International Journal of Wireless and Microwave Technologies @ijwmt
Статьи журнала - International Journal of Wireless and Microwave Technologies
Все статьи: 622
Identifying Sentiment in Web Multi-topic Documents
Статья научная
Most of web documents coverage multiple topic. Identifying sentiment of multi-topic documents is a challenge task. In this paper, we proposed a new method to solve this problem. The method firstly reveals the latent topical facets in documents by Parametric Mixture Model. By focusing on modeling the generation process of a document with multiple topics, we can extract specific properties of documents with multiple topics. PMM models documents with multiple topics by mixing model parameters of each single topic. In order to analyze sentiment of each topic, conditional random fields techniques is used to identify sentiment. Empirical experiments on test datasets show that this approach is effective for extracting subtopics and revealing sentiments of each topic. Moreover, this method is quite general and can be applied to any kinds of text collections.
Бесплатно
Impact of Mobility on MANETs Routing Protocols Using Group Mobility Model
Статья научная
The MANET routing protocols should be tested under realistic conditions of the network including various parameters such as network size, representative data traffic models, realistic movements of the mobile users, etc. Many factors impinge the performance evaluation of MANETs routing protocols. Mobile nodes are communicated with each other with the help of routing protocols. Unpredictable movement of a mobile node affect the routing information which directly interrupt the subsist communication. A mobility model is used to depict the realistic movements of mobile nodes in the designed scenario. In this study the group mobility model has been used to deploy the mobility effect in the scenario. The goal of this paper is to investigate the impact of group mobility on performance of routing protocols under group mobility model using QualNet simulator. In the paper it is illustrate that how the performance results of an ad hoc network protocol drastically change with the increasing node density.The various scenarios investigated with varying density of nodes in groups. Performance analysis is carried out on the basis of performance metrics under group mobility model. The outcome of this work shows that mobility has a detrimental impact on the performance of routing protocols. From the simulation results, it is shown that the DSR protocol clearly outperform all other routing protocols with increasing node density under group mobility model.
Бесплатно
Impact of Reducing Multicollinearity in a Dataset on Artificial Intelligence Algorithms
Статья научная
It is known that multicollinearity not only leads to the generation of redundant data as a result of data repetition, but also affects the stability of linear models of artificial intelligence and the reliability of results. The negative effects of multicollinearity can be seen especially clearly in the development of mathematical models of artificial intelligence algorithms. That is, the coefficients will be unstable in a mathematical model developed on the basis of a data set with multicollinearity. As a result of it, misconceptions arise in scientific conclusions drawn based on the coefficients. This article first discusses multicollinearity and its negative consequences in detail. In addition to, methods for determining multicollinearity in a data set based on the correlation coefficient, the variance inflation coefficient, and the condition index are discussed in detail. Moreover, this research paper analyzes the methods of eliminating multicollinearity by removing, combining features, and Principal Component Analysis. At the same time, the research will investigate the impact of multicollinearity on machine learning models such as LogisticRegression, LinearRegression, LinearSVC, and XGBClassifier using a multicollinearity dataset. The results of the study showed that eliminating multicollinearity leads to an increase in the accuracy of all considered artificial intelligence models. In particular, the ROC value increased by 0.102 in the Logistic Regression model, by 0.129 in the Ridge Classifier, and by 0.121 in the Linear SVC. Although the smallest difference value of 0.094 was achieved in the XGBoost model, the accuracy was higher than that of the other models. After the experimental results, the article presents conclusions and recommendations based on the results obtained.
