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A learning automata based edge resource allocation approach for IoT-enabled smart cities

Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and Technology Department of Electrical and Computer Engineering University of Alberta Systems Research Institute Polish Academy of Sciences Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University

Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field (AEF) Thunderstorm Fuzzy C-means (FCM) Attribute

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False Data Injection Attack Detection and Localization Framework in Power Distribution Systems Using a Novel Ensemble of CNNs and Explainable Artificial Intelligence

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IEEE Transactions on Industry Applications 2025年第3期61卷 4801-4811页

作者： Dehbozorgi, Mohammad Reza Rastegar, Mohammad Arani, Mohammadreza F. M. Shiraz University School of Electrical and Computer Engineering Shiraz Iran Toronto Metropolitan University Faculty of Engineering and Architectural Science Toronto Canada

Cyber-physical power systems are vulnerable to cyber-attacks, especially false data injection attacks (FDIAs). FDIAs against distribution system state estimation (DSSE), which alter state estimation (SE) by changing meter readings, have received researchers' attention in recent years. A common defense against FDIAs in the literature is the use of labeled data to train classifiers as FDIA detectors. However, this approach's performance can be limited by the highly imbalanced nature of FDIA datasets. The black box characteristics of the machine learning models can make them hard to trust and adopt in important applications. Hence, we propose an innovative explainable artificial intelligence (XAI)-enhanced ensemble-based detection and localization model that leverages convolutional neural networks (CNNs) and support vector machine (SVM). The ensemble model uses SVM to merge various spatiotemporal CNNs' outputs. Training these CNNs on under-sampled subsets of the majority class and using their ensemble addresses class imbalance. This paper leverages XAI to enhance the interpretability of the detection process and improve localization accuracy. The localization process uses the outputs of an XAI technique, gradient-weighted class activation mapping, to aid the majority-voting-based localization ensemble model. Our model can also detect FDIAs during the distribution feeder's topology changes. Extensive simulations on IEEE 13-bus and IEEE 123-bus feeders prove the proposed under-sampling-based detection approach as an alternative to prevalent over-sampling methods like generative adversarial networks (GANs), offering a novel solution to class imbalance challenges. The paper also provides a comprehensive analysis of the proposed spatiotemporal model's performance, demonstrating its superiority over temporal CNNs. © 1972-2012 IEEE.

关键词： DC distribution systems

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DialTest-EA: An Enhanced Fuzzing Approach With Energy Adjustment for Dialogue Systems via Metamorphic Testing

DialTest-EA: An Enhanced Fuzzing Approach With Energy Adjust...

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作者： Chen, Haibo Chen, Jinfu Wu, Yucheng Cai, Saihua Ahmad, Bilal Huang, Rubing Wang, Shengran Zhang, Chi School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Technology Industrial Cyberspace Jiangsu University Zhenjiang China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China

Deep neural networks (DNNs) possess potent feature learning capability, enabling them to comprehend natural language, which strongly support developing dialogue systems. However, dialogue systems usually perform incorrect behaviours in some corner cases, which may cause misunderstanding or economic loss. To test and debug dialogue systems, a popular fuzzing framework by metamorphic testing with Gini impurity guidance is proposed, namely, DialTest. However, DialTest treats all seeds (the initial test inputs to generate the mutated test inputs) equally during the fuzzing process and does not differentiate seeds, resulting in a certain limitation to its incorrect behaviour detection capability. In this paper, we propose to enhance the DialTest by applying a lightweight energy adjustment strategy called DialTest with Energy Adjustment (DialTest-EA). DialTest-EA employs the ant colony optimization algorithm (ACO) to adjust the mutation energy of each seed adaptively, ensuring that potential seeds have more opportunities to generate subsequent test inputs. To evaluate the effectiveness of the proposed DialTest-EA, we conduct a series of comparisons with the original DialTest and random mutation strategy. The experimental results show that the proposed DialTest-EA outperforms the compared methods both in the intent detection and slot filling tasks. Compared with the original DialTest, the intent detection accuracy of generated test cases by the proposed method is reduced by more than 14%, and the slot filling accuracy is reduced by more than 8%. © 2024 John Wiley & Sons Ltd.

关键词： Ant colony optimization

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Transformer and Long short term memory networks for long sequence time sequence forecasting problem 5

Transformer and Long short term memory networks for long seq...

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5th International Conference on computer Information science and Artificial Intelligence, CISAI 2022

作者： Fang, Wei School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9781510662520

The long sequence time-sequence forecasting problem attracts a lot of organizations. Many prediction application scenes are about long sequence time-sequence forecasting problems. Under such circumstances, many researchers have tried to solve these problems by employing some models that have proved efficient in the Natural Language Processing field, like long short term memory networks and Transformers, etc. And there are a lot of improvements based on the primary recurrent neural network, and Transformer. Recently, a model called informer which is made for the LSTF was proposed. This model claimed that it improves prediction performance on the long sequence time-series forecasting problem. But in the later experiments, more and more researchers found that informers still can not handle all the long sequence time-sequence forecasting problems. This paper is going to look at how datasets effect the performance of different models. The experiment is carried out on the Bitcoin dataset with four features and one output. The result shows that the Informer (transformer-like model) cannot always perform well so that sometimes choosing models with simple architecture may gain better results. © 2023 SPIE.

