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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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Computation Offloading in NTN-empowered MEC using Multi-Agent Distributed Deep Reinforcement Learning

Computation Offloading in NTN-empowered MEC using Multi-Agen...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Fatima, Nida Saxena, Paresh Giambene, Giovanni Bits Pilani Department of Computer Science and Information Systems Hyderabad India University of Siena Department of Information Engineering and Mathematical Sciences Siena Italy

ISBN: (纸本)9798350351255

In this paper, we investigate Non-Terrestrial Network (NTN)-empowered Multi-access Edge Computing (MEC) systems for emerging applications like telemedicine, industrial automation, and augmented reality, which demand significant computational resources from User Equipments (UEs). To address these issues, we consider that UEs can either process tasks locally or offload them to edge servers deployed on Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites. The efficacy of such systems crucially depends on formulating an efficient computation offloading policy. We propose a multi-agent distributed deep reinforcement learning (MADDRL) approach for computation offloading in NTN-empowered MEC systems. Our approach utilizes two distributed DRL frameworks: Centralized Training and Decentralized Execution (CTDE), where training occurs on a central controller, and Independent Learners (IL), where training is performed locally on the UEs. In both frameworks, policies are executed by UEs in a decentralized manner. Simulation results show that the CTDE framework reduces the average cost, defined as the weighted sum of delay and energy consumption experienced by the UEs, by 11%, 42%, and 66% compared to IL, local, and random policies, respectively. © 2024 IEEE.

关键词： Computation offloading

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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Colonic Pressure Signal Analysis Method Based on Ensemble Support Vector Machine 4

Colonic Pressure Signal Analysis Method Based on Ensemble Su...

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4th International Conference on Electronic information engineering and computer Technology, EIECT 2024

作者： Cui, Liu Si, Zhisen Zhao, Kai Wang, Shuangkui Shanghai Institute of Technology Department of Computer Science and Information Engineering Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331528850

Slow transit constipation is a common intestinal motility disorder. The colonic peristalsis can be well analyzed by the colonic pressure signal. However, medical physiological signal data are generally imbalanced, resulting in the low performance of traditional classification methods. To overcome this challenge, this paper proposes a Support Vector Machine classification method based on ensemble method. We extracted statistical features, nonlinear features and wavelet features from the colonic pressure signals, and integrated them as feature vectors to input the classifier. The experimental results show that, compared with the traditional support vector machine, the proposed method significantly improves the recognition effect of the minority class samples. Through grid search for model parameters optimization, ensemble support vector machine not only improves the recognition effect of minority class samples, but also maintains the recognition performance of majority class samples, which effectively alleviates the problem of biased toward the majority class in the classification process and improves the recognition accuracy of slow transit constipation patients. © 2024 IEEE.

关键词： Support vector machines

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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An Ant-Lion Optimization Based Approach to Solve Phase Balancing Problem in Distribution Networks 59

An Ant-Lion Optimization Based Approach to Solve Phase Balan...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Yesilyurt, Gunnur Ceylan, Oguzhan Istanbul Technical University Department of Electrical Engineering Turkey Kadir Has University Management Information Systems Turkey

ISBN: (纸本)9798350379730

Phase unbalance is a significant issue for power distribution networks. It can lead to increased energy losses and voltage instability, undermining the electrical grid's reliability and efficiency. We propose an approach to minimize voltage unbalance through reactive power management from PV instal-lations and the optimization of charging/discharging of energy storage devices utilizing a control algorithm based on Ant-Lion Optimizer. We tested the approach on the IEEE 123-Bus Test System, incorporating PV generations by daily simulations. From the results, the combined operation of reactive power support from PVs and Storage Units with the help of the ALO algorithm offers a promising solution to the phase unbalance problem. © 2024 IEEE.

