基于CVaR子模效益模型的传感器布局优化

谢晓娟; 王耀力

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基于CVaR子模效益模型的传感器布局优化

更新时间：2026-09-04

- 基于CVaR子模效益模型的传感器布局优化
- Sensor deployment optimization based on CVaR submodular utility maximization model
- 微电子学与计算机 2020年37卷第1期页码：14-19
- 作者机构：
  
  太原理工大学信息与计算机学院,山西,晋中,030600
- 作者简介：
- 基金信息：
  
  山西省自然科学基金 201801D121141
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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谢晓娟, 王耀力. 基于CVaR子模效益模型的传感器布局优化[J]. 微电子学与计算机, 2020, 37(1): 14-19.

XIE Xiao-juan, WANG Yao-li. Sensor deployment optimization based on CVaR submodular utility maximization model[J]. 2020, 37(1): 14-19.
谢晓娟, 王耀力. 基于CVaR子模效益模型的传感器布局优化[J]. 微电子学与计算机, 2020, 37(1): 14-19. DOI：

XIE Xiao-juan, WANG Yao-li. Sensor deployment optimization based on CVaR submodular utility maximization model[J]. 2020, 37(1): 14-19. DOI：

摘要

针对在不确定情况下如何保证传感器布局取得最优效果问题，本文在初始部署节点时考虑节点存在的不确定性，采用基于CVaR的子模效益模型来最小化这种不确定性对传感器网络布局效果的影响，为了快速有效获得该模型下的最优传感器布局，对传统贪婪算法进行改进，根据模型中存在的参数τ对全局最优解进行有序搜索，同时引入lazy evuluation减少算法的时间复杂度.仿真实验表明，在不确定情况下对传感器进行布局时，CVaR模型可以有效提高网络布局的鲁棒性，与改进的贪婪算法相结合，可以快速获得保证较高信息增益下的布局点集.

Abstract

In order to ensure the optimal effect of sensor layout under uncertain conditions

this paper considers the uncertainty of nodes in the initial deployment of nodes

and adopts CVaR submodular utility maximization model to minimize the uncertainty of sensor network layout. In order to quickly and effectively obtain the optimal sensor layout under the model

the traditional greedy algorithm is improved

and the global optimal solution is searched according to the parameter τ existing in the model. At the same time

the lazy evaluation is introduced to decrease time complexity. Simulation experiments show that the CVaR model can effectively improve the robustness of the sensor deployment network when the sensor is laid out under uncertainty. Combined with the improved greedy algorithm

the layout point set can be quickly obtained with high information gain.

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references

WU W L. Application of VaR in risk management[J].Modern Commerce, 2008(35):85-86. DOI: 85

SRINIVAS N, KRAUSE A, KAKADE S M, et al. Information-theoretic regret bounds for gaussian process optimization in the bandit setting[J]. IEEE Transactions on Information Theory, 2012, 58(5):3250-3265. DOI: 3250

LOU Y, HONG Y, XIE L, et al.Nash equilibrium comptetation in subnetwork Zero-Sum games with switching communication[J].IEEE Transactions on Automatic Control, 2016, 61(10):1-15. DOI: 1

KRAUSE A, RAJAGOPAL R, GUPTA A, et al. Simultaneous optimization of sensor placements and balanced schedules[J]. IEEE Transactions on Automatic Control, 2011, 56(10):2390-2405. DOI: 2390

TAKANORI MAEHARA. Risk averse submodular utility maximization[J]. Operations Research Letters, 2015, 43(5):526-529. DOI: 526

XIONG H, XIE J, DENG X. Risk-averse decision making in overbooking problem[J]. Journal of the Operational Research Society, 2011(62): 1655-1665.DOI:10.1057/jors.2010.130.

WEI B G, KONG Y Y. Application status of wireless sensor networks in various fields [J]. Electronic Technology and Software Engineering, 2018(7):25. DOI: 25

TONG G, WU W, TANG S, et al. Adaptive influence maximization in dynamic social networks[J]. IEEE/ACM Transactions on Networking, 2015, 25(1):112-125. DOI: 112

GONZALEZ-LONGATT F, RUEDA J L, DOGDANOV D. Mean variance mapping optimization for the identification of gaussian mixture model: test case[C]// Efficacité énergétique - sources d'énergies renouvelablesprotectionde l'environnement COFRET'12. IEEE, 2012.

PARK H, HUTCHINSON S. Robust optimal deployment in mobile sensor networks with peer-to-peer communication[C]// IEEE International Conference on Robotics % Automation. Hong Kong, China, IEEE, 2014.

NING Y, WANG X, PAN D. VaR for Loan Portfolio in Uncertain Environment[C]// Seventh International Joint Conference on Computational Sciences % Optimization. Beijing, China, IEEE Computer Society, 2014.

PIRSIAVASH H, RAMANAN D, FOWLKES C C. Globally-optimal greedy algorithms for tracking a variable number of objects[C]//Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011.

TYRRELL ROCKAFELLAR R, STANISLAV URYASEV. Optimization of conditional valueat-risk[J]. Journal of risk, 2000(2):21-42. DOI: 21

AKRAUSE, CGUESTRIN. Submodularity and its applications in optimized information gathering[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(4): 32. DOI: 32

ROCKAFELLAR R T. Conditional value-at-risk for general loss distributions[J]. Journal of Banking % Finance, 2002, 26(7):1443-1471. DOI: 1443

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