Бесплатно
Impact of Wall Coating on the Behavior of Indoor OWC under Diffuse Topology
Статья научная
Optical wireless communication (OWC) is an innovative technology that is gaining more attention as the demand for capacity continues to increase. It is one of the most promising alternative technologies for indoor and outdoor applications. In this paper, the effect of the inner wall coating material, color and roughness on the performance of OWC system implementing single-diffuse topology is studied. A new procedure is proposed to generate a rough surface model with predetermined statistical properties to simulate the matte painting material on the internal walls of a room. Additionally, a new technique that applies the geometrical theory of diffraction (GTD) in conjunction with a ray tracing (RT) scenario is developed to evaluate the scattered optical beam due to a primary ray incident on a Lambertian surface. The performance of the single-diffuse OWC strategy is assessed by investigating some important performance measurements such as signal strength and the bit error rate (BER) due to unavoidable ambient light which is modeled as an additive white Gaussian noise. It is shown that the surface roughness of the matte painting on the Lambertian diffuse surface has a major effect on the indoor OWC system performance.
Бесплатно
Implementation of Internet of Things Based Data Security Using Hybrid Cryptography
Статья научная
Internet of Things (IoT) is the current trends in tracking the variation of process variables in plant operations. The security threats and security issues continues to rise due to the wide usage of internet. The hybrid cryptography is proposed that involves symmetric AES, asymmetric RSA and hash functions all together enhance the security. The key length of this proposed symmetric AES encryption is 128-bit, RSA public key encryption is 1024-bit and 128-bit message digest is generated from the hash algorithm. It offers low latency in executing the proposed encoding and decoding algorithm. It is developed and verified in real-time environment using embedded system with internet of things. It assures data security and allows only authorized parties to monitor the plant parameters through the wireless networks. It preserves the intruders from gathering and modifying the sensitive plant information. It is suitable for protecting the plant parameters over the wide range of industrial applications.
Бесплатно
Implementation of a Locator-Based Route Switching Scheme for Improved Routing in Proxy Mobile IPv6
Статья научная
Proxy Mobile IPv6 (PMIPv6) is a promising IP mobility protocols that is being deployed in emerging wireless technologies. This however has a non-optimal packet route as a result of the triangular routing problem. This creates a bottle neck at the Local Mobility Anchor (LMA) thereby increasing packet delays. This paper presents the implementation of a locator-based route switching scheme on OPNET Modeler. The Mobility Access Gateway (MAG) and the LMA were enhanced by making them intelligent. This enables them to be able to check the position of the Corresponding Node (CN) with respect to the Mobile Node (MN) and also determine the available bandwidth on each link. From the checks made, a three-stage decision process is used to switch routing to the most optimal route that guarantees the best QoS. Node Models were developed for the MAG and LMA, network models were deployed and simulation tests were carried out. The results show that the developed scheme switched packets to a more optimal route according to the designed algorithm. The impact of this switching on differences between transmitted throughput at MN and the received throughput at CN was also evaluated. The receiver activity result shows a reduction in the bottleneck at the LMA-MAG link. The end-to-end delay results show over 50 milliseconds drop in packet delay as a result of the switching to a more optimal route. This shows that the packet delays result from the congestion at the LMA-MAG interface due to suboptimal routing.
Бесплатно
Статья научная
This paper presents a Causal Observability Framework designed to enhance the reliability and performance of cloud-native distributed systems through structured integration with the DevOps pipeline. The framework unifies three interdependent components: real-time telemetry collection, dual-domain causal tracing, and probabilistic causal inference. The causal tracing layer combines a time-domain vector autoregressive Granger causality model with a discrete Fourier transform frequency-domain extension. The causal inference layer employs Bayesian network propagation, updated online via the Expectation-Maximisation algorithm, to compute posterior downstream failure probabilities from upstream anomaly observations. Validation was conducted through a controlled, three-replicate experimental study on a seven-service AI-powered recommendation application deployed across a dual-provider six-node Kubernetes cluster (AWS EKS and GCP GKE) under three traffic profiles ranging from 50 to 500 requests per second. Against a conventional threshold-based monitoring baseline, the proposed framework achieved: a 35% reduction in incident response time (70 minutes to 45 minutes), a 40% reduction in mean time to recovery (50 minutes to 30 minutes), a 1.5 percentage-point improvement in system availability (98.0% to 99.5%), a 61% reduction in false-positive alert rate (18% to 7%), and a 63% improvement in root-cause localisation accuracy (54% to 88%). All five improvements were statistically significant at p < 0.05 via paired t-test. A quantified nine-minute early-warning lead time over conventional detection was demonstrated in the fault-injection scenario. Seven formal equations underpin the methodology, spanning Granger vector autoregression, F-test inference, AIC-based lag selection, normalised causality scoring, frequency-domain spectral causality, Bayesian posterior propagation, and expected detection lead time.
