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Biobjective Robust Incentive Mechanism Design for Mobile Crowdsensing

IEEE INTERNET OF THINGS JOURNAL 2021年第19期8卷 14971-14984页

作者： Xu, Jia Zhou, Yuanhang Ding, Yuqing Yang, Dejun Xu, Lijie Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Peoples R China Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA

In recent years, mobile crowdsensing has become an effective method for large-scale data collection. Incentive mechanism is fundamentally important for mobile crowdsensing systems. Many mobile crowdsensing systems expect to optimize multiple objectives simultaneously. Most of the existing works transform the multiobjective problem into a single objective problem through constraints or scalarization method. However, due to the uncertain importance (weights) of objectives and the instable quality of crowdsensed data, such transformation is usually unrealizable. In this article, we aim to optimize the worst performance of two objective functions in mobile crowdsensing in order to improve the system robustness. We model an auction-based biobjective robust mobile crowdsensing system, and design two independent objective functions to maximize the expected profit and coverage, respectively. We formulate the robust user selection (RUS) problem, and design an incentive mechanism, which utilizes the combination of binary search and greedy algorithm, to solve the RUS problem. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the designed incentive mechanisms satisfy desirable properties of computational efficiency, individual rationality, truthfulness, and constant approximation to the tightened RUS problem. Moreover, the proposed incentive mechanism can be easily extended to multiobjective robust mobile crowdsensing systems, and all desirable properties still hold. The simulation results reveal that our incentive mechanism achieves 11% improvement of the platform's utility, compared with the greedy algorithm for biobjective mobile crowdsensing systems on average.

关键词： Crowdsensing Sensors Task analysis Robustness linear programming Optimization Smart phones Biobjective problem incentive mechanism mobile crowdsensing robustness

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On finding maximum disjoint paths with different colors: Computational complexity and practical LP-based algorithms

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THEORETICAL COMPUTER SCIENCE 2021年 886卷 157-168页

作者： Deng, Yunyun Guo, Longkun Liao, Kewen Chen, Yi Officers Coll Chinese Peoples Armed Police Force Chengdu 610213 Peoples R China Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China Australian Catholic Univ Peter Faber Business Sch Discipline Informat Technol Sydney NSW Australia Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China

With the rapid development of wireless networks, the burden on data transmission is becoming much higher, so are the requirements for bandwidth and load balancing. To cope with these changing requirements, we investigate a novel problem of finding maximum disjoint paths with different colors (MDPDC). In MDPDC, transmission frequencies in a network are modeled as different colors on network nodes. The aim is to find a maximum number of color-constrained node-disjoint paths where nodes must share the same color within any disjoint path, and differ in color among different disjoint paths. For this proposed problem, we first prove MDPDC is MP-complete in both directed and undirected graphs. Then we provide two practical linear programming based solutions with theoretical justifications of their correctness and time complexity. Extensive computer experiments are also carried out with several compared baseline methods to demonstrate the effectiveness of proposed algorithms both in running time and solution quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Wireless networks Maximum disjoint paths with different colors NP-completeness linear programming

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A State Based Resource Allocation Game for Distributed Optimization in 5G Small-Cell Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第11期70卷 12072-12087页

作者： Mawatwal, Khushboo Roy, Rajarshi Sen, Debarati Indian Inst Technol Kharagpur 721302 W Bengal India

The upcoming 5G advocates efficient utilization of the existing spectrum through cell densification using small cells. However, dense packing of small-cells over a given area experiences severe inter-cell interference and fierce competition for shared resources. In this paper, we address the topic of resource allocation in interference-prone 5G small-cell networks (SCNs). We formulate a state based potential game for resource sharing problems to fulfill and maximize users' quality of experience (QoE). The designed game uses knowledge exchange among players through a communication graph to solve the optimization problem. We employ the time-invariant and time-varying structure of the communication graph in the proposed game. In addition, we propose a decentralized scheme for the joint optimization of spectrum allocation, power selection, and user scheduling. Through extensive simulations, we show that our algorithm surpasses the existing schemes in terms of QoE, computational complexity, and convergence rate for stable resource allocations. Furthermore, it is robust to wireless link failure and state transition in the communication graph. Besides, under imperfect channel state information, it suffers relatively less performance degradation compared to the existing schemes.

