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Virtual-Mobile-Agent-Assisted Boundary Tracking for Continuous Objects in Underwater Acoustic Sensor Networks

IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 9171-9183页

作者： Liu, Li Zheng, Zhaohui Zhu, Shengchao Chan, Sammy Wu, Changmao Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214126 Peoples R China Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Chinese Acad Sci Inst Software Lab Sci & Technol Integrated Informat Syst Beijing 100045 Peoples R China

Aquatic environments confront mounting threats from diverse sources, among which the persistent migration of continuous objects (e.g., chemical contaminants) is a primary concern. While advances have been made in tracking these entities using underwater acoustic sensor networks (UASNs), full-scale monitoring is challenged by unpredictable sensor deployments. Though Autonomous underwater vehicles show promise, their prohibitive costs and operational complexities limit their broad adoption. To tackle these issues, this article introduces a novel search strategy named virtual mobile agent-assisted continuous object tracking (VMA-COT). Drawing inspiration from binary tree structures, VMA-COT refines the search from the entire network to targeted hierarchical boundary mapping cells. Each cell encompasses a section of the object's boundary. A designated node within each cell evaluates information entropy at specified locations using a meticulously designed search sequence. By utilizing a feedback mechanism, grounded in the information entropy and the search sequence, VMA-COT progressively pinpoints the boundary. This methodology can be likened to a central node dispatching mobile agents in each cell. These agents, limited by a specific step range, adjust their trajectories for precise boundary delineation. Empirical tests and simulations demonstrate the effectiveness of VMA-COT, highlighting its efficiency, accuracy, and boundary node utilization.

关键词： Mobile agents Search problems Trajectory Mobile nodes Target tracking Oils clustering algorithms Binary tree boundary tracking continuous objects mobile agent underwater acoustic sensor networks (UASNs)

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An Unsupervised Data-Mining and Generative-Based Multiple Missing Data Imputation Network for Energy Dataset

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第11期20卷 13429-13440页

作者： Kim, Hyung Joon Kim, Mun Kyeom Korea Inst Energy Res Energy Efficiency Div Daejeon 34129 South Korea Chung Ang Univ Sch Energy Syst Engn Seoul 06974 South Korea

Missing values are ubiquitous in energy datasets, and therefore, generative-based imputation networks have attracted extensive research interest because of their strong imputation performance. However, these networks have limited accuracy when explicit class labels are unavailable and single imputation cannot fully address the uncertainty surrounding the true values of the imputed variables. This article proposes an unsupervised data-mining-based conditional generative adversarial multiple imputation network that exploits implicit categorical information and multiple imputation to improve the robustness of the final imputation results. First, a pretraining algorithm is added to develop an auxiliary classifier combined with the corresponding implicit class labels. Then, a "fuzzy-clustering-based ordering points to identify the clustering structure" algorithm is proposed to learn the implicit categorical information. Thereafter, multiple imputation is applied to an original energy dataset to improve the reliability of the final imputation results. Experimental results demonstrate the superiority of the proposed network compared to other networks.

关键词： Imputation Generators Tensors Optics Noise Decision making clustering algorithms clustering data mining energy dataset generative adversarial network imputation missing data

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Higher Order Fuzzy Membership in Motif Modularity Optimization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 7143-7156页

作者： Xiao, Jing Wei, Ya-Wei Cao, Jing Xu, Xiao-Ke Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Beijing Normal Univ Sch Journalism & Commun Beijing 100875 Peoples R China

Higher order community detection (HCD) reveals both mesoscale structures and functional characteristics of real-world networks. Although many methods have been developed from diverse perspectives, to our knowledge, none can provide fine-grained higher order fuzzy community information. This study introduces a novel concept of higher order fuzzy memberships that quantify the membership grades of motifs to crisp higher order communities, thereby revealing partial community affiliations. Furthermore, we utilize higher order fuzzy memberships to enhance HCD via a general framework called fuzzy memberships-assisted motif-based evolutionary modularity. On the one hand, a fuzzy membership-based neighbor community modification strategy is designed to correct misassigned bridge nodes, thereby improving partition quality. On the other hand, a fuzzy membership-based local community merging strategy is proposed to combine excessively fragmented communities, enhancing local search ability. Experimental results indicate that the proposed framework outperforms state-of-the-art methods in both synthetic and real-world datasets, particularly in networks with ambiguous and complex structures.

