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Dynamically Transient Social Community Detection for Mobile Social Networks

IEEE INTERNET OF THINGS JOURNAL 2021年第3期8卷 1282-1293页

作者： Bi, Xiaoyan Qiu, Tie Qu, Wenyu Zhao, Laiping Zhou, Xiaobo Wu, Dapeng Oliver Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

In mobile social networks (MSNs), mobile users communicate with each other via mobile devices, such as smartphones and tablets, transmitting data through intermittent connections. Mobile users have high mobility, which creates higher requirements for efficient data forwarding in MSNs. Therefore, forwarding data efficiently and quickly becomes a key problem. To tackle this problem, this article proposes a routing method based on a dynamic transient social community (DTSC) to optimize the routing and forwarding performance in MSNs. In this process, combined with the duration of intensive contact between nodes and the social relations of mobile users, the similarity of each pair of contact nodes is calculated, and community detection is carried out. Then, by analyzing the emergence mode of the DTSC, the measurement value and corresponding routing algorithm of the community's ability to deliver messages are designed. Our algorithm fully considers the duration of the node's direct encounter and the social connection of the indirect contact to ensure that the node can deliver successfully in a short time. The experimental results show that the DTSC has an excellent performance in data forwarding.

关键词： Routing Transient analysis Social networking (online) Mobile nodes Prediction algorithms Epidemics clustering algorithms Data forwarding dynamically transient social community (DTSC) mobile social network (MSN)

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Reinforcement Learning-Based Energy-Efficient Data Access for Airborne Users in Civil Aircrafts-Enabled SAGIN

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2021年第2期5卷 934-949页

作者： Chen, Qian Meng, Weixiao Han, Shuai Li, Cheng Chen, Hsiao-Hwa Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Mem Univ Newfoundland St John NF A1B 3X5 Canada Natl Cheng Kung Univ Dept Engn Sci Tainan 70101 Taiwan

Airborne users are always dreaming of enjoying a good Internet access experience while in the air. However, due to long propagation delay and limited network coverage, the existing data communication methods utilized in space and ground communications not only fail to ensure the quality-of-service (QoS) of airborne users, but also incur significant energy consumption to process content requests. In this paper, we introduce the aeronautical ad hoc network (AANET) as a new method of network access and design an energy-efficient data access scheme in civil aircrafts-enabled space-air-ground integrated networks (CAE-SAGIN). In order to minimize the energy consumption, we propose a service selection scheme based on reinforcement learning and formulate a joint optimization problem of resource allocation and request distribution. Leveraged by the Lyapunov optimization method, the optimization problem can be solved by the proposed joint optimization algorithm. Extensive simulations are conducted to confirm the stability of the CAE-SAGIN, and demonstrate that the proposed data access scheme can effectively reduce both the energy consumption and the processing delay. Moreover, the advantages of using AANET are becoming more obvious when higher data rate is required.

关键词： Internet Satellites Resource management Task analysis Energy consumption Reinforcement learning clustering algorithms Space-air-ground integrated networks energy-efficiency clustering task offloading resource allocation

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An Evaluation Model of the Stuck Risks Based on Remote Sensor Network and Fuzzy Logic

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IEEE SENSORS JOURNAL 2021年第20期21卷 23658-23666页

作者： Liang, Haibo Chen, Haifeng Guo, Jinhong Jiang, Yingjun Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China CNPC Chuanqing Drilling Engn Technol Res Inst Deyang 618300 Sichuan Peoples R China

Remote monitoring of drilling safety risks is an important part of oil drilling engineering. Accurate prediction of drilling risks can provide a reliable guarantee for safe drilling operations. Although the conventional collapse stuck risks monitoring technology is based on sensor detection, the risk assessment method is still based on the qualitative analysis of manual experience and lacks theoretical support. In order to solve this problem, based on the engineering parameters of the remote sensor network, this paper analyzes the characteristics of the collapse stuck, and establishes the collapse stuck risks evaluation model of the fuzzy dynamic cluster algorithm to optimize Fuzzy C-means clustering algorithm. The result of fuzzy dynamic cluster is tested by F statistics, and the result is used as the initial condition of the Fuzzy C-means clustering algorithm, and the method of iterative optimization is used to calculate the final classification result. The concept of nearness degree is introduced to calculate the nearness degree of prior unknown data to cluster center vector. According to the principle of maximum nearness degree, the risk level of prior unknown data is classified, so as to realize the risk prediction and evaluation of the collapse stuck accident. It provides theoretical support for remote monitoring and risk assessment of collapse stuck of oil drilling engineering.

