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Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

International Journal of computers and Applications 2024年第5期46卷 301-309页

作者： Ranjan, Rajeev Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Bakhtiyarpur College of Engineering Bakhtiyarpur India

Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Motion planning

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Investigation on Privacy Hazards in Social Area Network 7

Investigation on Privacy Hazards in Social Area Network

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Joyce, A. Preethi, D.M.D. Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9781665464086

The Social Area networking Platform (S ANP) is a rapidly developing technology that allows users to meet friends, family, and others, who share their interests and express their personal feelings. The potential privacy threats include, Paedophilia, identity theft, spoofing, data breaches, the Koob face worm, phishing, and evil twin attacks. These attacks are major threat to user privacy information. Many people are not aware of the risks of impersonation. To avoid this, it is necessary to understand social network privacy concerns. This scenario includes a study of social network privacy concerns, as well as hazards and risks from recent events. This study significantly contributes to the classification of dangers and categorization of frauds relevant to social networks. Following the completion of a comprehensive research study of privacy concerns in S ANP, optimized techniques were identified and performance for privacy threats was also evaluated. The proposed system has used Euclidean distance to evaluate the dataset based on privacy threats. It will be useful in establishing trustworthiness on social media. © 2023 IEEE.

关键词： Hazards

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Load Balancing and Energy Efficient Routing in Software-Defined Networking 1st

Load Balancing and Energy Efficient Routing in Software-Defi...

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Rani, J. Lece Elizabeth Jeyarohini, R. Selvan, G. S. R. Emil Konar, Rahul Shiva Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031472237

Software-defined networking (SDN), a newly developed architecture that alters the route (paths) of traffic packets, is a flexible and logically centralized control plane that provides a reduction in the network’s energy usage. SDN is designed in such a way that the switches and links can handle a large amount of traffic. Their energy use is not proportional to traffic. Due to the increase in network communication, SDN devices demand much more load and energy. The SDN controller has to be modeled efficiently. The system model of the controller is proposed which efficiently distributes the load. This model helps in traffic routing with minimal energy consumption for networking devices. In addition to the above, it balances the load based on the heterogeneous traffic distribution. The proposed method optimizes the energy consumed by the networking devices by means of using the few active switches, minimizing the number of device state transitions, and handling the volume of traffic. Load balancing is performed for multiple controllers. The switch migration technique is used for migrating the traffic from one controller to the other. Energy-efficient routing is done by the proposed algorithm with reduced device state transition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2025年第17期84卷 18327-18350页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Review on Malware Classification and Malware Detection Using Transfer Learning Approach 5

Review on Malware Classification and Malware Detection Using...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Priya, V. Sofia, A. Sathya Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9781665474672

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased. © 2023 IEEE.

关键词： Malware

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

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computer Systems science & engineering 2023年第2期44卷 1233-1250页

作者： R.M.Bhavadharini S.Karthik Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking *** Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security *** is designed as a game changer for highly secure and effective digital ***,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as *** proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered *** MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected *** addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onﬁtness ***,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the *** nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different *** on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

关键词： Wireless networks blockchain routing protocol clustering security metaheuristics

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