate that a proposed SOA-FACSO model achieves superior EED.

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.

Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained. However, conventional FL approaches often overlook two critical characteristics in the real-edge scenario: system heterogeneity and statistical heterogeneity. This oversight can detrimentally impact both the training efficiency and the model's accuracy. Specifically, it brings intolerable training delays and severe training accuracy degradation. To address these issues, this paper proposes a novel Quality-aware online Device Selection (QDS) algorithm. The QDS algorithm leverages a greedy selection method that guarantees the deadline restrictions and reflects upon their historical performance metrics, as indicated by their loss function values from preceding training rounds. This rigorous selection process ensures that the participating device set is optimally positioned to balance the dual objectives of training efficiency and model accuracy. Furthermore, we have developed a training loss-based device selection mechanism, aimed at prioritizing higher-quality devices for early submission of local updates prior to the designated deadline. Experimental findings demonstrate that the proposed QDS significantly enhances both the speed and accuracy of training when contrasted with baseline methods.

The utilization of radio frequency identification (RFID) technology has witnessed a substantial rise in recent years, and this upward trend is expected to continue in the coming years. It is often described as an industry-changing concept, which is why continuous improvements in the technique of object identification are still needed. These enhancements are related to security, speed, and reliability of communications. RFID is frequently used in areas where a large number of things must be identified. Therefore, some problems of tag collision when many tags are transmitting their data are presented by these systems. The research presented here thoroughly analyzes and evaluates existing tag reading approaches that offer low power consumption during identification. The outcome of this work is the proposal of energy-efficient anti-collision protocol, namely HT-EEAC. By assessing the energy consumed for HT-EEAC at checkpoints, we were able to increase throughput and reduce energy consumption in our protocol. The HT-EEAC approach for updating frame size aims to enhance the energy and throughput of the EPC C1 G2 UHF RFID standard. Performance comparisons demonstrate the advantages of the strategy we propose in terms of low energy consumption per identified tag, minimal collisions, and high throughput. As a result, this method can be effectively utilized in the field of RFID systems to significantly enhance their efficiency.