An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA
We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing...
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| Veröffentlicht in: | International journal of distributed sensor networks Jg. 2016; H. 3; S. 8612128 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London, England
Hindawi Publishing Corporation
01.01.2016
SAGE Publications Sage Publications Ltd. (UK) John Wiley & Sons, Inc Wiley |
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| ISSN: | 1550-1329, 1550-1477, 1550-1477 |
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| Abstract | We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times. |
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| AbstractList | We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times. |
| Audience | Academic |
| Author | Kim, Yong-Hyuk Yoon, Yourim Seo, Jae-Hyun |
| Author_xml | – sequence: 1 givenname: Jae-Hyun surname: Seo fullname: Seo, Jae-Hyun organization: Department of Computer Science and Engineering Kwangwoon University 20 Kwangwoon-ro Nowon-gu Seoul 139-701 Republic of Korea kw.ac.kr – sequence: 2 givenname: Yourim surname: Yoon fullname: Yoon, Yourim organization: Department of Computer Engineering Gachon University 1342 Sengnamdaero Sujeong-gu Seongnam-si Gyeonggi-do 461-701 Republic of Korea gachon.ac.kr – sequence: 3 givenname: Yong-Hyuk surname: Kim fullname: Kim, Yong-Hyuk organization: Department of Computer Science and Engineering Kwangwoon University 20 Kwangwoon-ro Nowon-gu Seoul 139-701 Republic of Korea kw.ac.kr |
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| Copyright | Copyright © 2016 Jae-Hyun Seo et al. 2016 Jae-Hyun Seo et al. COPYRIGHT 2016 Sage Publications Ltd. (UK) Copyright © 2016 Jae-Hyun Seo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| References | Cardei M. Wu J. Coverage in wireless sensor networks Handbook of Sensor Networks 2005 CRC Press (13) 2013; 43 (8) 2014; 2014 Wang X. Wang S. Ma J. Dynamic deployment optimization in wireless sensor networks Proceedings of Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16–19, 2006 2006 344 Berlin, Germany Springer 182 187 Lecture Notes in Control and Information Sciences 10.1007/978-3-540-37256-1_25 (15) 2012; 2 Carter B. Ragade R. An extensible model for the deployment of non-isotropic sensors Proceedings of the 3rd IEEE Sensors Applications Symposium (SAS '08) February 2008 Atlanta, Ga, USA 22 25 2-s2.0-65249129884 Zou Y. Chakrabarty K. Sensor deployment and target localization based on virtual forces 2 Proceedings of the Twenty-Second Annual Joint Conference of the IEEE Computer and Communications (INFOCOM '03) March-April 2003 San Francisco, Calif, USA IEEE 1293 1303 10.1109/INFCOM.2003.1208965 (2) 2011; 43 (16) 2012; 12 (3) 2002; 51 Jourdan D. B. de Weck O. L. Layout optimization for a wireless sensor network using a multi-objective genetic algorithm Proceedings of the IEEE 59th Vehicular Technology Conference (VTC '04) May 2004 IEEE 2466 2470 2-s2.0-15344347891 (7) 2015; 10 Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning 1989 Addison-Wesley Deif D. S. Gadallah Y. Wireless Sensor Network deployment using a variable-length genetic algorithm Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '14) April 2014 Istanbul, Turkey IEEE 2450 2455 10.1109/wcnc.2014.6952773 2-s2.0-84912096014 (14) 2011; 24 (22) 2015; 8 (24) 2014; 2014 (25) 2014; 15 Lamm L. M. J. Develop measures of effectiveness and deployment optimization rules for networked ground micro-sensors [M.S. thesis] 2001 Charlottesville, Va, USA Division of Systems Engineering, School of Engineering and Applied Science, University of Virginia Harvey I. The microbial genetic algorithm Advances in Artificial Life. Darwin Meets von Neumann 2011 Berlin, Germany Springer 126 133 (32) 2015; 45 Ab Aziz N. A. B. Mohemmed A. W. Alias M. Y. A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC '09) March 2009 Okayama, Japan 602 607 10.1109/icnsc.2009.4919346 2-s2.0-70349148619 Hogg R. V. Tanis E. A. Probability and Statistical Inference 2014 New York, NY, USA Macmillan (28) 2014; 2014 (30) 2012; 2 Jourdan D. B. de Weck O. L. Multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III April 2004 Orlando, Fla, USA 565 575 Proceedings of SPIE 10.1117/12.541685 (9) 2008; 91 Carter B. Ragade R. An extensible model for the deployment of