Similar Journals
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Wireless Personal Communications
Journal Prestige (SJR): 0.26  Citation Impact (citeScore): 1 Number of Followers: 6  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1572-834X - ISSN (Online) 0929-6212 Published by Springer-Verlag  [2468 journals]
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- An Approach Using in Communication Network Apply in Healthcare System
Based on the Deep Learning Autoencoder Classification Optimization
Metaheuristic Method-
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Abstract: Abstract Parkinson’s disease is a neurodegenerative disorder and affects the nerve cells that produce dopamine in the brain. In this paper, we investigated comparative studies on the different scenarios such as AutoEncoder and Ant Colony Optimization feature selection algorithms for the effective features in diagnosis of Parkinson’s disease. These algorithms are implemented to the voice dataset obtained from online repository. Then selected features are presented to the Decision tree, SVM, K-NN, Ensemble, Naive Bayes and Discriminant classifiers for each of the binary classification problems. The proposed methods are evaluated with the sensitivity, specificity, precision, recall and accuracy criteria. The proposed systems are trained and tested with these classifiers separately to carry out a comparative study and to analyse the success of feature selection methods in discriminating healthy people and PD patients. In Parkinson’s data there are 24 features that obtained from the signal voices. Some of the features in training of the classifier have problems and these problems reduce the accuracy of the system. It is found that for K-NN and Ensemble classification methods both ACO and Autoencoder have the same and the best training performance. Testing results show that accuracy rate of ACO is higher than Autoencoder method.
PubDate: 2023-11-25
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- Ergodic Secrecy Capacity of Cooperative NOMA System with Untrusted User
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Abstract: Abstract Non-orthogonal multiple access (NOMA) technique can potentially increase spectral efficiency and improve the network capacity of wireless networks. Ergodic secrecy capacity (ESC) is an important parameter for evaluating the security of wireless systems. In this paper, we investigate the secrecy performance of a cooperative NOMA system with an untrusted user using a half-duplex amplify-and-forward relay in terms of ESC over Rayleigh fading environments. A satellite source uses the NOMA technique to communicate with the near and far users, which is assisted by a relay for downlink communication, and an untrusted user, i.e., an eavesdropper, also interacts with this relay. We derive the lower bound ESC of the proposed system and analyze its performance in terms of power allocation coefficient, variance between relay and user, variance between relay of the eavesdropper, and average signal-to-noise ratio of the eavesdropper link. Simulation results demonstrate that the cooperative NOMA system outperforms the cooperative orthogonal multiple access system in terms of ESC. Finally, Monte Carlo simulations are used to validate the analytical results.
PubDate: 2023-11-24
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- Performance Analysis of Spectral Amplitude Coding Methods in Fiber Optical
Communication System-
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Abstract: Abstract This study analyzes optical code division multiplexed access system coding with two different types of spectral amplitude coding (SAC): Double Weight (DW) codes and Multiple Diagonal (MD) codes. These two encoding types differ in code structure and channel bandwidth length (the number of wavelengths representing the channel's bandwidth), as well as Q-Factor, BER, and MAI values. This study analyzes systems with different number of users over single-mode optical fiber and presents a comparison of both. DW codes have a shorter length of code than MD codes, and although this technique can generate unique code words for each user, it will break the system using MD encoding. This is another reason to increase bandwidth for MD codes.
PubDate: 2023-11-21
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- Optimal Station Placement Method for Three-Station TDOA Localization Under
Signal Beam Constraint-
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Abstract: Abstract The accuracy of passive localization is impacted by the position of the receiving stations. In practical applications, signal reception is frequently influenced by the emission angle of the target signal, which makes it a challenge to ensure localization accuracy. We present an optimal placement method for TDOA localization with constraints on the signal angle. Firstly, we establish a localization error model based on polar coordinates, which reveals that the localization error is solely dependent on the angular relationships between the target and receiving stations. We determine the optimal locations of the stations without constraints. We then introduce a novel approach where the quarter-angle and one-fifth angle within the emitted beam angle are employed as critical values to determine station placement. Given the constraint of the angle, we use the critical values to evaluate the angular relationship between the station and the target, determining if the station should be positioned on the boundary or on the angle bisector to achieve optimal placement method. Finally, we validate our proposed method through simulations, demonstrating its ability to minimize errors in various scenarios. The method proposed provides an effective solution to the challenge of target localization with constraints on the received signal beam angle.
