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Attack Detection and Optimal Deployment for Underwater Constrained Wireless Sensor Networks via Hybrid Trust Evidence

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1791-1803页

作者： Jiang, Bin Zhou, Ronghao Luo, Fei Cui, Xuerong Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Northeastern University Computing Programs ArlingtonVA22209 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Underwater wireless sensor networks have been widely used in the acquisition and processing of oceanic information. The marine environment is complex and changeable, and the existence of obstacles is the main manifestation of the complex underwater environment, which affect the communication between underwater nodes. In addition, wireless sensor networks with obstacles are often more vulnerable to various attacks, making it more fragile. In order to address the aforementioned issues, we firstly propose a underwater wireless sensor deployment strategy with obstacle avoidance as the target (GEHO). After that, we use Tabtransformer algorithm to build trust model and detect attacks according to trust data set, which can enhance the robustness of the entire wireless sensor network. In the final stage, we collect the patterns of malicious attacks on nodes according to the detection results, which is convenient for us to make timely responses and reduce the losses of underwater wireless sensor networks due to malicious attacks. The simulation results show that the trust model can effectively detect malicious nodes and attack types in the network, and has higher detection accuracy than the existing trust model. © 2013 IEEE.

关键词： Hybrid sensors

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ReCaLS-in-DLT: resilient consensus algorithm for leader selection in distributed ledger technology

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Cluster computing 2025年第5期28卷 1-17页

作者： Hussain, Munir Mehmood, Amjad Khan, Muhammad Altaf Wang, Huihui Song, Houbing Herbert Maple, Carsten Institute of Computing Kohat University of Science and Technology Kohat Pakistan WMG University of Warwick Coventry United Kingdom Computing Programs Northeastern University 1300 17th St N 1500 ArlingtonVA22209 United States Department of Information Systems University of Maryland BaltimoreMD United States

Distributed ledger technology (DLT) is most widely used in many areas for example healthcare, finance, public welfare, and Internet of Things (IoT) to provide security and effi- ciency. The consensus algorithm is a key component of DLT-based networks that plays a vital role in maintaining the performance of the network and ensuring security. We proposed a consensus algorithm that selects a leader or miner node based on a lucky number for block mining. The proposed algorithm is not only scalable but also enhances security during the consensus process in the presence of less trustworthy nodes, the selected miner node is re-authenticated by the trusted authority before adding a block in the ledger. Apart from the re-authentication process, the trust level of the miner node is also checked. Statistical analysis is performed to prove the security of the proposed algorithm. The results demonstrate that our proposed algorithm offers a good level of resistance against abnormal behavior of the network nodes. Furthermore, performance evaluation is also performed through the Hyper-ledger Fabric Blockchain platform. The simulation results show that the proposed consensus algorithm provides high throughput, low latency, low CPU utilization, and communication bandwidth consumption as compared to the state- of-the-art benchmark proposed in the literature. The lightweight and secure nature of the proposed algorithm makes it a suitable solution for the consensus process in DLT. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Consensus algorithm DLT Hyperledger fabric Proof-based Trust Vote-based

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Decentralized Federated Learning in Metacomputing Based on Directed Acyclic Graph With Optimized Tip Selector

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IEEE Internet of Things Journal 2025年第10期12卷 13723-13733页

作者： Jiang, Bin Zhao, Bo Luo, Fei Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Computing Programs Northeastern University Arlington ArlingtonVA22209 United States University of Maryland at Baltimore County Department of Information Systems BaltimoreMD21250 United States

Metacomputing optimizes distributed computing resources to enhance federated learning (FL) systems by enabling efficient resource allocation, improved scheduling, and greater scalability, thereby addressing challenges in large-scale and dynamic environments. This article proposes an innovative framework integrating directed acyclic graph (DAG) technology with FL within a metacomputing environment. The key contributions include a three-layer decentralized FL model integrating DAG and metacomputing to enhance resilience and scalability, two advanced tip selection models LazyEval tip selector and precision tip selector to optimize node selection and improve data flow, and a benchmark improvement protocol (BIP) for efficient node publishing and role adaptation. The BIP ensures that only high-performing models are published by comparing new models against established benchmarks, which enhances node collaboration and optimizes resource allocation. LazyEval Tip Selector minimizes redundant computations by leveraging a global cache and employing a lazy evaluation strategy, thereby improving computational efficiency. On the other hand, Precision Tip Selector uses a precise scoring mechanism to ensure accurate tip selection, thereby enhancing the robustness and reliability of the entire system. Collectively, these innovations enhance model training efficiency, support real-time updates, and improve the scalability of FL systems, making them well-suited for managing complex, dynamic environments. © 2014 IEEE.

