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A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

IEEE ACCESS 2021年 9卷 16114-16132页

作者： Aslam, Saad Alam, Fakhrul Hasan, Syed Faraz Rashid, Mohammad A. Massey Univ Sch Food & Adv Technol Dept Mech & Elect Engn Auckland 0632 New Zealand Manukau Inst Technol Sch Profess Engn Auckland 2104 New Zealand

clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.

关键词： clustering algorithm content multicasting D2D enabled networks deep neural networks eNB loading machine learning random forest support vector machine user segregation

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Battlefield Target Grouping by a Hybridization of an Improved Whale Optimization algorithm and Affinity Propagation

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IEEE ACCESS 2021年 9卷 46448-46461页

作者： Duan, Yuxian Liu, Changyun Li, Song Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China Air Force Engn Univ Grad Coll Xian 710051 Peoples R China

Target grouping, which is essentially a data clustering problem, is a research hotspot in the field of battlefield situation assessment. To address an unknown battlefield environment, affinity propagation based on an improved whale optimization algorithm (APBWOA) is proposed from the perspective of clustering. First, we propose a whale optimization algorithm based on a chaotic map and nonlinear inertia weight improvement called CPIW-WOA, which uses an improved circle map to generate initial populations and introduces nonlinear inertia weights to improve its convergence efficiency. The test results on nine benchmark functions show that the CPIW-WOA algorithm has superior performance to existing methods. Second, based on the fact that it fully considers the weights of the attributes in a given sample, the weighted Mahalanobis distance is adopted to replace the Euclidean distance. In addition, the silhouette index is introduced to determine the optimal number of clusters. By iteratively updating through CPIW-WOA to search for the optimal settings, the limitation of manually entering specified parameters can be overcome. Test results on real-world datasets show that the new method is more accurate and effective than other methods;therefore, it can provide effective solutions with respect to battlefield target grouping.

关键词： clustering algorithms Optimization algorithms Statistics Sociology Convergence Whales clustering algorithm affinity propagation whale optimization algorithm target grouping situation assessment

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Trust and grey model based data aggregation algorithm

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2021年第4期35卷 258-266页

作者： Wang, Jun Wang, Ni Li, Li Du, Yansui Shenyang Univ Chem Technol Sch Comp Sci & Technol Shenyang 110142 Peoples R China Party Sch Liaoning Prov Party Comm Shenyang 110004 Liaoning Peoples R China

The reliability of data aggregation is an important problem in wireless sensor networks. Therefore, trust and grey model based data aggregation algorithm (TGDA) is proposed. The algorithm combines technologies such as trust mechanism, data prediction and data aggregation. Before initiating data aggregation, the cluster heads will detect the abnormal nodes whose trust value is lower than a certain value, and add them to the blacklist. According to the high time dependence of the data collected by sensor nodes, the grey model is used to predict the missing data of the abnormal nodes. Finally, the trusted data are aggregated. Simulation results show that compared with other algorithms, the algorithm can effectively improve the security and accuracy of data aggregation under the premise of ensuring the network life cycle.

关键词： trust mechanism data prediction grey model data aggregation time dependence time series abnormal nodes weight mechanism clustering algorithm wireless sensor networks

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Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks

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CURRENT BIOINFORMATICS 2021年第3期16卷 385-394页

作者： Tian, Lei Wang, Shu-Lin Hunan Univ Sch Informat Sci & Engn Changsha Peoples R China

Background: Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers. Objective: We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNA-disease-lncRNA and miRNA-target networks (MSNMDL). Methods: Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly, MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks. Result: MSNMDL is superior to other methods in terms of biological significance and its identified modules might act as module signatures for prognostication of breast cancer. Conclusion: MiRNA sponge networks identified by MSNMDL are biologically significant and are closely associated with breast cancer, which makes MSNMDL a promising way for researchers to study the pathogenesis of breast cancer.

关键词： miRNA sponge networks miRNA sponge modules breast cancer biological enrichment clustering algorithm

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An energy-balanced unequal clustering approach for circular wireless sensor networks

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AD HOC NETWORKS 2022年 132卷

作者： Zhao, Chengkun Wu, Qian Lin, Deyu Zhang, Zhiqiang Zhang, Yujie Kong, Linghe Guan, Yong Liang Nanchang Univ Sch Software Nanchang 330047 Jiangxi Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds England Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Singapore

The network lifetime of the Wireless Sensor Networks (WSNs) is a critical factor for the relevant applications due to the limited energy supply. However, the imbalanced energy consumption of the whole network always leads to the "Hot Spot Problem" and the decline of network lifetime inevitably. To this end, the schemes of energybalanced unequal clustering and energy-efficient cluster head rotation are considered in this paper. To be specific, we conduct detailed theoretical derivation and mathematical calculation based on the concept of gradient to obtain the optimal number of CHs, with the aim of balancing the energy consumption for the whole network topology. In addition, a fuzzy logic-based mechanism for CHs rotation is also proposed to balance the energy distribution among different cluster heads. Subsequently, a novel Energy-Balanced unequal clustering Approach (EBCA) for circular wireless sensor networks is proposed and detailed. Finally, we conduct extensive simulations to verify its performance. The experimental results indicate that EBCA is able to balance the energy consumption of cluster heads in different gradient, reduce the energy consumption and prolong the network lifetime effectively compared with the classic and latest clustering algorithms.