PubDate: 2023-11-13
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- Bio-Inspired Approach to Extend Customer Churn Prediction for the Telecom
Industry in Efficient Way-
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Abstract: Abstract Churn prevention has always been a top priority in business retention. The significant problem of customer churn was confronted by the telecommunications industry due to saturated markets, harsh competition, dynamic criteria, as well as the launch of new tempting offers. By formalizing the telecom industry’s problem of churn prediction as a classification task, this work makes a contribution to the field. To effectively track customer churn, a churn prediction (CP) model is needed. Therefore, using the deep learning model known as the reformatted recurrent neural network in conjunction with the Elephant herding optimization (EHO) method, this work provides a novel framework to forecast customer turnover (R-RNN). EHO is a meta-heuristic optimization algorithm that draws inspiration from nature and is based on the herding behaviour of elephants. The distance between the elephants in each clan in relation to the location of a matriarch elephant is updated by EHO using a clan operator. For a wide range of benchmark issues and application domains, the EHO approach has been shown to be superior to several cutting-edge meta-heuristic methods. In order to classify the Churn Customer (CC) and a regular customer, RRNN is modified. This improved EHO effectively optimises the specific RNN parameters. If a client churns as a result, network usage is examined as a retention strategy. However, this paradigm does not take into account the number of consumers who leave based on how often they use their local networks. The results of the simulation and performance metrics-based comparison are assessed to show that the newly proposed technique can identify churn more successfully than pertinent techniques.
PubDate: 2023-11-08
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- Sales Demand Forecasting in Car Industry Using Seagull Optimization Based
Holt Winter and Quantile Regression Neural Network-
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Abstract: Abstract Automobile manufacturing industry is at recent trend because people all over the globe requires automobile to support transportation. Due to increase in demand for automobile vehicles, forecasting its sales has become quite significant. Devoiding of sales forecasting results in excess raw material procurement, delay in product delivery anf excess production. To address these issue, sales demand forecasting model must be developed for predicting the future sales of the product. However the exisiting sales demand forecasting model fails to achieve accurate prediction of future sales due to process complexity. So, this current research focus on designing an automated sales demand forecasting model using statistical and neural network approach for forecasting future sales in the car industry. In order to forecast furture sale of cars, past sales record is essential. Initially, the past sales record is collected from three car industries. Using this past sales information, the future sales is predicted in two scenarios. The first scenario is a prediction of linear series of data using the Seagull Optimization-based Holt Winter (SO-HW) method. Then, in the second scenario, the prediction of nonlinear data series is achieved using Quantile Regression Neural Network (QRNN). The performance of the proposed sales demand forecasting model is estimated using parameters such as RMSE, MSE, MAE and MAPE. The value of RMSE and MSE for proposed forecasting model is 0.5%, 0.6% and 0.3%, 0.4% respectively. Based on this analysis, it is suggested that error metrics are minimal for the proposed sales demand forecasting model, and this proves that forecasting of seasonality, level and trends of car sales can be attained effectively.
PubDate: 2023-10-30
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- MSHRP: Mobile Sink Based Limited Hop Routing Protocol for Wireless Sensor
Networks-
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Abstract: Abstract In a wireless sensor network, sensor nodes can send data to the sink directly or through some intermediate nodes. The nearby nodes of the sinks are heavily loaded and thus exhaust energy more rapidly and create a hotspot problem in the network. Mobile sink nodes enable the sensor network to enhance its lifetime. In this paper, a routing protocol is proposed and considers more than one number of mobile sinks in the network. Here, two different types of sink movements (Random Sink Movement) and (Circular Sink Movement) is proposed. The first one is based on the random waypoint model, whereas, in the second one, the sinks move in a circular path. The sink nodes broadcast their mobility information into the network during movement in regular intervals to make aware of the sensor nodes regarding sinks availability. The sinks node mobility information helps the sensor nodes to discover the most energy efficient and delay tolerant path towards the sink node. Simulation results show that our proposed MSHRP routing algorithm can reduce the hotspot problem and lengthen the network lifetime. Further an improvement is observed on the performance in comparison with the existing protocols in terms of energy consumption, end-to-end delay, node lifetime, and average hop distance to sink. The proposed RSM model reduces the energy consumption by 51.56 percent,43.3 percent and 20 percent respectively than RBR, PEGASIS and EPEGASIS. Similarly, the proposed CSM model reduces the energy consumption by 67.8 percent, 62.11 percent and 46.7 percent as compared to RBR,PEGASIS and EPEGASIS respectively.