关键词： Optimization Topology Network motifs Bridges Accuracy Partitioning algorithms Image edge detection Fuzzy systems clustering algorithms Space exploration Fuzzy membership higher order community detection (HCD) modularity optimization network motif

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Community-Based Task Assignment Method in Mobile Crowd Sensing

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IEEE ACCESS 2024年 12卷 84387-84400页

作者： Long, Hao Hao, Jiawei Zhang, Shukui Zhang, Yang Zhang, Li Xuzhou Coll Ind Technol Sch Informat Engn Xuzhou 221140 Jiangsu Peoples R China Global Inst Software Technol Suzhou 215163 Jiangsu Peoples R China Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China

With the rapid development of mobile networks and widespread use of mobile devices, there is an increasing focus on assigning location-based tasks to mobile users in the context of Mobile Crowd Sensing (MCS). One of the primary challenges in MCS is task assignment, i.e., distributing tasks to suitable users for completion. However, existing work often assumes static offline scenarios where the spatiotemporal information of all users and tasks is pre-determined and known. Neglecting the dynamic spatiotemporal distribution of users and tasks can lead to suboptimal assignment results. In this study, we investigate a novel task assignment problem called Community Task Assignment (CTA). The objective is to enhance the effectiveness and precision of task distribution by considering the sociality of current users and distributing location-based tasks through communities. Initially, we partition users into different communities by abstracting and identifying behavior patterns through the computation of minimum spanning trees, connectivity parameters, and community cohesion. Subsequently, we calculate the match between perception tasks and community behavior pattern features, and task distribution is carried out by the central nodes of the communities based on this match. Experimental validation first confirms the effectiveness of the community partitioning algorithm. Compared to existing algorithms, the proposed method more accurately detects community structures with similar behavioral features in the network. Furthermore, a comparison with existing task assignment algorithms verifies the superiority of the proposed method in terms of average task completion time, task matching rate, and overall utility of task assignments.

关键词： Task analysis Sensors Resource management Robot sensing systems Partitioning algorithms clustering algorithms Heuristic algorithms Crowdsourcing Behavioral sciences Mobile crowd sensing task assignment community sociality behavioral features

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DSMN: An Improved Recommendation Model for Capturing the Multiplicity and Dynamics of Consumer Interests

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1236-1245页

作者： Lv, Pengtao Guan, Zhenhan Zhang, Qinghui Gu, Jiale Zhang, Shuai Lv, Lei Zhang, Mengya Li, Lei Zhao, Chengyang Henan Univ Technol Key Lab Grain Informat Proc & Control Henan Key Lab Grain Photoelect Detect & Control Minist Educ Zhengzhou 450052 Peoples R China Henan Univ Technol Henan Grain Big Data Anal & Applicat Engn Res Ctr Zhengzhou 450052 Peoples R China Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450052 Peoples R China Suzhou Ind Pk Inst Serv Outsourcing Sch Informat Engn Suzhou 215123 Peoples R China Qi Xin Technol Grp Inc Res & Dev Dept Beijing 100044 Peoples R China

Electronic shopping has become an important way of shopping in our daily life. A good recommendation system can greatly improve the consumer shopping experience through alleviating consumer and product information overload problem. In the real scenario, each consumer has a wide variety of interests, and consumer's interests are constantly changing. Many recent models usually give an overall embedding for consumer behavior sequence or only capture features of customers and characteristics of products from single perspective. However, these methods can hardly model consumer's multiple interests and evolving of interests completely. Thus, in this study, we consider both the diversity and dynamics of consumer interests, and model them in a unified model. In light of this, we propose a novel model named Deep Self-Attention Mulit-Interest Networks (DSMN), which captures latent multiple interests by using capsule networks. It can perceive evolving of interests from consumer behavior sequences by using attention mechanism. We evaluate our model on three real-world datasets. In the experiments, our DSMN is 4.934% higher than the average of competitors in terms of AUC. Compared to our original silver medal method on the Kaggle competition, our DSMN is 18.978% higher in terms of MAP@12.

关键词： Heuristic algorithms Routing Recommender systems Silver Computational modeling Collaborative filtering clustering algorithms Massive consumer data consumer electronics recommendation system information overload

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Energy Efficient Hybrid Evolutionary Algorithm for Internet of Everything (IoE)-Enabled 6G

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IEEE ACCESS 2024年 12卷 63839-63852页

作者： Singh, Shailendra Pratap Kumar, Naween Singh, Akansha Kant Singh, Krishna Askar, S. S. Abouhawwash, Mohamed Bennett Univ Dept SCSET Greater Noida 201310 Uttar Pradesh India Delhi Tech Campus Greater Noida 201306 India King Saud Univ Coll Sci Dept Stat & Operat Res Riyadh 11451 Saudi Arabia Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