关键词： Heuristic algorithms clustering algorithms Monitoring Drilling machines Safety Sensors Oils Remote monitoring F-FCM collapse stuck engineering parameters nearness degree

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Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

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TRANSPORTATION RESEARCH RECORD 2021年第10期2675卷 723-733页

作者： Azizi, Leila Hadi, Mohammed Cent Transportat Planning Staff Boston MA 02116 USA Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

关键词： clustering algorithms

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Label Distribution Learning with Label Correlations on Local Samples

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第4期33卷 1619-1631页

作者： Jia, Xiuyi Li, Zechao Zheng, Xiang Li, Weiwei Huang, Sheng-Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210014 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

Label distribution learning (LDL) is proposed for solving the label ambiguity problem in recent years, which can be seen as an extension of multi-label learning. To improve the performance of label distribution learning, some existing algorithms exploit label correlations in a global manner that assumes the label correlations are shared by all instances. However, the instances in different groups may share different label correlations, and few label correlations are globally applicable in real-world tasks. In this paper, two novel label distribution learning algorithms are proposed by exploiting label correlations on local samples, which are called GD-LDL-SCL and Adam-LDL-SCL, respectively. To utilize the label correlations on local samples, the influence of local samples is encoded, and a local correlation vector is designed as the additional features for each instance, which is based on the different clustered local samples. Then, the label distribution for an unseen instance can be predicted by exploiting the original features and the additional features simultaneously. Extensive experiments on some real-world data sets validate that our proposed methods can address the label distribution problems effectively and outperform state-of-the-art methods.

关键词： Correlation clustering algorithms Silicon compounds Optimization Computer science Visualization Trees insulation Multi-label learning label distribution learning label correlations

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Reconstruction Method of Ocean Front Model Based on Fuzzy Cluster Analysis of Sound Speed Profile

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IEEE ACCESS 2021年 9卷 112714-112725页

作者： Liu, Yuyao Chen, Wen Chen, Wei Chen, Yu Ma, Lina Meng, Zhou Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410073 Peoples R China

Ocean fronts are mostly determined according to the horizontal temperature gradient, and various selection criteria may lead to different frontal zones. This study proposes a frontal zone delineation method based on sound speed profile. In this paper, the sound speed profile of the Kuroshio intrusion front in the Luzon Strait is classified via fuzzy cluster analysis based on transitive closure and fuzzy C-means algorithm. At the same time, considering that ocean fronts affect sound propagation, we verify the effectiveness of the two algorithms in terms of sound transmission loss. The results show that in different depth ranges, the effectiveness of the two algorithms differs in identifying the frontal zone. At depth below 300 m, the sound speed profile changes drastically, and fuzzy cluster analysis based on the transitive closure is more effective. At 300-700 m depth where the sound speed profile tends to be stable, the fuzzy C-means algorithm is more effective. At 700-1000 m depth near the SOFAR axis (the depth of the minimum sound speed), fuzzy cluster analysis based on the transitive closure is more effective. Finally, based on the sound speed profile, we combine both algorithms for the first time to obtain a three-layer geometric optimized structure of the Kuroshio intrusion front in the Luzon Strait. The classification of sound speed profiles via the two fuzzy cluster algorithms provides a new way to judge the frontal zone in different depth ranges and reconstruct geometric models of ocean fronts.