关键词： WSNs Energy balance Network lifetime clustering algorithm

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Hybrid grey wolf sunflower optimisation algorithm for energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement

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IET COMMUNICATIONS 2021年第3期15卷 384-396页

作者： Nagarajan, Lavanya Thangavelu, Shankar VIT Univ Dept Commun Engn Vellore Tamil Nadu India

Wireless sensor networks (WSNs) are expected to find extensive applicability and accelerating deployment in the future. However, the main challenge faced in WSN is its perishing lifetime. The process of clustering a network is a popular mechanism employed for the purpose of extending the lifespan of WSNs and thereby making efficient data transmission. The main aim of a clustering algorithm is to elect an optimal cluster head (CH). The recent research trend suggests meta-heuristic algorithms for the selection of optimal CHs. Meta-heuristic algorithms possess the advantages of being simple, flexible, derivation-free, and avoids local optima. This research proposes a novel hybrid grey wolf optimiser-based sunflower optimisation (HGWSFO) algorithm for optimal CH selection (CHS) under certain factor constraints such as energy spent and separation distance, such that the network lifetime is enhanced. Sunflower optimisation (SFO) is employed for a broader search (exploration) where the variation of the step-size parameter brings the plant closer to the sun in search of global refinement, thus increasing the exploration efficiency. Grey wolf optimisation (GWO) is employed for a narrow search (exploitation), where the parameter coefficient vectors are deliberately required to emphasise exploitation. This balances the exploration-exploitation trade-off, prolongs the network lifetime, increases the energy efficiency, and enhances the performance of the network with respect to overall throughput, residual energy of nodes, dead nodes, alive nodes, network survivability index, and convergence rate. The superior characteristic of the suggested HGWSFO is validated by comparing its performance with various other existing CHS algorithms. The overall performance of the proposed HGWSFO is 28.58%, 31.53%, 48.8%, 49.67%, 54.95%, 70.76%, and 87.10%, better than that of GWO, SFO, particle swarm optimisation (PSO), improved PSO, low-energy adaptive clustering hierarchy (LEACH), LEACH-centra

关键词： optimal CH selection hybrid grey wolf sunflower optimisation algorithm WSNs particle swarm optimisation narrow search telecommunication power management meta-heuristic algorithms sensor placement Optimisation techniques low-energy adaptive clustering hierarchy existing CHS algorithms broader search grey wolf optimisation optimisation Wireless sensor networks energy efficiency novel hybrid grey wolf optimiser-based sunflower optimisation algorithm exploration-exploitation trade-off accelerating deployment pattern clustering network survivability index residual energy perishing lifetime wireless sensor networks clustering algorithm exploration efficiency network lifetime energy-efficient cluster head selection optimal cluster head recent research trend lifetime enhancement

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COGNITIVE COMPUTATION 2021年第2期13卷 447-465页

作者： Garg, Harish Rani, Dimple Deemed Univ Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

Intuitionistic fuzzy set (IFS) is one of the most robust and trustworthy tools for portraying the imprecise information with the help of the membership degrees. Similarity measure, one of the information measures, plays an important role in treating imperfect and ambiguous information to reach the final decision by determining the degree of similarity between the pairs of the numbers. Motivated by these, this paper aims to present a novel distance/ similarity among the IFSs based on the transformation techniques with their characteristics. To explore the study, the given IFSs are transformed into the right-angled triangle over a unit square area, and hence based on the intersection of the triangles, novel distance and similarity measures are proposed. An algorithm to solve the decision-making problems with the proposed similarity measure is developed and implemented to execute their performance over the numerous examples such as pattern recognition and clustering analysis. The reliability of the developed measure is investigated by applying it in clustering and the pattern recognition problems and their results are compared with some prevailing studies. From the investigation, we conclude that several existing measures fail to give classification results under the different instances such as "division by zero problems" or "counter-intuitive cases" while the proposed measure successfully overcomes this drawback.