Бесплатно
Implementing Video OCR along with SWT Technique for Video indexing and Analysis
Статья научная
The main purpose of this paper is to expand the usage of OCR (Optical character recognition) as this is only implemented over images and to extend this Video OCR is introduced in a way to help to retrieve the information from the video without playing the video. Video OCR is executed with the assistance of OpenCv2 module and PyTesseract [7] at the side of SWT approach which all pretty collectively make an ideal aggregate to offer an appropriate content from the video (i.e., Lecture video or any kind of video which has slides or text on the background of the video) [2,4].This technique is performed in a well-designed along with easy steps to provide us an correct end result of the facts from the video into textual files. In addition to this we also added Speech Recognition module within the project to support the video along with the text file. This speech delivered by the faculty (i.e., instructor/educator/teacher), or an educator will be also resulted in a text file.
Бесплатно
Improved Hybrid Architecture to Mitigate Free Riding in P2p Network
Статья научная
Peer-to-peer (P2P) networks rely heavily on resources shared by peers in the network as a result of this mitigating free riding in the network is very crucial in a P2P system. In this work, we introduced a dynamic grace period allocation and a content scanning mechanism to a hybrid P2P architecture to mitigate free riding and prevent peers from uploading repeated and fake files within the network. The method introduced was simulated using Python programming language with peers selected at random to upload and download files representing a typical scenario of a P2P network. From the range of 0-500 and 600-2000, twenty different peers were selected at random the first scenario represents few peers and the second scenario represents many peers for analysis and experimentation purposes and also for the different percentages of free riders used for the experiment, this was chosen at random. Finally, we compared our method with a credit-based approach (CBA) that uses a common grace period assigned to peers in the network. Then, for the performance evaluation metric, we used the total uploads and downloads, contributing peers uploads and downloads, free rider peers uploads and downloads, and repeated and fake files detected gotten from the simulation result to evaluate the model and analyze the outcome of the experiments. Results from the simulation revealed that the Dynamic grace period approach (DGA) is 25-70% more effective than CBA in maintaining contributing peer activity and preventing the spread of repeated and fake files,while also achieving lower latency, higher throughput, and better quality of service (QoS) across diverse network conditions, particularly in high free-rider environments.
Бесплатно
Improved Route Discovery Scheme under Blackhole Attack in MANET
Статья научная
A Mobile Ad Hoc Network (MANET) consists of numerous wireless mobile devices. It is a self-organizing network and does not require any pre-established infrastructure. Communication between devices sets up without any dedicated centralized server. A malicious node takes advantage of this vulnerability and attempts to integrate into the network in order to lower its overall performance. In MANET, one of the most dangerous types of attacks is the blackhole node assault. In order to join the route, a node with blackhole assault wrongly sends route information to the source node during the route discovery process and degrades the network performance. In order to address this problem, a novel Blackhole Detection Algorithm (BHDA) has been proposed in this work. To determine the existence of blackhole nodes, the protocol takes into account various factors including number of route request packets (RREQ) received, number of RREQ packets forwarded, and number of route reply packets (RREP) transmitted by nodes throughout the route discovery process. Apart from this, each node maintains a local neighbourhood information and for that all neighbourhood node has to pass the check before becoming a neighbour. The simulation results prove that the proposed technique BHDA shows drastic improvement in network performance under blackhole attack.