PubDate: 2023-10-27
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- Performance Enhancement of Indoor Cellular Visible Light Communication
through Cell Size and Wavelength Reuse Pattern-
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Abstract: Abstract The emerging technology of visible light communication (VLC) has become a promising solution for the requirements of wide-bandwidth, high-speed, and infinite-capacity wireless communication networks. A novel design of high-performance multi-user indoor visible light communication (VLC) based on hexagonal-cell arrangement is introduced in the present paper. The wavelength division multiple access (WDMA) is used to enable multiple users to communicate through the network of access point (APs) by assigning a unique wavelength division channel (WDC) to each user. A wavelength reuse scheme is utilized to increase the maximum number of users in the system and to enhance the VLC system capacity. The on–off keying (OOK) is used as the modulation technique for light signaling. The intercell interference (ICI) caused by the wavelength reuse is evaluated and its dependence on the cell radius and the wavelength reuse pattern size is numerically investigated. Both the received power density and the ICI at the location of the moving user are evaluated and the resulting signal-to-ICI ratio (SICIR) is calculated at every point over the indoor area. The VLC system capacity is evaluated and its dependence on the design parameters including the cell radius, the size of the wavelength reuse pattern, and the user data rate is numerically investigated. A design procedure is proposed to minimize the bit-error rate (BER) resulting from the ICI and to maximize the system capacity and the maximum allowable number of users in the system by selecting the optimal radius of the hexagonal cells and the most appropriate size of the frequency reuse pattern. The effect of the data rate per user on the system capacity is numerically investigated. It is shown that a SICR of greater than 21 dB and BER of less than 1 × 10−15 is achieved. Also, a system capacity of more than 4 bps/Hz is achieved by the application of the proposed VLC design optimization procedure.
PubDate: 2023-10-18
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- Design of an Internet of Things Powered Automated Power Factor Correction
System and Monitoring of Consumption of Energy-
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Abstract: Abstract Monitoring of energy enables easy access and provides information on power use, normal and abnormal circumstances. Many applications today, mainly in the industrial sector, struggle with power quality issues. The power factor is crucial when it comes to power quality. Increased energy efficiency and lower energy expenses can be accomplished with the aid of power factor (PF) modification and monitoring of the consumption of energy. A capacitance is supplemental to counteract power factor deterioration and lessen power loss. In order to monitor a system’s energy usage and automatically boost its power factor, this study attempts to develop an Automatic Power Factor Correction (APFC) system and energy monitoring using IoT techniques and develop a mobile application for increased comfort and convenience. An open-source energy monitoring library was designed and implemented for precise power calculations. Using a capacitor bank and Internet of Things (IoT) technologies, this paper conducted hardware experiments for energy monitoring and automated power factor correction concerning various loads and various real-time fluctuating case study-based assessments. The Pure restive load (R Load), series resistive-inductive loads (S-RL Load) and parallel resistive-inductive (P-RL Load) loads were used to validate the performance of the designed hardware model. The outcome demonstrates that the intended Raspberry Pi-based energy monitoring and self-governing power factor correction system excels in enhancing the power quality automatically obligating no human input through appropriate transitioning of the capacitor bank’s capacitors. The inductance in this study results in a lag with a power factor of 0.747 and 0761 for the series RL and parallel RL loads, respectively. The recommended strategy connects the capacitor banks according to the inductive load, resulting in a power factor of 0.972 and 0.977 for series RL and parallel RL loads, respectively. The concerns relating to power loss, penalty, and power quality were thus handled using the suggested methodology. The design that has been suggested helps to improve the power system and is small, straightforward, and simple to execute.