关键词： Federated learning

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An Empirical Study of Sewage Issues and Management Systems in a Rural Village of India 1st

An Empirical Study of Sewage Issues and Management Systems i...

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1st International Conference on Pollution Control for Clean Environment, ICPCCE 2023

作者： Manikandan, A. Bulusu, Abhiram Parthasaradhi, H. Prakash, K. S. Reddy Banu Jayan, Ridhika Kunnambath, Akhil Revi Mohan, Renjith Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India Amrita Center for International Programs Amrita Vishwa Vidyapeetham Vengal India

ISBN: (纸本)9789819778416

Village sewage systems can differ significantly depending on the location, size, state of the local economy, and local government policies. Rural communities frequently need better built or unofficial sewage systems compared to urban regions. Some common sewage systems are Open Drainage Systems, septic tanks, Pit Latrines, Community sewage systems, Eco-friendly solutions, and some government initiatives. It's important to remember that a range of factors, such as resource availability, area geography, community preferences, and political legislation, affect the choice of a sewage system. As the importance of sanitation and environmental conservation is recognized, efforts are being made in rural areas to develop more effective and sustainable sewage solutions to promote public health and save the environment. This paper studies the sewage problems and proposes solutions for Pachokara Village in Jewar Block in Gautam Buddha Nagar District of Uttar Pradesh State, India. It is located 15 km South of the District headquarters and belongs to Meerut Division of Noida. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sanitation

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Modifying Blowfish Algorithm Using Permutation Techniques

Modifying Blowfish Algorithm Using Permutation Techniques

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Jon Laurence B. Wenceslao Felicisimo V. Wenceslao Patrick D. Cerna College of Information and Computing Studies Northern Iloilo State University Iloilo Philippines Graduate Programs State University of Northern Negros Negros Occidental Philippines

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper proposes a modified version of the Blowfish Algorithm using permutation techniques. These techniques are key parts of the F-function of the algorithm. The first modification is in the bit operations while the second part employs the cyclical Shift Rows operation. We conducted simulations to test the security strength of the modified Blowfish Algorithm in changing one bit from two input strings. The avalanche effect showed 54.687% and 53.125% changes in the output bits thus making our model secure. We also tested the execution performance of the original Blowfish Algorithm and the modified Blowfish Algorithm in the encryption and decryption processes. While the original version performs better than the modified version, no statistical difference was found between the two versions.

关键词： Statistical analysis Encryption

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Modifying Blowfish Algorithm Using Permutation Techniques 24

Modifying Blowfish Algorithm Using Permutation Techniques

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Wenceslao, Jon Laurence B. Wenceslao, Felicisimo V. Cerna, Patrick D. Northern Iloilo State University College of Information and Computing Studies Estancia Iloilo Philippines State University of Northern Negros Graduate Programs Negros Occidental Philippines

ISBN: (纸本)9798331515799

关键词： Encryption algorithms

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Long-Tailed Federated Learning in Internet of Medical Things Based on Ensemble Distillation and Imbalanced Calibration

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Bin Shang, Yuchen Yue, Guanghui Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen518060 China Northeastern University Computing Programs 1300 17th St N 1500 ArlingtonVA22209 United States Department of Information Systems BaltimoreMD21250 United States

The Internet of Medical Things (IoMT) has a promising future, as its devices can monitor vital signs, offer treatment guidance, and perform real-time diagnostics using AI and wireless communication technologies. However, due to the difficulty of collecting patient data on a large scale and potential privacy risks, traditional centralized machine learning methods are often challenging to apply in IoMT devices. Federated learning, as a privacy-preserving technology, aims to build high-quality deep learning models across distributed clients while protecting data privacy. However, current popular federated learning methods exhibit suboptimal performance when dealing with non-IIDness data, especially in the case of long-tail class distributions, leading to unsatisfactory results. Additionally, due to privacy constraints on distributed clients, these methods cannot leverage traditional deep learning techniques to handle long-tail data, which is often characterized by long-tail heterogeneous distributions in IoMT. To address these challenges, this paper proposes a solution of Privacy-preserving computing Client Scoring and Knowledge Distillation (FedLT+SKD). The method uses privacy protection computation to provide prior knowledge of global data class distribution while ensuring data privacy. Based on this prior knowledge, it employs a points-based sampling strategy to identify clients that perform well on long tail data and uploads their local model to the server. On the server side, the robustness of the global model is enhanced by collection distillation and imbalance correction. We verify the effectiveness of this method on the medical datasets ISIC, ChestX-ray14, MRI and also on the traditional datasets CIFAR-10-LT and CIFAR-100-LT, and the experimental results show that the method is superior to the popular federation and long-tail learning methods. © 1975-2011 IEEE.