The advancement of Internet of Everything (IoE) propels the fast growth of next-generation, such as 6G networks, leading to a new era of coverage, connectivity, and technological innovation, which calls for novel approaches to address rising energy consumption and maximize resource use. The proposed article presents a robust hybrid algorithm that combines leader-based optimization and Adaptive Differential Evolution (DE) in the framework of the Energy Efficient Hybrid Evolutionary Algorithm (EEHEA), which is specifically designed for the complex environment of IoE-enabled 6G networks. The scheme EEHEA combines the efficacy of leader-based optimization (LBO) for an effective decision-making process and adaptive differential evolutionary optimization (ADE)'s dynamic network-parameters adaptation, enhanced convergence, and global searching ability, persistently fine-tuning optimization strategies based on the dynamics of the network. Combining these components into the scheme EEHEA allows it to balance local exploitation and global exploration effectively. This implies better resource allocation and improved energy efficiency in ecosystems with IoE-driven 6G (IoE-6G). The outcomes report that the scheme EEHEA can address the rising energy consumption issues and enhance the efficiency of IoE-6G. Based on simulation experiments, the proposed scheme EEHEA can demonstrate faster convergence times, higher accuracy, and superior flexibility concerning changing network conditions. Its capability to handle energy-related challenges and navigate complex network environments with resilience shows the ability to enhance the performance of IoE-6G. The EEHEA scheme reports its efficacy over state-of-the-art schemes regarding localization, latency, coverage, and energy expenditure performance metrics.

关键词： 6G mobile communication Optimization Energy efficiency clustering algorithms Resource management Heuristic algorithms Convergence Leader-based optimization adaptive differential evolutionary algorithm resource allocation energy efficiency dynamic parameters adaptation IoE-driven 6G

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KM-Mask RCNN: A Lightweight Instance Segmentation Algorithm for Strawberries With Multiple Growth Cycles

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IEEE ACCESS 2024年 12卷 169291-169309页

作者： Cong, Peichao Xu, Yutao Li, Tianheng Li, Shanda Feng, Hao Zhang, Xin Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Peoples R China

Accurate and efficient detection of multi-growth-cycle strawberry fruits can improve automated harvesting. However, the small size and unbalanced distribution of strawberry fruits make accurate identification of multi-growth-cycle strawberries difficult using the existing detection models. Herein, local enhancement technology is adopted for preprocessing when compiling the dataset to ensure the balance of the number of samples in each category to solve the above problems. Second, a new instance segmentation algorithm called KM-Mask RCNN is developed, which optimally adjusts the size of the anchor frame and the anchor ratio based on the K-Means clustering algorithm to improve the recognition accuracy of the algorithm on small targets and uses MobileNet V3 to replace the Resnet50 structure in the Mask RCNN backbone network to reduce the complexity of the algorithm and realize lightweight operation. Finally, the experimental results reveal five strawberry growth stages in the homemade dataset(based on StrawDI_Db1 database): 'Green ripe stage', 'White ripe stage', 'Turning stage', 'Mature', and 'Deformed', for which the KM-Mask RCNN yields mAPs of 91.19%, 88.09%, 93.70%, 93.19%, and 87.13%, respectively. The P, R, and F1-score values of this algorithm are 93.9%, 94.2%, and 94.05%, respectively. Additionally, the number of parameters, FLOPs, and fps of this algorithm are 27M, 12G, and 22.32, respectively, satisfying the real-time requirements for strawberry detection. The findings provide important theoretical support for the automated harvesting of strawberries with multiple growth cycles.

关键词： Feature extraction Accuracy Residual neural networks Robots clustering algorithms Classification algorithms Transformers Prediction algorithms Instance segmentation Manuals Convolutional neural networks K-means clustering algorithm lightweighting mask RCNN strawberry

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Enhancing Data Collection in Heterogenous Wireless Sensor Networks: A Novel Tree-Structured Genetic Algorithm Approach

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IEEE ACCESS 2024年 12卷 173020-173036页

作者： Alasadi, Hasan Salah Farzinvash, Leili Feizi-Derakhshi, Mohammad-Reza Mortazavi, Seyed Amir Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Heterogenous Wireless Sensor Networks (HWSNs) are cost-effective solutions to monitor the environment. These networks face limited battery as a critical challenge. Traditionally, energy-efficient routing algorithms have been the primary solution. While Artificial Intelligence (AI)-based approaches, particularly metaheuristics, show promise, current methods suffer from a key limitation: problem formulation hinders their success. Existing approaches rely on the array data structure for tree construction, leading to inefficiency. This paper proposes a novel Genetic Algorithm (GA)-based approach for data collection in HWSNs. It introduces a simpler and more intuitive tree data structure specifically designed by GA, leading to improved performance. Additionally, the paper proposes population initialization schemes and GA operators customized for the problem. Finally, an advanced cost function is employed that distributes workloads based on hop count to the Base Station (BS), further optimizing energy consumption. Extensive simulations demonstrate the superiority of the proposed algorithm in data gathering. It increases the number of received bits to the BS by an average of 6.6%, while also almost significantly improves the network lifetime and the number of alive nodes.

