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Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

computer Systems science & engineering 2023年第1期44卷 423-440页

作者： Devakirubai Navulkumar Menakadevi Thangavelu Department of Computer Science and Engineering Jayam College of Engineering and TechnologyDharmapuri636813India Department of Electronics and Communication Engineering Adhiyaman College of EngineeringHosur635109India

Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

关键词： Vehicular cloud container migration cell transmission model 5G NR V2X PC5 interface

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

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computer Systems science & engineering 2023年第1期44卷 265-280页

作者： M.Rajakani R.J.Kavitha A.Ramachandran Full Time Research Scholar Department of Electronics and Communication EngineeringUniversity College of EngineeringPanruti607106India Department of Electronics and Communication Engineering University College of EngineeringPanruti607106India Department of Computer Science and Engineering University College of EngineeringPanruti607106India

Feature extraction is the most critical step in classification of multispectral *** classification accuracy is mainly influenced by the feature sets that are selected to classify the *** the past,handcrafted feature sets are used which are not adaptive for different image *** overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this *** extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three *** selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based *** Multiclass Support Vector Machine(MSVM)is used for classification *** con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

关键词： Multispectral image modifiedfireﬂy algorithm 3-D feature extraction feature selection multiclass support vector machine classification

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Enhancing link prediction in graph data augmentation through graphon mixup

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Neural Computing and Applications 2025年第8期37卷 6267-6282页

作者： Sultana, Tangina Hossain, Md. Delowar Morshed, Md. Golam Lee, Young-Koo Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer N

关键词： Molecules

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Design of Six Element MIMO Antenna with Enhanced Gain for 28/38 GHz mm-Wave 5G Wireless Application

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computer Systems science & engineering 2023年第8期46卷 1689-1705页

作者： K.Jayanthi A.M.Kalpana Department of Electronics and Communication Engineering Government College of EngineeringSalemIndia Department of Computer Science and Engineering Government College of EngineeringSalemIndia

The fifth-generation(5G)wireless technology is the most recent standardization in communication services of interest across the *** concept of Multiple-Input-Multiple-Output antenna(MIMO)systems has recently been incorporated to operate at higher frequencies without *** paper addresses,design of a high-gain MIMO antenna that offers a bandwidth of 400 MHz and 2.58 GHz by resonating at 28 and 38 GHz,respectively for 5G millimeter(mm)-wave *** proposed design is developed on a RT Duroid 5880 substrate with a single elemental dimension of 9.53×7.85×0.8 mm^(3).The patch antenna is fully grounded and is fed with a 50-ohm stepped impedance microstrip *** also has an I-shaped slot and two electromagnetically coupled parasitic slotted *** design is initially constructed as a single-element structure and proceeded to a six-element MIMO antenna configuration with overall dimensions of 50×35×0.8 mm^(3).The simulated prototype is fabricated and measured for analyzing its performance characteristics,along with MIMO antenna diversity performance factors making the proposed antenna suitable for 5G mm-wave and 5G-operated handheld devices.

关键词： MIMO antenna parasitic elements 5G mm-wave dual band frequency diversity factors

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G communication Environment

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Tsinghua science and Technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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Multiuser beamforming and transmission based on BAAS and signal prediction using channel allocation

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Multimedia Tools and Applications 2024年第39期83卷 86869-86882页

作者： Bansal, Dipali Gupta, Sindhu Hak Kaur, Harleen Department of Electronics and Communication Engineering Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard New Delhi India

The performance for communication in a real-time mobile communication system is contingent upon the distribution of spectrum among the devices and the best antenna selection, which is determined by the Direction of Arrival and the power spectrum of the antenna characteristics. The prediction and directivity of signal spectrum level based on antenna parameters were the primary areas of research attention. Generally speaking, a number of techniques anticipate the antenna spectrum level by comparing the parameters of the channel capacity and the device communication signal strength. The best selection model for the communication antenna parameter's spectrum range was used to process this. In this case, improving mobile communication performance for the entire system is thought to be the primary goal of the study. To analyze the effectiveness of antenna optimization and its performance, this has been done by testing the beamforming method's parameter under a variety of scenarios. The Boundary Assisted Antenna Selection (BAAS) technique was employed in this to minimize the number of iterations required for feature learning and antenna selection. In order to create an improved beamforming model for parallel antenna parameter estimation, this was processed through the hybridization of multi-objective optimization. Using the combination of Beamforming with BAAS based antenna selection approach, which chooses the optimal feature attribute of the antenna power spectrum with Direction of Arrival (DOA), this can further accelerate performance. Metrics like the quantity of allocated spectrum, power factor, throughput, spectrum range of capacity, and other relevant factors can be used to validate the effectiveness of the suggested strategy. The results show that the proposed work achieved approximately 7.27% of Spectral efficiency (bps), Signal Power (dB) and Interference (dB). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natu

关键词： Beamforming

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Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

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computer Systems science & engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

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FusionEXNet: an interpretable fused deep learning model for skin cancer detection

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International Journal of computers and Applications 2024年第9期46卷 743-753页

作者： Gautam, Yuvika Gupta, Piyush Kumar, Deepika Kalra, Bhavya Kumar, Abhinav Hemanth, Jude D. Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics & Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Deep learning

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