PubDate: 2023-10-11
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- Evaluating the Sustainable COVID-19 Vaccination Framework of India Using
Recurrent Neural Networks-
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Abstract: Abstract COVID-19 has laid an impact on every sector of the world. Howsoever severe, vaccines have acted as the sole source of a protective guard to prevent the further spread of the COVID-19 pandemic. In this research, the authors broadly focus on the trends in the vaccination drive of India. The paper revolves around a prediction and evaluation approach, which depending on the past and the current trends of daily vaccinations, obtain comparable results using a self-built recurrent neural network of LSTM layers for this study on time series evaluation. Through the neural network, the study predicts the exact vaccination figures likely to be achieved 1 year after vaccine introduction in the Indian subcontinent. The gathered data from January 16, 2021, until September 30, 2021, follow effective visualization of how the model outputs resemble the vaccination numbers for October 2021 and the predictions until January 16, 2022. Finally, the paper follows an extensive data analysis keeping in mind, the analogy of the number of COVID-19 cases and deaths before and after the vaccination system was centralized, to prove how sustainable the framework has been so far.
PubDate: 2023-10-11
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- Exploring the Challenges and Tensions of Privacy Using Internet of Things
(IoT) and Cloud Technologies-
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Abstract: Abstract The Internet of Things (IoT) is a global system of networked physical devices that will play a significant part in the next generation of the web (FI). More than 50 billion gadgets are predicted to be linked to the internet by 2023. As a result, a large number of Apps and services will be necessary to make these items readable, identifiable, locatable, accessible, and/or controlled through the web. Trust in the IoT security architecture is crucial for the IoT to be extensively accepted by both consumers and businesses. It is crucial to specify how data may be sent and received between devices in the IoT in a safe and reliable manner. We begin by developing a mechanism for establishing group keys, which will allow for secure multicast group communication in the Internet of Things. The second security aspect of our work is the design of a lightweight biometric user anonymity-preserving authentication protocol. “The third security aspect of our work is a technique for preserving the Source Location privacy. Improvements in accessibility are being made possible by the Internet of Things and the data it generates, in areas as diverse as smart homes and autonomous vehicles.” As a result, Internet of Things gadgets and services are helping individuals with impairments become more independent and engaged in their daily lives.
PubDate: 2023-10-09
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- Node Layout Optimization Strategy Based on Aquaculture Water Quality
Monitoring System-
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Abstract: Abstract Due to the complex environment in the field, the number of nodes and the energy consumption of nodes should be considered in the deployment of aquaculture water quality monitoring system. Therefore, according to the actual network framework of aquaculture water quality monitoring system, based on the energy balance mechanism of clustering routing protocol, clustering mode and path energy consumption model, a new node layout and energy consumption optimization strategy is proposed in this paper, by improving artificial bee colony algorithm and genetic algorithm, the number of relay nodes and energy consumption of network are reduced. Through simulation and comparison, it is verified that the network coverage can be increased by 36.92% when the proposed optimization strategy and PSO perform the node placement task in the same scenario. The improved artificial bee colony algorithm has a significant improvement in the network coverage of the monitored area with the same number of nodes. On the basis of this, the final node layout scheme obtained by GA extends the life cycle of the network to a certain extent, and proves the guidance and application value of the strategy in the process of system building.
PubDate: 2023-10-01
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- Multi-view Feature Learning Based on Texture Description for Palm-Print
Recognition-
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Abstract: Abstract Biometric is the science of validating an individual’s identity while using behavioral and physiological characteristics. In unconstrained scenario, contactless palm-print recognition leads to better recognition accuracy of individuals. Most of the existing texture descriptors are fail to learn stable and discriminative features from palm-print images. The paper presents a multi-view feature learning method based on texture description for palm-print recognition. The multi-view features are simultaneously extracted by two complementary operators. We also learn how to use feature mapping to convert multi-view data into hash codes. Experiments are carried out on palm-print databases captured using a variety of devices and acquisition methods. We demonstrate that the proposed method has superior performance compared to the current methods.