关键词： Pattern recognition Fuzzy set Right-angled triangle Intuitionistic fuzzy set Similarity measure clustering algorithm

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Evaluation for block-scale solar energy potential of industrial block and optimization of application strategies: A case study of Wuhan, China

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SUSTAINABLE CITIES AND SOCIETY 2021年 72卷 103000-103000页

作者： Xu, Shen Jiang, Haihua Xiong, Feng Zhang, Chen Xie, Mengju Li, Zhixin Sch Architecture & Urban Planning Hust Peoples R China Hubei New Urbanizat Engn & Technol Res Ctr Wuhan Peoples R China

Industrial blocks are an important part of the city which is very suitable for PV application because of its largescale, unified property rights and simple form. In this study, a multi-dimensional evaluation method of solar energy potential suitable for the block-scale is constructed. Then, 48 cases of industrial blocks in a typical city of china were selected as examples. Basing on morphological parameters, all cases were classified into 7 types by clustering algorithm. Afterwards, the total amount and distribution of the solar energy potential were calculated. Finally, the strategies of the photovoltaic system application in blocks were proposed according to types of blocks. It is found that there is significant difference in solar energy potential between different types of industrial blocks. Among them, solar energy potential of single-storey types is the highest up to 90.85 kW h/ m2 center dot year, while solar energy potential of high-rise type blocks is relatively lower which is only 55.64 kW h/ m2 center dot year. In addition, block shape has obvious impact on solar energy potential, among which building density having the greatest influence, as building density increases from 20 % to 55 %, the solar energy potential also increased by 138.74 % from 42.54 kW h/m2 center dot year to 101.56 kW h/m2 center dot year.

关键词： Block-scale solar energy potential Evaluation method Industrial block clustering algorithm Morphological parameter Optimization application strategies

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Optimal allocation of higher education resources based on fuzzy particle swarm optimization (Publication with Expression of Concern)

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INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 2021年第2-Sup期60卷 312-324页

作者： Cui, Jiangning China Univ Min & Technol Xuzhou 221116 Jiangsu Peoples R China Shandong Womens Univ Jinan Peoples R China

The development concept of higher education will have a direct impact on the development direction of higher education, because the concept usually reflects people's understanding of the law of higher education, and the current education concept restricts the scale, quality, structure and efficiency of higher education to a certain extent. Throughout the evolution of the world's higher education development concept, it has generally experienced the overall coordination of the economic growth, the internal and external laws, and the people-centered sustainable development. This paper hopes to provide effective help by joint intelligent algorithms. In this paper, the particle swarm optimization algorithm in intelligent optimization algorithm and the FCM algorithm in cluster analysis are studied in depth, and the two are merged together to propose a fuzzy clustering algorithm based on improved particle swarm optimization algorithm. The main research contents of this thesis include: Firstly, the PSO algorithm is improved, and the random search step levy flights and PSO algorithm are repeatedly called by the iterative mode by introducing the eagle strategy. After improvement, the ESPSO algorithm can achieve a balance between global search and local development, and make the particles have more satisfactory search ability and convergence effect. Then the improved PSO algorithm is integrated into the FCM algorithm to solve the above problems of the FCM clustering algorithm and improve the clustering effect. The experimental results show that the improved espso algorithm and FCM algorithm are suitable for actual data. The simulation results show that the algorithm has better clustering effect than the traditional clustering algorithm.

关键词： Fuzzy particle swarm optimization higher education resource optimization artificial intelligence clustering algorithm

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Effective User Preference clustering in Web Service Applications

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COMPUTER JOURNAL 2020年第11期63卷 1633-1643页

作者： Wang, Yan Zhou, Jian-Tao Li, Xinyuan Song, Xiaoyu Inner Mongolia Univ Inner Mongolia Engn Lab Cloud Comp 235 Daxue West St Saihan Dist 010021 Hohhot Peoples R China Inner Mongolia Univ Coll Comp Sci 235 Daxue West St Saihan Dist 010021 Hohhot Peoples R China Portland State Univ Dept Elect & Comp Engn 1900 SW Fourth AveSuite 160 Portland OR 97207 USA

The research on personalized recommendation of Web services plays an important role in the field of Web services technology applications. Fortunately, not all users have completely different service preferences. Due to the same application scenarios and personal interests, some users have the same preferences for certain types of Web services. This paper explores the problem of user clustering in the service environment, grouping users according to their service preferences. It helps service providers to identify and characterize the preferences of similar users and provide them with customized services. We propose two combination-based clustering algorithms which make full use of the advantages of the K-means algorithm and the affinity propagation algorithm. In addition, a three-stage clustering process is elaborated to improve the accuracy of user clustering. To reduce the time complexity of the algorithms, we create a parallel execution model of the algorithms implemented by a higher-order MapReduce sequence linking technology. Extensive experiments on simulated datasets and real datasets are performed on the comparisons between the proposed algorithms and the other combination-based clustering algorithms. The experimental results substantiate that the proposed algorithms can effectively distinguish user group with different preferences.

关键词： clustering algorithm combination user preference Affinity propagation K-means

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