Бесплатно
Статья научная
The design of a double layer stacked microstrip loop shaped patch antenna for multiband operation has been proposed. The design has been evolved following the iterations of a rectangular patch and a single layer loop patch structure. The material considered for the substrate of both the layers is 1.6 mm thick FR4 epoxy and the feeding technique used for the bottom patch is coaxial/ probe feed. The radiations from the bottom layer patch have been electromagnetically coupled to the upper layer patch. The main results including the reflection coefficient, bandwidth, radiation pattern, gain, directivity and VSWR for single frequency operation in each case have been discussed separately and finally compared. The comparison shows that the proposed stacked structure is clearly advantageous over the conventional rectangular patch and the single layer designed prototype in terms of the standard parameters that have been obtained. The three stage designs are useful to serve the X-band aviation applications including radio location and fixed mobile radio location.
Бесплатно
Improvement of ZigBee Using by Thread and Backpressure Algorithm
Статья научная
In wireless sensor networks, two approaches of tree and mesh routing are introduced to determine the path of packets during the transition process. Tree routing is a simple routing protocol with low overhead that in this protocol father-child bonds for packet transmission from the source to the destination is used. The biggest problem of routing is the increase of the number of mutations in comparison with other routing protocols. In order to improve this problem, protocols have been introduced in recent years to determine a shortcut path on the basis of the tree routing. This study is an attempt to analyse and evaluate the existing routing algorithms, identify and overcome their disadvantages, also in some other protocols, only reducing the number of mutations has been discussed. However, to achieve this goal leads to increased energy consumption and thus reducing the lifetime of the network; reducing the number of mutations is an important parameter and can reduce delays in the network, however, it should be noted the energy consumption in ZigBee networks is a very important debate. Besides that, this study will try—in addition to reducing the average number of mutations—to reduce the traffic load near the root node in the proposed algorithms. As a result, on the one hand, the application of this algorithm in ZigBee networks reduces delays and on the other hand, will also lead to balancing of load and energy in the network. Using this algorithm, the scope and lifetime of the proposed protocol-based networks can be increased.
Бесплатно
Статья научная
Wireless sensor network has many applications and very active research area. The coverage span of this network is very important parameter where wide coverage area is a challenge. This paper proposes an architecture for large-scale wireless sensor network (LSWSN) based on satellites and the High-Altitude Platforms (HAP) where the sensor nodes are located on the ground and a wide coverage sink station may be in the form of a satellite or a network of HAPs. A scenario is described for multilayer LSWSN and a study for the system requirements has been established showing the number of Satellites, HAPs and coverage per each sink according to the elevation angle requirements. The Satellite-HAP-Sensor multilayer LSWSN architecture has the feasibility for effective energy and earth coverage and is optimum for covering largely sparse regions.
Бесплатно
Integrated Guard Channel Synthesis in AESA based Airborne Surveillance Radar
Статья научная
The guard channel is very effective in eliminating side-lobe returns, both targets and discrete clutter, in airborne radar's. The ideal design criterion for the guard channel is that all the side lobes of the main antenna are covered by the guard pattern. In this paper a novel design approach of generating the guard using the elements of the main Active Electronically Scanned Array (AESA) antenna, thereby eliminating the need for a separate antenna, is presented. The ground clutter has an angle dependent Doppler and clutter discrete that leak in through the side lobes, especially the inter-cardinal elevation side lobes looking at the near range, needs to be eliminated. Towards this the design of the weighting coefficients for the guard to cover all the side lobes of the main array is discussed. Furthermore a digital threshold scheme is proposed to improve the effectiveness of guard channel in eliminating side-lobe returns. The detection loss and blanking probability are characterized for the design. The measurement results confirm that the design objectives are met.