PubDate: 2023-10-01
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- A Framework for QoS Parameters-Based Scheduling for IoT Applications on
Fog Environments-
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Abstract: Abstract Job scheduling in fog computing environments is an evolving area of research and researchers are finding improved ways to assign jobs to the appropriate fog nodes (FN). This paper proposes a job scheduling framework that divides geographical areas into hexagonal regions for better coverage of available resources. Each hexagonal region is considered as a fog environment (FE) and the proposed framework chooses an appropriate FE and FN with the help of brokers. Most of the scheduling frameworks ignore geographic coverage of fog resources, consequently causing the over and under-utilization of available fog resources. The hexagonal shape of FEs in the proposed framework ensures that every single instance of fog resource is covered and utilized for job execution. In addition to this, a Quality of Service (QoS) parameter-based job selection methodology is adopted to choose an FN from the available FEs. Three parameters physical proximity, bandwidth, and Quality of Devices are being considered to calculate a QoS score. The final decision is made based on the comparison of the deadline specified in the Service Level Agreement and the average execution time of the FN. Experimental results of the implementation of the proposed system show better coverage and QoS achievement along with improved resource utilization, resource availability waiting time, response time, and completion time. Experiments are performed on the tasks generated by applications available on the Aneka platform. These tasks are scheduled by a GB (GB), which is a windows 10 machine with Intel(R) Core (TM) i7-10,700 CPU 2.90 GHz processor.
PubDate: 2023-10-01
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- A Review on Machine Learning-based Malware Detection Techniques for
Internet of Things (IoT) Environments-
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Abstract: Abstract Internet of Things (IoT) is the recent digital trend that connects the physical and virtual world. The strong bonding between the people, objects, machines and the web are assisting to develop new business models and also ensuring a better communication framework. On the other side, IoT devices are the main targets for cybercriminals that take vulnerable action over the authentication model, outdated data services and the malware. Henceforth, the security metrics of IoT devices is explored by several researchers while focusing on IoT malware. Many studies on the security issues for IoT systems are explored. Specifically, the employment of Machine learning techniques used for detecting the IoT malwares is studied. In this paper, a detailed survey on detecting the IoT malware using ML techniques are presented. Initially, the fundamentals of the malware analysis and the process and tools used to identify the malwares are discussed. The main intention of this survey is to support the security analysts who are interested to understand and innovate new trends in ML for IoT devices. This study is categorized into two groups, namely, machine learning techniques and neural networks. Both the groups are reviewed from the aspects of preprocessing and feature extraction process of the suggested ML techniques. The study ends the research issues in this field from the aspects of evaluating the performance of methods, as dataset collection, parameter optimization, neural network structure, throughput and scalability.
PubDate: 2023-10-01
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- Chirp Signal Based Timing Offset Estimation for GFDM Systems
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Abstract: Abstract Generalized frequency division multiplexing (GFDM) is a block-based multicarrier modulation scheme that is a potential candidate for the fifth generation (5G) and beyond wireless communication systems. A critical factor in enhancing the performance of GFDM is achieving precise synchronization. To that end, a new timing synchronization algorithm that utilizes cross-correlation and the sliding window algorithm is presented in this article. The algorithm uses a training symbol based on a chirp sequence. The simulation results of the proposed estimator and conventional CP based approach are analyzed under indoor office scenarios and urban macro cells scenarios for orthogonal frequency division multiplexing and GFDM. The performance of the proposed method is better for GFDM in terms of the three parameters: mean of the timing offset, mean square error of the timing offset, and probability of timing failure. For the proposed algorithm, the results show that the probability of timing failure decreases with increasing signal to noise ratio in the case of the GFDM system.