关键词： Oceans Ocean temperature clustering algorithms Classification algorithms Matrix converters Salinity (geophysical) Water Fuzzy cluster analysis ocean front sound speed profile transmission loss

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Optimized Energy - Efficient Path Planning Strategy in WSN With Multiple Mobile Sinks

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IEEE ACCESS 2021年 9卷 82833-82847页

作者： Al-Kaseem, Bilal R. Taha, Zahraa K. Abdulmajeed, Sarah W. Al-Raweshidy, Hamed S. Al Iraqia Univ Coll Engn Dept Elect Engn Baghdad 7366 Iraq Al Iraqia Univ Coll Engn Dept Network Engn Baghdad 7366 Iraq Al Iraqia Univ Coll Engn Dept Comp Engn Baghdad 7366 Iraq Brunel Univ London Coll Engn Design & Phys Sci Wireless Networks & Commun Ctr WNCC Dept Elect & Comp Engn London UB8 3PH England

Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors' data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks' trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks' sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

关键词： Wireless sensor networks Internet of Things Wireless communication Sensors clustering algorithms Batteries Routing protocols Ant colony optimization clustering protocol genetic algorithm IoT M2M multiple mobile sinks optimized path routing protocol simulated annealing stable election algorithm WSN

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Random Forest Model for Trip End Identification Using Cellular Phone and Points of Interest Data

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TRANSPORTATION RESEARCH RECORD 2021年第7期2675卷 454-466页

作者： Yang, Fei Wang, Yanchen Jin, Peter J. Li, Dingbang Yao, Zhenxing Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Sichuan Peoples R China Rutgers State Univ Dept Civil & Environm Engn Piscataway NJ USA Changan Univ Coll Transportat Engn Xian Peoples R China

Cellular phone data has been proven to be valuable in the analysis of residents' travel patterns. Existing studies mostly identify the trip ends through rule-based or clustering algorithms. These methods largely depend on subjective experience and users' communication behaviors. Moreover, limited by privacy policy, the accuracy of these methods is difficult to assess. In this paper, points of interest data is applied to supplement cellular phone data's missing information generated by users' behaviors. Specifically, a random forest model for trip end identification is proposed using multi-dimensional attributes. A field data acquisition test is designed and conducted with communication operators to implement synchronized cellular phone data and real trip information collection. The proposed identification approach is empirically evaluated with real trip information. Results show that the overall trip end detection precision and recall reach 95.2% and 88.7% with an average distance error of 269 m, and the time errors of the trip ends are less than 10 min. Compared with the rule-based approach, clustering algorithm, naive Bayes method, and support vector machine, the proposed method has better performance in accuracy and consistency.

关键词： clustering algorithms

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A Permutation-Based Model for Analysis of Resource Allocation Overheads in Vehicular Ad Hoc Networks

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IEEE ACCESS 2021年 9卷 12282-12290页

作者： Zhang, Xin Shen, Zhongyi Yang, Dacheng Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

This paper considers the theoretical limits of resource allocation overheads in vehicular ad hoc networks (VANETs), where the resource allocation procedure may consume extensive system resources. A permutation-based model is proposed to analyze the overhead limits where the unordered resource allocation patterns are considered as a permutation process. The minimum bits to represent the resource allocation information can be derived based on the permutation-based model. The bipartite graph is applied to describe the resource reuse in the respective domains for practical scenarios, and the dynamicity is taken into consideration. The proposed permutation-based model is applied to analyze the resource allocation overheads of two typical resource allocation schemes in VANETs: time-division multiple access (TDMA) and cluster-based algorithms. The proposed methodology provides new insights in a tradeoff analysis between overheads and system efficiency when analyzing the capacity of the VANETs.

关键词： Resource management Vehicular ad hoc networks Analytical models clustering algorithms Protocols Topology Time division multiple access Overhead resource allocation vehicular ad hoc networks theoretical limit

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Feature Selection with Data Field

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Chinese Journal of Electronics 2025年第4期23卷 661-665页

作者： Hanning Yuan Shuliang Wang Ying Li Jinghua Fan School of Software Beijing Institute of Technology Beijing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China International School of Software Wuhan University Wuhan China

A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.

关键词： Electric potential High dimensional data Noise Education clustering algorithms Medical services Feature extraction Entropy Noise measurement

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