Бесплатно
Статья научная
IoT networks face persistent security challenges due to limited compute, heterogeneous hardware, and weak threat-detection coverage. Classical machine-learning methods struggle with high-dimensional traffic and novel attack patterns. This paper proposes a hybrid framework combining Fractional Generalized Laguerre (FrGL) moment-based feature extraction with a Residual Network augmented by Squeeze-and-Excitation attention (ResNet-SE). FrGL moments yield compact, noise-resistant descriptors via simple recurrence relations, while ResNet-SE mitigates degradation in deep networks through identity shortcuts and adaptively recalibrates channels to highlight attack-relevant features. On the Bot-IoT and Leopard Mobile IoT benchmarks the method reaches 99.78 % accuracy and 99.37 % F1, exceeding KNN (84.7 %), MLR (87.5 %) and a baseline CNN (99.3 %); cross-dataset tests on UNSW-NB15 and IoT-Bot give 96.34 % and 97.12 % accuracy. The framework additionally delivers per-sample inference latency on server- and edge-class hardware (3.9 ms on an NVIDIA V100 and 27.4 ms on a Raspberry Pi 4B with a Coral USB accelerator), an energy cost of 0.42 J per inference on the edge platform, a sensitivity analysis over learning rate, batch size, fractional order λ and reduction ratio r, and an adversarial-robustness evaluation under FGSM and PGD attacks, supporting real-time deployment on resource-constrained IoT gateways.
Бесплатно
Integrating Quantum Computing with Cloud Systems: Opportunities, Challenges, and Future Prospects
Статья научная
Cloud computing can be revolutionized by quantum computing which will offer the world more computational power than has ever been seen to solve complex issues. Quantum computing coupled with cloud computing enables the remote access to quantum resources, thus greatly minimizing the cost, technical, and operational difficulties of having quantum hardware owned and maintained in the field. The integration makes large-scale data processing, cryptography, and optimization tasks as well as new applications in artificial intelligence efficient in terms of their computation. This work is a review of the existing approaches, system, and systems to quantum cloud computing, the main algorithms, software applications, implementation plans, and real-life examples. We find that quantum cloud computing provides significant enhancements in computational speed and parallelism, scalability, as well as provides the capability to process data securely and to execute quantum circuits remotely. However, there are still a few obstacles such as stability of qubits, error correction, noise reduction, and effective resource utilization, which restrict the practical use of quantum cloud services. The findings indicate that, irrespective of these challenges, quantum computing with the use of cloud computing platforms offers meaningful potentials to scientific discovery, business, and an AI-based innovation. The paper wraps up by noting that further research should be done to enhance the reliability of quantum hardware, optimize quantum algorithms, and design quantum cloud computing security systems, enabling quantum cloud computing to be adopted more broadly as a more transformative model of computation and ensuring that quantum cloud computing can grow sustainably.
Бесплатно
Статья научная
The new and emerging challenges posed by the convergence of cyber threats and socio-political tensions have risen as one of the core formidable threats to the present global security landscape. This paper proposes a hybrid predictive model intended to act against these real-world multidimensional attack vectors. The model integrates cyber threat hunting techniques with socio-political risk assessment methodologies to comprehensively forecast consequent cybersecurity threats to social unrest scenarios. Cyber threat data is collected from sources such as the Offensive Defensive-Intrusion Detection System (OD-IDS2022) and the Aegean Wi-Fi Intrusion Dataset (AWID3), and social terror attack information is gathered from the Global Database of Events, Language, and Tone (GDLET) Project and Armed Conflict Location & Event Data (ACLED) to comprise the bidirectional dataset for the model that contains views from both cyber and socio-political risk landscapes. The model adopts a holistic, robust predictive capability through k-fold cross-validation and feature importance evaluation implementation techniques. This multidisciplinary approach offers a synoptic understanding of emerging and future security threats and enables the execution of proactive measures to secure national and transnational borders.