关键词： Power distribution networks

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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DCCMamba: A Dual-stream Cross-time and Cross-feature with Mamba for Multivariate Time Series Forecasting

DCCMamba: A Dual-stream Cross-time and Cross-feature with Ma...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Senlin Wang, Zhuoyue Zhao, Chunna Huang, Yaqun Xu, Jinpeng Yang, Yaoyuan School of Information Science & Engineering Yunnan University Kunming China Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

In recent years, multivariate time series forecasting (MTSF) has gained significant attention. And transformer-based models have showed strong performance. However, the quadratic complexity of attention mechanisms leads to inefficiency and high overhead. The Mamba state-space model offers a more efficient alternative with lower complexity and fewer parameters. However, its unilateral nature limits the effective capture of time and feature dimensions. To address this, we propose A Dual-stream Cross-time and Cross-feature with Mamba (DCCMamba) for MTSF. Specifically, we employ a dual-stream structure to extract different information from the time and feature dimensions. One stream encodes data from a feature perspective and passes it through a Mamba layer to capture cross-time information. The other stream encodes the data from the time perspective, decomposes it into trend and seasonal components, and captures cross-feature information using a Mamba layer. Finally, we fuse the cross-feature information and cross-time information to get the final result. Experiments on four public datasets demonstrate that DCCMamba achieves state-of-the-art performance. © 2025 IEEE.

关键词： Cross-feature Cross-time dual-stream Mamba time series

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Receding horizon optimization for distributed control of electric vehicle charging stations

Receding horizon optimization for distributed control of ele...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Malkova, Anna Zepter, Jan Martin Marinelli, Mattia Amezquita, Herbert Morais, Hugo Technical University of Denmark Department of Wind and Energy Systems Roskilde Denmark Department of Electrical and Computer Engineering Lisbon Portugal

ISBN: (纸本)9789531842976

With the continuous increase of electric vehicle (EV) adoption, deploying smart charging techniques offer a practical solution to mitigate the impact of grid overloading caused by simultaneous EV charging. At the same time, smart charging can help to stabilize the fluctuations in the production from local renewable energy sources (RES). This article introduces a receding horizon optimization model for the distributed control of EV chargers at charging stations, focusing on maximizing the profit of the charging station, while enhancing the utilization of local PV generation. The proposed model operates in 5-minute intervals, determining the power reference for the EV cluster at the charging station. Results demonstrate that the proposed model effectively lowers electricity cost for charging stations, while ensuring more than 90% energy delivery for charging EVs. Future research will be focused on integrating wind energy and refining the model in controlled lab tests for practical implementation and validation. © 2024 IEEE.

关键词： Charging stations

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A System-theoretic Note on Model-Based Actor-Critic 63

A System-theoretic Note on Model-Based Actor-Critic

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Baroncini, Simone Carnevale, Guido Gharesifard, Bahman Notarstefano, Giuseppe Alma Mater Studiorum - Universita' di Bologna Department of Electrical Electronic and Information Engineering Bologna Italy University of California Department of Electrical and Computer Engineering Los Angeles United States

ISBN: (纸本)9798350316339

In this paper, we provide preliminary results toward the direction of using systems theory tools for the design and analysis of reinforcement learning algorithms. Specifically, we analyze the convergence properties of a model-based scheme with an actor-critic structure. The distinctive feature of our scheme is that the actor and critic updates are equipped with auxiliary variables that allow for the use of a constant step size. Although idealized due to the assumption on access to the underlying Markov Decision Process (MDP), the investigated setting is a starting point toward a genuine (model-free) actorcritic scheme. A key contribution is the interpretation of this algorithmic framework in terms of discrete-time, interconnected dynamical systems. Specifically, by resorting to Singular Perturbations (S P), we reinterpret the whole algorithm as the interconnection of a fast subsystem (auxiliary variables' mechanism), an intermediate one (critic), and a slow one (actor). We separately analyze three auxiliary systems each corresponding to one of the identified subsystems. These preparatory results, combined with SP and LaSalle arguments, allow us to prove that the overall method asymptotically converges to a problem stationary point. Some numerical simulations confirm our theoretical findings. © 2024 IEEE.

关键词： Markov processes

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