关键词： Games Resource management Quality of experience 5G mobile communication Optimization linear programming NOMA Decentralized optimization resource allocation QoE state based game 5G small-cell networks

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Prestack Seismic Inversion With Data-Driven MRF-Based Regularization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第8期59卷 7122-7136页

作者： Guo, Qiang Ba, Jing Luo, Cong China Jiliang Univ Coll Informat Engn Key Lab Electromagnet Wave Informat Technol & Met Hangzhou 310018 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China

Regularization is effective in mitigating the ill-condition existing in inverse problems. With respect to the ill-conditioned prestack seismic inversion, regularization aims to stabilize the multiple inverted results and, essentially, reconstruct structural features of subsurface parameter (model) as realistic as possible. Among variants of regularization method, Markov random field (MRF) is an effective approach in formulating prior constraint. However, standard MRF-based or other methods often require prior knowledge of the structural features of desired models, e.g., smoothness, blockiness, and sparsity, after which the prior constraint is formulated. Therefore, such a model-driven regularization method lacks applicability to the cases with geological complexity or limited prior knowledge. In this article, we propose a data-driven regularization scheme for prestack seismic inversion. The MRF-based constraints formulated by multiple orders are quantitatively integrated into the inversion procedure driven by seismic data. In order to endow the method with high adaptation to geological complexity, we iteratively adjust the regularization parameters of multiple orders via the maximum likelihood estimator. Besides, we incorporate the multivariate Gaussian distribution among elastic parameters into the model update/perturbation in fast simulated annealing, by which the objective function is optimized while the multiple results are correlated and stabilized. Synthetic tests indicate that the proposed method is capable of revealing structural details and achieving multiple results with less uncertainty. Field application provides further validation, wherein the results distinctly reveal structural details within the target formation.

关键词： Maximum likelihood estimation Uncertainty Inverse problems Geology Simulated annealing linear programming Complexity theory Data-driven regularization Markov random field (MRF) maximum likelihood estimator (MLE) prestack seismic inversion

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Distributed Constrained Optimization Over Unbalanced Directed Networks Using Asynchronous Broadcast-Based Algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第3期66卷 1102-1115页

作者： Li, Huaqing Lu, Qingguo Chen, Guo Huang, Tingwen Dong, Zhaoyang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. Furthermore, we particularly discuss the scenario in which the interactions among agents over the whole network are subjected to possible link failures. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures. Under two standard assumptions that the communication network is directly strongly connected and the subgradients of all local objective functions are bounded, we provide an explicit analysis for convergence of the algorithm. Simulation results obtained by three numerical experiments substantiate the feasibility of the algorithm and validate the theoretical findings.

关键词： Signal processing algorithms Convex functions linear programming Convergence Distributed algorithms Cost function Asynchronous algorithm distributed convex optimization epigraph form inequality constraints unbalanced directed networks

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A Multiobjective Optimization of PCB Prototyping Assembly With OFA Based on the Similarity of Intuitionistic Fuzzy Sets

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第7期29卷 2054-2061页

作者： Zhang, Wei-Bo Zhu, Guang-Yu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China

Shop scheduling is a significant factor affecting the ability of manufacturers of printed circuit board (PCB) prototyping assemblies to remain profitable and meet the needs of customers. This scheduling is complicated and involves various factors;thus, the shop scheduling problem of a PCB prototyping assembly is regarded as a multiobjective permutation flow shop scheduling problem (MOPFSP). A mathematical model with four objective functions is built based on the characteristics of a PCB prototyping assembly shop. In this article an aggregation-based approach is designed to aggregate multiple objective function values into the similarity of intuitionistic fuzzy sets (sIFSs). This approach is adopted as a fitness function assignment strategy and is combined with the optimal foraging algorithm (OFA) to build a multiobjective evolutionary algorithm: the OFA based on sIFSs (OFA-sIFSs). To verify the performance of OFA-sIFSs, six congress on evolutionary computation benchmark test functions, ten MOPFSP benchmark instances and a practical problem for a PCB prototyping assembly are solved by OFA-sIFSs and three state-of-the-art algorithms. Three types of similarity measures are evaluated. During the process of testing, four performance metrics, statistical analysis, and business software are employed. The results of this study suggest that OFA-sIFSs can be used to solve the MOPFSP and have better performances compared with the other three algorithms.

关键词： Fuzzy sets Optimization Job shop scheduling linear programming Mathematical model Software algorithms Intuitionistic fuzzy sets (IFSs) multiobjective optimization multiobjective permutation flow shop scheduling problem (MOPFSP) optimal foraging algorithm (OFA) printed circuit board (PCB) prototyping assembly

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Multi-Objective Optimization Method to Search for the Optimal Convolutional Neural Network Architecture for Long-Term Structural Health Monitoring

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IEEE ACCESS 2021年 9卷 44738-44750页

作者： Oh, Byung Kwan Kim, Jimin Seoul Natl Univ Sci & Technol Sch Architecture Seoul 01811 South Korea

To ensure the safety of structures, structural health monitoring (SHM) techniques that use cutting-edge sensing technologies have been developed. However, in the process of long-term structural health monitoring, sensor defects and data loss commonly occur, which pose limitations in the current SHM technique. To recover lost data and predict structural responses, convolutional neural networks (CNNs) have been used in SHM, but no obvious technique or rule for configuring CNN architecture with optimal performance has been presented yet. This study proposes a method for searching for the optimal CNN architecture capable of predicting the structural response of structures to evaluate their long-term safety. In this method, multi-objective optimization, considering both prediction performance and CNN training efficiency, is presented as a strategy. The optimization method using the two objective functions is applied to the structural response estimation, and the characteristics of the derived solutions are examined. Furthermore, the solutions derived using the two objective functions are classified into two solution groups that are biased to each objective function, and a strategy for minimizing the discrepancy between the two solution groups is additionally presented based on their trade-off relationship. The architecture characteristics, estimation performance, and training efficiency of the solutions derived by setting the discrepancy as the third objective function are investigated. The CNN derived by the proposed method with the third objective function reduced 40.35% of computational cost compared with that derived with two objective functions while they showed similar accuracies for the response estimation.