Бесплатно
Intelligent Load Balancing Framework for Distributed Big Data Processing System
Статья научная
This paper proposes an intelligent load balancing framework for distributed big data processing systems that integrates machine learning techniques with adaptive weight-based decision mechanisms. The study addresses limitations of traditional static load balancing methods, which do not account for dynamic workload variations and heterogeneous request characteristics, leading to inefficient resource utilization and bottlenecks in multi-node environments. The proposed approach combines an online learning model for real-time estimation of request complexity with multi-parameter evaluation of node states, including CPU utilization, memory consumption, queue length, response latency, and cache efficiency. A dynamic weighting strategy is used to construct an integrated load indicator for adaptive request distribution across nodes. The framework is deployed within a multi-layer distributed architecture consisting of clustered application servers, distributed databases, caching subsystems, and monitoring components, ensuring scalable and fault-tolerant processing. For evaluation, a three-node simulation environment was used with 10,000 heterogeneous requests, followed by extended testing on semi-realistic workload traces derived from web traffic patterns and database query logs. The dataset included over 1.2 million requests, capturing bursty arrivals, skewed distributions, and heterogeneous complexity. Experimental results show that the proposed method improves load distribution uniformity to 6%, reduces average response time to 210 ms, and increases throughput up to 13,800 requests per second. Statistical validation using confidence intervals and hypothesis testing confirms a 47% (±3.2% at 95% confidence level) reduction in mean response time and throughput improvement up to 14,200 requests per second under realistic workloads.
Бесплатно
Intelligent and Distributed Localization of Nodes in Wireless Sensor Networks
Статья научная
In wireless sensor networks, the issue of nodes localization has taken a wide area of research. Most applications need to know the position of sensor nodes for reasons of optimal and fast data routing. In this paper, a new distributed localization algorithm based on Self Organizing Maps (SOMs) is proposed to determine the location of a node in a wireless sensor network. The proposed algorithm is classified as a range-free algorithm which uses only the connectivity information between nodes without the need to measure the time of arrival or signal strength as range-based algorithms require. It utilizes the neighborhood information and the well-known anchors' positions to calculate the estimated locations of nodes. Our algorithm is made up of two main stages. The initial estimated locations of nodes are calculated in the initialization stage, and fed to the learning stage in which a SOM is used to calculate the final estimated locations of nodes. By using the neighborhood information at the first stage, the algorithm has significantly reduced the SOM learning time and the number of iterations to converge. On the other hand, starting with real data rather than random data maximized the accuracy of the resulted locations. Furthermore, the distributed implementation of the algorithm highly alleviated the pressure on the wireless nodes which are characterized with low power and limited capabilities. The proposed algorithm has been implemented using MATLAB software and experimented by deploying different number of nodes in a specific area with different communication radio ranges. Extensive simulations evidently verified the performance of the algorithm and achieved a very good accuracy. Moreover, the algorithm proved its effectiveness with a lower average error and lower number of iterations compared to other related algorithms.
Бесплатно
Статья научная
In this paper, we present atmospheric effect on 5th Generation (5G) millimeter wave (MMWave) communication system. Atmospheric effects for Delhi (India) based 5G communication system is calculated as per Delhi atmospheric conditions. Atmospheric impairments are major cause of degrading mmWave signal power while mmWave propagation in wireless channel. Due to Atmospheric impairments attenuation takes place and major impairments are like water vapour, oxygen, rain and fog for Delhi (India). 5G mmWave attenuation calculations are performed for the mmWave band frequencies 28 GHz, 37 GHz and 39 GHz. In this paper intelligent adaptive transmitter based on trend of the atmospheric conditions tunes to machine learning (ML) based derivation of channel capacity. The ML based transmitter is a supervised ML device and it has provision of self teaching learning machine based on data. Results are graphed for the mentioned frequencies and also intelligently software defined (SD) Shannon channel capacity calculated for Delhi (India) based 5G mmWave communication system under different atmospheric conditions.
Бесплатно