non-isotropic sensors Proceedings of the IEEE Sensors Applications Symposium (SAS '08) February 2008 Atlanta, Ga, USA IEEE 22 25 CUDA—Nvidia Compute Unified Device Architecture C Programming Guide 2014 Carter B. Ragade R. A probabilistic model for the deployment of sensors Proceedings of the IEEE Sensors Applications Symposium (SAS '09) February 2009 New Orleans, La, USA IEEE 7 12 10.1109/sas.2009.4801767 2-s2.0-65249093587 Watfa M. K. Commuri S. Optimal 3-dimensional sensor deployment strategy 2 Proceedings of the 3rd IEEE Consumer Communications and Networking Conference (CCNC '06) January 2006 IEEE 892 896 10.1109/ccnc.2006.1593167 2-s2.0-33749057692 (1) 2014; 16 Yoon, Kim 2013; 43 Ta, Huang, Binh 2015; 10 Choi, Jeong, Seo, Yang 2014; 2014 Feng, Liu, Wang 2014; 15 Chen, Mukhopadhyay, Chuang, Lin, Liao, Wang, Jiang 2015; 45 Indhumathi, Venkatesan 2015; 8 Chakrabarty, Iyengar, Qi, Cho 2002; 51 Seo, Kim, Ryou, Cha, Jo 2008; 91 Unaldi, Temel, Asari 2012; 12 Shi, Zhou 2012; 2 Kim, Park, Lee, Won 2011; 24 Deif, Gadallah 2014; 16 Wang 2011; 43 Seo, Lee, Kim 2014; 2014 Zhao, Bai, Jiang, Shen, Tang 2014; 2014 Khan, Shams, Umair, Waseem 2012; 2 B29-2016-8612128 Carter B. (B4-2016-8612128) B27-2016-8612128 B28-2016-8612128 B24-2016-8612128 B25-2016-8612128 B11-2016-8612128 B13-2016-8612128 Khan F. H. (B26-2016-8612128) 2012; 2 Jourdan D. B. (B15-2016-8612128) B8-2016-8612128 B2-2016-8612128 B7-2016-8612128 B14-2016-8612128 B3-2016-8612128 Carter B. (B20-2016-8612128) B30-2016-8612128 B5-2016-8612128 B12-2016-8612128 Goldberg D. E. (B10-2016-8612128) 1989 Cardei M. (B6-2016-8612128) 2005 B32-2016-8612128 B19-2016-8612128 B18-2016-8612128 Hogg R. V. (B31-2016-8612128) 2014 B16-2016-8612128 B1-2016-8612128 CUDA—Nvidia (B9-2016-8612128) 2014 B17-2016-8612128 B23-2016-8612128 B22-2016-8612128 B21-2016-8612128 |
| References_xml | – reference: Jourdan D. B. de Weck O. L. Multi-objective genetic algorithm for the automated planning of a wireless sensor network to monitor a critical facility Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III April 2004 Orlando, Fla, USA 565 575 Proceedings of SPIE 10.1117/12.541685 – volume: 91 start-page: 3534 issue: 11 year: 2008 end-page: 3543 ident: 9 article-title: Optimal sensor deployment for wireless surveillance sensor networks by a hybrid steady-state genetic algorithm – reference: Goldberg D. E. Genetic Algorithms in Search, Optimization, and Machine Learning 1989 Addison-Wesley – volume: 43 start-page: 1473 issue: 5 year: 2013 end-page: 1483 ident: 13 article-title: An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks – volume: 12 start-page: 5116 issue: 4 year: 2012 end-page: 5133 ident: 16 article-title: Method for optimal sensor deployment on 3D terrains utilizing a steady state genetic algorithm with a guided walk mutation operator based on the wavelet transform – volume: 15 start-page: 643 issue: 3 year: 2014 end-page: 656 ident: 25 article-title: Genetic algorithm based optimal placement of PIR sensors for human motion localization – reference: Zou Y. Chakrabarty K. Sensor deployment and target localization based on virtual forces 2 Proceedings of the Twenty-Second Annual Joint Conference of the IEEE Computer and Communications (INFOCOM '03) March-April 2003 San Francisco, Calif, USA IEEE 1293 1303 10.1109/INFCOM.2003.1208965 – reference: Carter B. Ragade R. An extensible model for the deployment of non-isotropic sensors Proceedings of the 3rd IEEE Sensors Applications Symposium (SAS '08) February 2008 Atlanta, Ga, USA 22 25 2-s2.0-65249129884 – volume: 2014 year: 2014 end-page: 9 ident: 8 article-title: Optimal deployment and scheduling with directional sensors for energy-efficient barrier coverage – reference: CUDA—Nvidia Compute Unified Device Architecture C Programming Guide 2014 – reference: Deif D. S. Gadallah Y. Wireless Sensor Network deployment using a variable-length genetic algorithm Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '14) April 2014 Istanbul, Turkey IEEE 2450 2455 10.1109/wcnc.2014.6952773 2-s2.0-84912096014 – reference: Harvey I. The microbial genetic algorithm Advances in Artificial Life. Darwin Meets von Neumann 2011 Berlin, Germany Springer 126 133 – volume: 10 start-page: 300 issue: 5 year: 2015 end-page: 308 ident: 7 article-title: Covering the target objects with mobile sensors by using genetic algorithm in wireless sensor networks – volume: 45 start-page: 2309 issue: 10 year: 2015 end-page: 2322 ident: 32 article-title: A hybrid memetic framework for coverage optimization in wireless sensor networks – reference: Wang X. Wang S. Ma J. Dynamic deployment optimization in wireless sensor networks Proceedings of Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16–19, 2006 2006 344 Berlin, Germany Springer 182 187 Lecture Notes in Control and Information Sciences 10.1007/978-3-540-37256-1_25 – volume: 8 issue: 16 year: 2015 ident: 22 article-title: Improving coverage deployment for dynamic nodes using genetic algorithm in wireless sensor networks – volume: 2 start-page: 1 issue: 1 year: 2012 end-page: 8 ident: 15 article-title: Two-stage dynamic sensor deployment strategy based on virtual force and genetic algorithm in wireless sensor networks – reference: Carter B. Ragade R. A probabilistic model for the deployment of sensors Proceedings of the IEEE Sensors Applications Symposium (SAS '09) February 2009 New Orleans, La, USA IEEE 7 12 10.1109/sas.2009.4801767 2-s2.0-65249093587 – volume: 16 start-page: 834 issue: 2 year: 2014 end-page: 855 ident: 1 article-title: Classification of wireless sensor networks deployment techniques – volume: 2014 year: 2014 end-page: 15 ident: 28 article-title: Feature selection for very short-term heavy rainfall prediction using evolutionary computation – reference: Jourdan D. B. de Weck O. L. Layout optimization for a wireless sensor network using a multi-objective genetic algorithm Proceedings of the IEEE 59th Vehicular Technology Conference (VTC '04) May 2004 IEEE 2466 2470 2-s2.0-15344347891 – reference: Hogg R. V. Tanis E. A. Probability and Statistical Inference 2014 New York, NY, USA Macmillan – reference: Ab Aziz N. A. B. Mohemmed A. W. Alias M. Y. A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC '09) March 2009 Okayama, Japan 602 607 10.1109/icnsc.2009.4919346 2-s2.0-70349148619 – volume: 43 issue: 4, article 32 year: 2011 ident: 2 article-title: Coverage problems in sensor networks: a survey – volume: 2014 year: 2014 end-page: 10 ident: 24 article-title: Game-theoretic camera selection using inference tree method for a wireless visual sensor network – reference: Cardei M. Wu J. Coverage in wireless sensor networks Handbook of Sensor Networks 2005 CRC Press – volume: 51 start-page: 1448 issue: 12 year: 2002 end-page: 1453 ident: 3 article-title: Grid coverage for surveillance and target location in distributed sensor networks – reference: Watfa M. K. Commuri S. Optimal 3-dimensional sensor deployment strategy 2 Proceedings of the 3rd IEEE Consumer Communications and Networking Conference (CCNC '06) January 2006 IEEE 892 896 10.1109/ccnc.2006.1593167 2-s2.0-33749057692 – reference: Carter B. Ragade R. An extensible model for the deployment of non-isotropic sensors Proceedings of the IEEE Sensors Applications Symposium (SAS '08) February 2008 Atlanta, Ga, USA IEEE 22 25 – reference: Lamm L. M. J. Develop measures of effectiveness and deployment optimization rules for networked ground micro-sensors [M.S. thesis] 2001 Charlottesville, Va, USA Division of Systems Engineering, School of Engineering and Applied Science, University of Virginia – volume: 24 start-page: 318 issue: 2 year: 2011 end-page: 324 ident: 14 article-title: Determining optimal sensor locations in freeway using genetic algorithm-based optimization – volume: 2 start-page: 8 issue: 1 year: 2012 end-page: 11 ident: 30 article-title: Deployment of sensors to optimize the network coverage using genetic algorithm – volume: 2 start-page: 8 issue: 1 year: 2012 end-page: 11 article-title: Deployment of sensors to optimize the network coverage using genetic algorithm – volume: 43 start-page: 1473 issue: 5 year: 2013 end-page: 1483 article-title: An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks – volume: 2 start-page: 1 issue: 1 year: 2012 end-page: 8 article-title: Two-stage dynamic sensor deployment strategy based on virtual force and genetic algorithm in wireless sensor networks – volume: 45 start-page: 2309 issue: 10 year: 2015 end-page: 2322 article-title: A hybrid memetic framework for coverage optimization in wireless sensor networks – volume: 16 start-page: 834 issue: 2 year: 2014 end-page: 855 article-title: Classification of wireless sensor networks deployment techniques – volume: 51 start-page: 1448 issue: 12 year: 2002 end-page: 