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Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Intelligent Automation & Soft Computing 2023年第4期36卷 879-894页

作者： T.Shanmugapriya Dr.K.Kousalya Department of Information Technology SNS College of TechnologyCoimbatore641035India Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India

The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst *** is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by *** is not much published information on issues related to node mobility and management of energy at the time of aggregation of *** the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efﬁcient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artiﬁcial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy *** is avoidance of the local optima problem at the time of solution space search in this proposed *** have been conducted on a large WSN network that has issues with mobility of nodes.

关键词： Wireless sensor network routing clustering mobility low-energy adaptive clustering hierarchy energy efﬁcient heterogeneous clustered artiﬁcial bee colony

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A dynamic and resource sharing virtual network mapping algorithm

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Digital Communications and Networks 2023年第5期9卷 1101-1112页

作者： Xiancui Xiao Xiangwei Zheng Ji Bian Cun Ji Xinchun Cui School of Information Science and Engineering Shandong Normal UniversityJinan250014China School of Information Engineering Shandong Management UniversityJinan250358China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014China School of Computer Science and Technology Qufu Normal UniversityRizhao276800China

Network virtualization can effectively establish dedicated virtual networks to implement various network ***,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and *** addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation *** on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology *** the node mapping process,three properties of the node are used to measure its mapping ***,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link ***,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource *** former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision *** results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenu

关键词： Network virtualization VNRs Network frameworks Dynamic resource allocation Resource sharing

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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FeAdaBoost: EnhancingFederated Learning Accuracy by Ensemble Techniques 29

FeAdaBoost: EnhancingFederated Learning Accuracy by Ensemble...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Fotoohabadi, Vida Afzali, Afsaneh Shamsinejadbabaki, Pirooz Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran

ISBN: (纸本)9798331523114

Federated Learning (FL), an advanced technique in machine learning (ML), allows for model training without transferring data to a central server. This method is particularly valuable in sensitive applications, such as healthcare and finance, as it preserves data privacy and reduces security risks. However, FL faces the challenge of reduced accuracy compared to centralized approaches due to limited access to the full dataset. To address this issue, we propose a solution that integrates the ensemble learning method AdaBoost with FL, which we call FeAdaBoost. We validate the effectiveness of our approach using the task of classifying a chest X-ray image dataset. The experimental results confirm that the proposed approach significantly improves both accuracy and efficiency compared to traditional FL. © 2025 IEEE.

关键词： Federated learning

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IoT-Supported Indoor Navigation System Using Machine Learning Technique 12th

IoT-Supported Indoor Navigation System Using Machine Learnin...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Bhuvana, S. Vidhya, K. Sudharsan, S. Suhita, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819788354

Indoor Navigation System (INS) supports seamless movement of objects within confined spaces in smart environments. In this paper, a novel INS that relies on ESP32-based Received Signal Strength Indication (RSSI) measurements for precise estimation of distance using K-Nearest Neighbour (K-NN) algorithm is proposed. The system architecture includes three ESP32 nodes functioning as clients, and ESP8266 combined with ESP32 acting as a server. To overcome the limitations of Global Positioning System (GPS) within an indoor environment, Machine Learning (ML)-based prediction models are employed to estimate distance between nodes and predict user locations along with the distance between source and destination. This approach not only addresses the challenges posed by GPS in indoor settings, but also shows the potential of ESP32 devices and predictive algorithms for enhancing navigation accuracy and reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Joint Beamforming, Power Control, and Interference Coordination: A Reinforcement Learning Approach Replacing Rewards With Examples

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IEEE Access 2023年 11卷 88854-88868页

作者： Sheu, Jeng-Shin Huang, Cheng-Kuei Tsai, Chun-Lung National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin640002 Taiwan

In this paper, we consider the problem of multi-cell interference coordination by joint beamforming and power control. Recent efforts have explored the use of reinforcement learning (RL) methods to tackle this complex optimization problem. Typically, a decentralized multi-agent framework is adopted, wherein each base station operates as an independent RL agent. This distributed coordination has gained attention because designing a reward function that effectively captures the condition of the entire cellular network is challenging for single-agent RL models. However, the distributed approach introduces unique challenges, particularly the non-stationary of the multi-agent environment, as agents continually adapt their policies to interact with one another. The non-stationary environment necessitates information exchange among agents, as local observations of each agent are insufficient to fully capture the true state of the environment. Unfortunately, this information exchange incurs a significant overhead, thereby limiting data transmission capabilities. To address these challenges, we propose a novel single-agent RL approach that eliminates the need for information exchange and the conventional reward function. Instead, we leverage success examples to guide the learning process. Simulation results show that the proposed approach outperforms the existing multi-agent method and theoretical algorithm in terms of sum rates. Additionally, our approach ensures a uniform quality of service while maximizing the overall sum rate. © 2013 IEEE.

关键词： Beamforming

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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

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Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Preserving friendly stacking and weighted shadows in selective scalable secret image sharing

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Multimedia Tools and Applications 2023年第19期82卷 29283-29305页

作者： Lee, Jung-San Chen, Yin-Chin Hsieh, Yun-Hao Chang, Shih-Hao Huynh, Ngoc-Tu Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

With advancement of technology, the technique of selective scalable secret image sharing (SSSIS) was first proposed in 2017 as a new alternative of secret image sharing. The region of interest (ROI) in the protected secret could be revealed progressively once the number of collected shadows has reached a predefined threshold. Accordingly, the ROIs of the secret image increase based on the number of collected shadows. Nonetheless, players without sufficient shadows may be able to learn the location and contour of hidden secret to perform advanced guessing attacks. Thus, we aim to introduce friendly stacking to yield a genuine outcome of mitigating this risk during the recovery process. Furthermore, the weighting concept is brought into constructing the meaningful shadow so that the priority of a specific participant can be emphasized to approach real applications. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Image segmentation

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TSC:Efficient FRB Signal Search by a Two-stage Cascade Deep Learning Model on FAST

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Research in Astronomy and Astrophysics 2025年第3期25卷 60-69页

作者： Ronghuan Yan Junlin Li Weixin Tian Nyasha Mkwanda Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University College of Computer and Information Technology China Three Gorges University

Fast Radio Bursts(FRBs) have emerged as one of the most intriguing and enigmatic phenomena in the field of radio astronomy. The key of current related research is to obtain enough FRB signals. computer-aided search is necessary for that task. Considering the scarcity of FRB signals and massive observation data, the main challenge is about searching speed, accuracy and recall. in this paper, we propose a new FRB search method based on Commensal Radio Astronomy FAST Survey(CRAFTS) data. The CRAFTS drift survey data provide extensive sky coverage and high sensitivity, which significantly enhance the probability of detecting transient signals like FRBs. The search process is separated into two stages on the knowledge of the FRB signal with the structural isomorphism, while a different deep learning model is adopted in each stage. To evaluate the proposed method,FRB signal data sets based on FAST observation data are developed combining simulation FRB signals and real FRB signals. Compared with the benchmark method, the proposed method F-score achieved 0.951, and the associated recall achieved 0.936. The method has been applied to search for FRB signals in raw FAST data. The code and data sets used in the paper are available at ***/aoxipo.

关键词： techniques: high angular resolution techniques: image processing techniques: interferometric

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