关键词： clustering algorithms Wireless sensor networks Genetic algorithms Routing Metaheuristics Energy efficiency Energy consumption Data communication Cost function Monitoring heterogeneity clustering tree-based routing customized genetic algorithm tree-based chromosome

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Big Coding Data: Analysis, Insights, and Applications

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IEEE ACCESS 2024年 12卷 196010-196026页

作者： Rahman, Md. Mostafizer Shirafuji, Atsushi Watanobe, Yutaka Tulane Univ Dept Comp Sci New Orleans LA 70118 USA Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan

In recent years, there has been a notable surge in the generation of coding data on various platforms, including programming competitions and educational institutions. These platforms serve as repositories for substantial volumes of real-world code, problem descriptions, test cases, and activity logs. Despite this wealth of coding data, its potential for advancing software engineering, programming, and research remains largely unexplored. To the best of our knowledge, coding data has been partially explored and utilized in previous research projects such as CodeNet and AlphaCode, but has not been fully considered. There exists a compelling need to explore coding data in more depth to explore its potential for programming and research endeavors. Recognizing this gap, our study undertakes a comprehensive analysis of extensive coding data obtained from a programming learning platform. The Aizu Online Judge (AOJ) serves as our chosen programming platform, providing access to coding data and its associated features. We collected approximately 9 million code evaluation logs, code files, as well as a substantial number of problem descriptions and input/output test cases for thorough analysis and experimentation. The goal of this study is to explore the full potential of the coding data for latent knowledge extraction, programming, and research. We conducted experiments with code evaluation logs, code files, problem descriptions, and test cases to demonstrate the suitability of coding data for various research and applications. Additionally, this study introduces a comprehensive array of features and application programming interfaces (APIs) associated with the AOJ platform. These resources facilitate seamless access and use of coding data, making them a valuable tool for professional and educational initiatives as well as research endeavors.

关键词： Codes Programming profession Big Data Education Data mining Source coding clustering algorithms Classification algorithms Relational databases Data analysis Big coding data data analytics source code evaluation logs software engineering programming education information visualization AI for code coding datasets

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A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 3740-3753页

作者： Xu, Zhaozhao Shen, Derong Kou, Yue Nie, Tiezheng Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

Data imbalance is a common phenomenon in machine learning. In the imbalanced data classification, minority samples are far less than majority samples, which makes it difficult for minority to be effectively learned by classifiers A synthetic minority oversampling technique (SMOTE) improves the sensitivity of classifiers to minority by synthesizing minority samples without repetition. However, the process of synthesizing new samples in the SMOTE algorithm may lead to problems such as "noisy samples" and "boundary samples." Based on the above description, we propose a synthetic minority oversampling technique based on Gaussian mixture model filtering (GMF-SMOTE). GMF-SMOTE uses the expected maximum algorithm based on the Gaussian mixture model to group the imbalanced data. Then, the expected maximum filtering algorithm is used to filter out the "noisy samples" and "boundary samples" in the subclasses after grouping. Finally, to synthesize majority and minority samples, we design two dynamic oversampling ratios. Experimental results show that the GMF-SMOTE performs better than the traditional oversampling algorithms on 20 UCI datasets. The population averages of sensitivity and specificity indexes of random forest (RF) on the UCI datasets synthesized by GMF-SMOTE are 97.49% and 97.02%, respectively. In addition, we also record the G-mean and MCC indexes of the RF, which are 97.32% and 94.80%, respectively, significantly better than the traditional oversampling algorithms. More importantly, the two statistical tests show that GMF-SMOTE is significantly better than the traditional oversampling algorithms.

关键词： Noise measurement clustering algorithms Heuristic algorithms Gaussian mixture model Sensitivity Interpolation Radio frequency Expectation maximum (EM) Gaussian mixture model (GMM) imbalanced data oversampling synthetic minority oversampling technique (SMOTE)

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