关键词： Training Safety linear programming Computer architecture Strain Monitoring Kernel Convolutional neural network data prediction genetic algorithm measurement multi-objective optimization structural health monitoring

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Weighted Double-Low-Rank Decomposition With Application to Fabric Defect Detection

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第3期18卷 1170-1190页

作者： Mo, Dongmei Wong, Wai Keung Lai, Zhihui Zhou, Jie Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

Recently, many methods based on low-rank representation have been proposed for fabric defect detection. Most of them relax the low-rank decomposition problem to a nuclear norm minimization (NNM) problem to pursue the convexity of the objective function. When solving the standard NNM problem, matrix singular values have to be treated equally. This, however, would be impractical in the scenario of fabric defect detection as the matrix singular values have clear physical meanings, and thus, they should be treated differently. In this article, we propose a weighted double-low-rank decomposition method (WDLRD) to treat the matrix singular values differently by assigning different weights. Thus, the most important/distinguishing characteristics of a fabric image can be preserved. Another difference between WDLRD and the other existing low-rank-based methods is that WDLRD considers a defective fabric image being decomposed to two low-rank matrices, i.e., low-rank defect-free matrix and low-rank defect matrix, as the defect-free and defective regions are usually composed of homogeneous objects that have a high correlation. Besides, WDLRD is more robust for defect detection in various situations by adding a noise term to avoid noise or other interference on the fabric surface. In addition, a defect prior is incorporated into the objective function of WDLRD to guide locating the defective regions. The proposed optimization problem can be easily solved by an iterative algorithm based on augmented Lagrange multipliers. Experimental results on TILDA, periodically patterned fabric, and Textile & Apparel Artificial Intelligence databases show that the proposed WDLRD obtains better performance than state-of-the-art methods in locating the defective regions on fabric images. Note to Practitioners-This article is motivated by the problem that the performance of fabric defect detection in the textile industry is poor. It is necessary to develop an effective method to improve the defec

关键词： Fabrics Inspection Matrix decomposition Weaving Optimization Image processing linear programming Fabric defect detection low-rank representation patterned fabric plain fabric sparse matrix

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EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2384-2393页

作者： Yaghoobi, Hanif Babaei, Esmaeil Hussen, Bashdar Mahmud Emami, Ali Univ Tabriz Sch Nat Sci Dept Anim Biol Tabriz 51368 East Azerbaijan Iran Univ Tabriz Inst Environm Tabriz 51368 East Azerbaijan Iran Hawler Med Univ Coll Pharm Erbil 44000 Iraq

Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified Multi Objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l(1)-SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a. Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://***/hanif-y.

关键词： Cancer Bioinformatics Biomarkers linear programming Optimization Classification algorithms Support vector machines Bioinformatics feature selection microRNAs with biomarker potency multi objective optimization

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Adaptive online portfolio selection with transaction costs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期295卷 1074-1086页

作者： Guo, Sini Gu, Jia-Wen Ching, Wai-Ki Univ Hong Kong Dept Math Adv Modeling & Appl Comp Lab Pokfulam Rd Hong Kong Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen Peoples R China Hughes Hall Wollaston Rd Cambridge England

As an application of machine learning techniques in financial fields, online portfolio selection has been attracting great attention from practitioners and researchers, which makes timely sequential decision making available when market information is constantly updated. For online portfolio selection, transaction costs incurred by changes of investment proportions on risky assets have a significant impact on the investment strategy and the return in long-term investment horizon. However, in many online portfolio selection studies, transaction costs are usually neglected in the decision making process. In this paper, we consider an adaptive online portfolio selection problem with transaction costs. We first propose an adaptive online moving average method (AOLMA) to predict the future returns of risky assets by incorporating an adaptive decaying factor into the moving average method, which improves the accuracy of return prediction. The net profit maximization model (NPM) is then constructed where transaction costs are considered in each decision making process. The adaptive online net profit maximization algorithm (AOLNPM) is designed to maximize the cumulative return by integrating AOLMA and NPM together. Numerical experiments show that AOLNPM dominates several state-of-the-art online portfolio selection algorithms in terms of various performance metrics, i.e., cumulative return, mean excess return, Sharpe ratio, Information ratio and Calmar ratio. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Decision support systems Online portfolio selection Adaptive moving average method Transaction cost linear programming

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