关键词： Differential privacy

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Federated learning with Blockchain on Denial-of-Service attacks detection and classification of edge IIoT networks using Deep Transfer Learning model

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Computers and Electrical Engineering 2025年 124卷

作者： Abdullah, Monir Mengash, Hanan Abdullah Maray, Mohammed Alrslani, Faheed A.F. Alkhudhayr, Hanadi Alghanmi, Nouf Atiahallah Subahi, Alanoud Majdoubi, Jihen Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Engineering and Technical Programs Unit Applied College Majmaah University Al-Majmaah11952 Saudi Arabia

With the rapid evolution of Internet of Things (IoT) and artificial intelligence (AI), the industry 4.0 era has arisen. As per the IBM prediction, by the constant spread of 5G technology, the IoT intend to be more extensively utilized in industries. Recently, federated learning (FL) turned out to be significant focus among Industrial IoT (IIoT) scholars. Conversely, numerous devices in IIoT presently hold an issue of low computational power, so these devices can't able to function well while challenging the updating and training method tasks in FL. The latest development of machine learning (ML) and deep learning (DL) approaches will help to reinforce these IDSs. The secrecy behaviour of these databases and extensive advent of combative outbreaks makes it challenging for main institutions to transmit their fragile data. Therefore, the current study designs a Federated Learning on Denial-of-Service Attacks Detection and Classification using Deep Transfer Learning (FLDoSADC-DTL) model for BC-supported IIoT environment. The aim of the presented FLDoSADC-DTL approach is to recognize the presence of DoS attacks in the BC-based IIoT environment. To enable secure communication in the IIoT networks, BC technology is used. To accomplish this, the FLDoSADC-DTL technique performs sand cat swarm algorithm (SCSA) based feature subset selection. For the DoS attack detection process, a stacked auto-encoder (SAE) model is utilized in this study. Finally, the black widow optimization algorithm (BWOA) can be implemented for hyperparameter tuning of the SAE technique. A widespread of experiments were performed to emphasize the higher performance of FLDoSADC-DTL method. The complete experimentation outcomes indicated the enhanced performance of FLDoSADC-DTL approach over other recent methods. © 2025 Elsevier Ltd

关键词： Federated learning

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Lessons learned from the implementation of an internetworking remote access laboratory

Lessons learned from the implementation of an internetworkin...

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113th Annual ASEE Conference and Exposition, 2006

作者： Houston, Shelton Herrod, Christopher Blesse, Steven University of Southern Mississippi United States School of Computing University of Southern Mississippi United States Information Technology Programs

Improving student learning outcomes in Information Technology programs often depends upon maximizing student access to technologies. Courses dealing with internetworking technologies - routing, switching, dialup - may be constrained by the use of simulation applications and their inherent functional limitations, or by the use of live equipment onsite with its inherent high cost and limited access to students. This paper describes the implementation of a remote access internetworking laboratory designed for the purpose of improving student learning outcomes through increased access to internetworking laboratory equipment. Technical planning, design, and implementation issues are examined along with the capabilities and limitations of this implementation. Student feedback from a pilot course is presented. Advantages and disadvantages of the implementation are addressed including scheduling of classes, routine maintenance, management of network topologies, and student access. © American Society for Engineering Education, 2006.

关键词： Engineering education

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Towards Designing an Intelligent Recommender System using Adaptive Collaborative Filtering Technique 3

Towards Designing an Intelligent Recommender System using Ad...

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3rd International Symposium on Computer Technology and Information Science, ISCTIS 2023

作者： Zhang, Qi Abisado, Mideth College of Computing and Information Technologies National University Graduate Programs Department Manila Philippines TangShan Normal University Hebei China

ISBN: (纸本)9798350325386

In recent years, intelligent recommendation systems using adaptive collaborative filtering have received great attention due to their ability to provide personalized and relevant recommendations. The innovation of this study lies in proposing an adaptive collaborative intelligent recommendation model (ACIR), which combines adaptive collaborative filtering with context, user behavior, and movie feature information. It utilizes automatic encoders and deep learning algorithms to capture the characteristic relationships of the context, explore the internal connections between the context and users, and improve the accuracy and interpretability of the recommendation system to meet the personalized needs of users. The aim of this study is to predict user preferences by considering the characteristics of users, projects, and contexts, and to evaluate the potential changes in user preferences over time. In the experimental stage, LDOS CoMoDa was used to test and train the dataset, and the model was validated. Choose traditional collaborative recommendation, k-means algorithm, CAMF, and CAMF_ CM algorithm for comparison. After experimental verification, this article proposes that compared with other traditional recommendation algorithms, ACIR has higher accuracy and stronger application value © 2023 IEEE.

关键词： Recommender systems

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