1453 article-title: Grid coverage for surveillance and target location in distributed sensor networks – volume: 10 start-page: 300 issue: 5 year: 2015 end-page: 308 article-title: Covering the target objects with mobile sensors by using genetic algorithm in wireless sensor networks – volume: 24 start-page: 318 issue: 2 year: 2011 end-page: 324 article-title: Determining optimal sensor locations in freeway using genetic algorithm-based optimization – volume: 2014 year: 2014 end-page: 9 article-title: Optimal deployment and scheduling with directional sensors for energy-efficient barrier coverage – volume: 91 start-page: 3534 issue: 11 year: 2008 end-page: 3543 article-title: Optimal sensor deployment for wireless surveillance sensor networks by a hybrid steady-state genetic algorithm – volume: 43 issue: 4, article 32 year: 2011 article-title: Coverage problems in sensor networks: a survey – volume: 12 start-page: 5116 issue: 4 year: 2012 end-page: 5133 article-title: Method for optimal sensor deployment on 3D terrains utilizing a steady state genetic algorithm with a guided walk mutation operator based on the wavelet transform – volume: 8 issue: 16 year: 2015 article-title: Improving coverage deployment for dynamic nodes using genetic algorithm in wireless sensor networks – volume: 2014 year: 2014 end-page: 10 article-title: Game-theoretic camera selection using inference tree method for a wireless visual sensor network – volume: 2014 year: 2014 end-page: 15 article-title: Feature selection for very short-term heavy rainfall prediction using evolutionary computation – volume: 15 start-page: 643 issue: 3 year: 2014 end-page: 656 article-title: Genetic algorithm based optimal placement of PIR sensors for human motion localization – volume: 2 start-page: 8 issue: 1 year: 2012 ident: B26-2016-8612128 publication-title: Sir Syed University Research Journal of Engineering and Technology – ident: B11-2016-8612128 doi: 10.1109/TCYB.2013.2250955 – ident: B29-2016-8612128 doi: 10.17706/jcp.10.5.300-308 – ident: B25-2016-8612128 doi: 10.1109/icnsc.2009.4919346 – ident: B5-2016-8612128 doi: 10.1109/INFCOM.2003.1208965 – ident: B28-2016-8612128 doi: 10.1109/wcnc.2014.6952773 – ident: B2-2016-8612128 doi: 10.1145/1978802.1978811 – ident: B12-2016-8612128 doi: 10.1016/j.engappai.2010.10.020 – ident: B7-2016-8612128 doi: 10.1155/2014/596983 – ident: B27-2016-8612128 doi: 10.1007/978-3-540-37256-1_25 – volume-title: Genetic Algorithms in Search, Optimization, and Machine Learning year: 1989 ident: B10-2016-8612128 – ident: B30-2016-8612128 doi: 10.17485/ijst/2015/v8i16/62538 – ident: B22-2016-8612128 doi: 10.1109/ccnc.2006.1593167 – volume-title: Handbook of Sensor Networks year: 2005 ident: B6-2016-8612128 – ident: B24-2016-8612128 doi: 10.1155/2014/203545 – start-page: 22 volume-title: Proceedings of the 3rd IEEE Sensors Applications Symposium (SAS '08) ident: B20-2016-8612128 – start-page: 2466 volume-title: Proceedings of the IEEE 59th Vehicular Technology Conference (VTC '04) ident: B15-2016-8612128 – volume-title: Probability and Statistical Inference year: 2014 ident: B31-2016-8612128 – ident: B8-2016-8612128 doi: 10.1093/ietcom/e91-b.11.3534 – ident: B32-2016-8612128 doi: 10.1109/tcyb.2014.2371139 – start-page: 22 volume-title: Proceedings of the IEEE Sensors Applications Symposium (SAS '08) ident: B4-2016-8612128 – ident: B17-2016-8612128 doi: 10.1109/sas.2009.4801767 – ident: B18-2016-8612128 doi: 10.1007/978-3-642-21314-4_16 – volume-title: Compute Unified Device Architecture C Programming Guide year: 2014 ident: B9-2016-8612128 – ident: B19-2016-8612128 doi: 10.1155/2014/839710 – ident: B23-2016-8612128 – ident: B1-2016-8612128 doi: 10.1109/surv.2013.091213.00018 – ident: B14-2016-8612128 doi: 10.3390/s120405116 – ident: B3-2016-8612128 doi: 10.1109/tc.2002.1146711 – ident: B13-2016-8612128 doi: 10.5815/ijeme.2012.01.01 – ident: B16-2016-8612128 doi: 10.1117/12.541685 – ident: B21-2016-8612128 doi: 10.1007/s11081-012-9209-z |
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| SubjectTerms | Algorithms Applied research Computation Computational efficiency Computer simulation Computing time Fitness Genetic algorithms Optimization theory Sensors |
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| Title | An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA |
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