PubDate: 2023-10-01
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- Optimal Physical Shared Channels NB-IOT Design BLER Assessment, for
Cellular LTE WAN Network in Smart Healthcare-
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Abstract: Abstract Particularly, for real time monitoring-IOT has just evolved to support massive capacity Cellular LTE networks. The fundamental issue is the constrained bandwidth that NB-IOT channels allow. Therefore, goal of paper is to enhance the capacity of the NB-IOT physical layer shared channels for uplink and down links. The primary objective of this article is to improve the efficiency of an LTE-OFDM framework for NB-IOT-WAN via BER and BLER assessment. The proposed approach improves performance by selecting suitable cyclic prefix sampling with FFT size. It is planned to adopt M-QAM for increased data transmission capacity and reducing OFDM BER. In order to enhance the capacity approach of diversity reception is considered along with the modified modulation parameters. Increased FFT size is offered for OFDM capacity enhancement for narrowband physical downlink shared channel (NPDSCH) and narrowband physical uplink shared channel (NPUSCH) transceivers. The block error rate (BLER) is used as the performance evaluation parameters for the LTE-OFDM based NB-IOT system. The impact of the higher block transmission length is assessed for the improvement in the BLER rates. It is proposed to use M-QAM for NPUSCH which may offer better capacity of data transmission. The performance is compared with the standard QPSK modulation. For the uplink design under AWGN and frequency selective channels are considered for evaluation. The numbers of receiver antenna are increased for offering the better diversity reception to further enhance the channel capacity. The optimal design parameter selection might improve the performance of the NB-IOT cellular networks. The BLER is evaluated for different case of uplink and downlink designs against the wide range of the SNR. The entire document takes into account all factors in order to get the greatest system efficiency, and it offers capacity enhancement.
PubDate: 2023-10-01
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- Design and Simulation of Air-Core Polymeric Photonic Crystal Fiber by
Investigating the Reduction of Confinement Losses in the Terahertz (THz)
Range-
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Abstract: Abstract In the present study, a photonic crystal fiber with a simple design and low losses in the range of terahertz broadband pulse was designed. The proposed structure of this study consisted of a large central air core arranged by three rings of air holes in a regular hexagonal pattern in a uniform Teflon matrix. The refractive index of Teflon, which was made of polymer, was 1.44. The conduction in this fiber was achieved using photonic band gap (PBG) only for a certain range of non-zero values. The simulation results showed that at the wavelength of 174 µm with a central hole diameter of 2.9Ʌ (Ʌ = 300 µm), the lowest confinement losses equal to 0.2 dB/m occurred and the dispersion parameter was 1.2 ps/nm km.
PubDate: 2023-10-01
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- Design and Implementation of 3.2-GHz Co-Planar Miniaturized Antenna for
S-Band Communication and Wireless Applications-
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Abstract: Abstract The use of miniaturized antennas in wireless communications is very common. In the current paper, a miniature coplanar-waveguide fed-rectangular patch antenna with semicircular ground is presented. The antenna performance was studied at two different configurations; straight and bent. Cross lines were added to ensure the obtained frequency band. Different parameters were evaluated including return-loss, radiation-pattern, gain and band-width. These parameters were analyzed numerically after twisting along both X and Y axis. Additionally, a prototype of the straight structured antenna is fabricated, and compared with the simulation results. The numerical results show high return loss (− 33 dB) at the straight structure, while the measured return loss decreased to − 28 dB. The bandwidth was 0.75-GHz in case of the straight structure and the measured bandwidth 0.18 GHz. The obtained gain at the resonance frequency is − 13 dB. Moreover, the proposed antenna resonates at frequency 3.22-GHz making it suitable for wireless communications, WIMAX and microwave S-band applications.
PubDate: 2023-10-01
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- Oppositional Grass Hopper Optimization with Fuzzy Classifier for Face
Recognition from Video Database-
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Abstract: Abstract Recognizing faces from the video employs artificial intelligence-based computer technology. This includes applications such as law enforcement, biometrics, security, personal safety, etc., for tracking and enabling surveillance in real-time. However, detection and recognition of the face from the video are influenced by the change in variation of pose, brightness, occlusion, expression, and resolutions. While facial images are simple to detect, others may necessitate the use of specialized software. To address those challenges we propose an efficient face detection and recognition system with optimal features. Initially, the keyframes are extracted by the Key Frame Extraction method which utilizes Wavelet Information. Subsequently, the characteristics such as holo-entropy, appearance features, SURF feature, and multi-angle movement feature are extracted. The Oppositional Grass Hopper Optimization Algorithm is used to identify optimal features from this large feature set. The extracted features are classified using the fuzzy classifier that was designed. The proposed method's performance is validated using several benchmark video datasets and its efficiency is measured in terms of keyframe extraction time, sensitivity, accuracy, and specificity by comparing it to other state-of-the-art approaches. The proposed method outperforms previous state-of-the-art methods in terms of recognition accuracy.
PubDate: 2023-09-06
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