关键词： Forecasting

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A Deep Learning Approach for Lunar Impact Crater Detection Based on YOLO v7 and CBAM Attention Mechanism 8

A Deep Learning Approach for Lunar Impact Crater Detection B...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zhu, Jionghao Liang, Jiarui Tian, Xiaolin Yan, Pengcheng Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering Macau China

ISBN: (纸本)9798350302455

Impact craters are the most common geomorphic unit on the lunar surface, and there are a large number of impact craters of different sizes and morphologies on the lunar surface. Lunar impact craters are a key basis for lunar geological studies, and their study allows for the exploration of the Moon and other planets. Therefore, this paper builds a convolutional neural network YOLO V7-CBAM based on YOLO V7 and the attention mechanism for identifying lunar impact craters. Based on the full-moon CCD images and DEM images provided by NASA, a comparison test was conducted and the precision of YOLO V7-CBAM based on both images was higher than that of YOLO V7, from 72.31% to 74.65% based on CCD images;and from 70.60% to 71.70% based on DEM images. © 2023 IEEE.

关键词： Object detection

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Dictionary Attention-Weighted Cross-Domain Contrastive Learning for Remote Sensing Image Change Detection

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Data Intelligence 2024年第4期6卷 893-908页

作者： Tian Wei Youfa Liu Rui Zhao School of Computer Science Wuhan UniversityWuhan430072China Faculty of Electrical Engineering and Computer Science Ningbo University315211China

Significant progress has been made in remote sensing image change detection due to the rapid development of Deep Learning techniques. Convolutional neural networks(CNNs) have become foundational models in this field. Previous works on remote sensing image change detection has utilized domain adaptation methods, achieving promising predictive performance. However, the transferable knowledge between source and target domain has not been fully exploited. In this paper, we propose a novel cross-domain contrastive learning approach for remote sensing image change detection, which correlates source and target domain using contrastive principles. Specifically, we introduce a transferable cross-domain Dictionary Learning scheme where a shared dictionary between the source and target domains generates sparse representations. Based on these representations, we compute attention weights and propose an attention-weighted contrastive loss to enhance knowledge transfer between source and target domains. Experiments demonstrate the effectiveness of the proposed methods on public remote sensing image change detection datasets.

关键词： Change detection Domain adaptation Contrastive learning Dictionary Learning Attention mechanism

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A Dynamic and Collaborative Spectrum Sharing Strategy Based on Multi-Agent DRL in Satellite-Terrestrial Converged Networks

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7969-7984页

作者： Tang, Chao Chen, Yueyun Chen, Guang Du, Liping Liu, Huan School of Computer and Communication Engineering Beijing100083 China University of Science and Technology Beijing Shunde Innovation School Foshan528000 China

Satellite-terrestrial spectrum sharing is an effective method to alleviate the scarcity of spectrum resources. However, with the dense deployment of terrestrial systems, the satellite downlink beam covers multiple terrestrial base stations (BSs) and interacts with terrestrial downlinks during spectrum sharing. The spectrum sharing users encounter co-channel interference of inter-system, inter-cell and intra-cell, posing significant challenges for downlink spectrum sharing in converged system. In this paper, we propose a cooperative multichannel spectrum sharing framework where the multiple terrestrial BSs downlinks share multichannel spectrum resources of low earth orbit (LEO) satellite while guaranteeing the rate requirements of all shared users and aiming at improving spectrum efficiency of converged system. We formulate the cooperative satellite-terrestrial spectrum sharing strategy as a global spectrum efficiency maximization problem for the converged system, achieved through multichannel assignment and power allocation optimization for terrestrial shared users. To solve the non-convex optimization problem, we provide a dynamic and collaborative spectrum sharing (DCSS) algorithm based on multi-agent deep reinforcement learning (MADRL), which includes DDQN-based multichannel assignment and DDPG-based power allocation. DCSS transforms the problem to a decentralized partially observable Markov decision processes (Dec-POMDP) and adopts centralized-training-distributed-execution (CTDE) framework to address the challenge of acquiring global information in real-time. Specifically, each terrestrial BS acts as an agent with a globally trained two-layer joint deep neural network model that can adaptively allocate the multichannel and power based on observed system local information. Extensive simulation results demonstrate that DCSS algorithm outperforms the existing typical algorithms. © 1967-2012 IEEE.

关键词： Convex optimization

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Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

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China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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