Một số thuật toán metaheuristic giải bài toán bao phủ diện tích và đối tượng trong mạng cảm biến không dây

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BË GIO D÷C V O TO TR ÕNG I HÅC BCH KHOA H NËI NGUYN THÀ HNH MËT SÈ THUT TON METAHEURISTIC GII BI TON BAO PH’ DIN TCH V ÈI T —NG TRONG MNG CM BIN KHỈNG DY LUN N TIN S KHOA HÅC MY TNH H NỴi - 2019 BË GIO D÷C V O TO TR ÕNG I HÅC BCH KHOA H NËI NGUYN THÀ HNH MËT SÈ THUT TON METAHEURISTIC GII BI TON BAO PH’ DIN TCH V ÈI T —NG TRONG MNG CM BIN KHỈNG DY Ng nh : M sậ : Khoa hc mĂy tẵnh 9480101 LUN N TIN S KHOA HÅC MY TNH NG ÕI H ŒNG DN KHOA HÅC: PGS.TS Hu˝nh Th‡ Thanh Bẳnh PGS.TS Nguyạn c Nghắa H Nẻi - 2019 Lèi cam oan Nghiản cu sinh cam oan luên Ăn n y l cấng trẳnh nghiản cu ca chẵnh mẳnh dểi sá hểng dăn ca têp th cĂn bẻ hểng dăn Luên Ăn c s dng thấng tin trẵch dăn t nhiãu ngun tham khÊo khĂc v cĂc thấng tin trẵch dăn ềc ghi r ngun gậc CĂc sậ liằu, kát quÊ luên Ăn l trung thác v ch˜a t¯ng ˜Ịc cÊng bË c¡c cÊng tr¼nh nghiản cu ca bĐt k tĂc giÊ n o khĂc Thay mt têp th giĂo viản hểng dăn PGS.TS Hunh Th Thanh Bẳnh ii H Nẻi, ng y 05 thĂng 11 nôm 2019 Nghiản cu sinh Nguyạn Th HÔnh Lèi cÊm ẽn Lèi Ưu tiản, tấi xin b y t lãng biát ẽn sƠu sc tểi cĂc thƯy cấ giĂo hểng dăn, PGS.TS Hunh Th Thanh Bẳnh v PGS.TS Nguyạn c Nghắa ,  nh hểng khoa hc v tên tƠm gip ễ, ch bÊo suật quĂ trẳnh ho n th nh luên Ăn tÔi trèng Ôi hc BĂch Khoa H Nẻi Tấi xin chƠn th nh cÊm ẽn Ban giĂm hiằu, Ban lÂnh Ôo Viằn cấng nghằ thấng tin v truyãn thấng, cĂc thƯy cấ bẻ mấn Khoa hc mĂy tẵnh v cĂc bÔn phãng nghiản cu Mấ hẳnh ha, mấ phng v tậi u ha, trèng Ôi hc BĂch khoa H Nẻi  tÔo iãu kiằn thuên lềi nhĐt tấi ho n th nh chẽng trẳnh hc têp v thác hiằn luên Ăn nghiản cu khoa hc ca mẳnh Tấi xin chƠn th nh cÊm ẽn Ban giĂm hiằu trèng Ôi hc Phẽng ấng, têp th cĂn bẻ, giÊng viản Khoa cấng nghằ thấng tin v truy·n thÊng nÏi nghi¶n c˘u sinh cÊng t¡c v c¡c bÔn b thƠn thiát  luấn tÔo iãu kiằn, ẻng viản, khuyán khẵch tấi ho n th nh luên ¡n n y CuËi cÚng, tÊi ch¥n th nh b y t lãng cÊm ẽn tểi gia ẳnh  kiản trẳ, chia s, ẻng viản nghiản cu sinh suật quĂ trẳnh hc têp v ho n th nh luên ¡n n y H NỴi, ng y 05 th¡ng 11 nôm 2019 Nghiản cu sinh Nguyạn Th HÔnh iii MữC L÷C BNG THUT NG⁄ VIT TT DANH SCH BNG DANH SCH HNH V M– U CÌ S– Lfi THUYT 1.1 MÔng cÊm bián khấng dƠy 1.1.1 1.1.2 1.1.3 1.1.4 1.2 C¡c mÊ h¼nh bao phı cıa c£m bi¸ 1.2.1 1.2.2 1.3 B i to¡n tËi ˜u 1.3.1 1.3.2 1.3.3 1.4 Kát luên chẽng BI TON C‹C I DIN TCH BAO PH’ TRONG MNG CM BIN KHỈNG DY KHặNG ầNG NHT iv 2.1 PhĂt biu b i to¡n 2.2 GiÊi thuêt ã xuĐt 2.2.1 2.2.2 2.2.3 2.2.4 2.3 K¸t qu£ th¸c nghi»m 2.3.1 2.3.2 2.3.3 2.4 Kát luên chẽng BI TON C‹C I DIN TCH BAO PH’ TRONG MNG CM BIN KHặNG DY KHặNG ầNG NHT C RNG BUËC CH ŒNG NGI VT 3.1 3.2 Ph¡t biºu b i to¡n GiÊi thuêt ã xuĐt 3.2.1 3.2.2 K¸t qu£ th¸c nghi»m 3.3.1 3.3.2 3.3.3 Kát luên chẽng 3.3 3.4 BI TON BAO PH’ ÈI T —NG M BO KT NÈI V CHÀU LÉI TRONG MNG CM BIN KHỈNG DY V MNG CM BIN KHặNG DY C SÔ DữNG IM THU PHT DI ËNG 4.1 B i to¡n bao phı Ëi t˜Òng £m bÊo kát nậi v chu lẩi mÔng cÊm bián khÊng d¥y 4.1.1 Ph¡t biºu b i toĂn GiÊi thuêt ã xuĐt 4.1.2 v 4.1.3 Kát quÊ th¸c nghi»m 114 4.2 B i to¡n bao ph ậi tềng Êm bÊ bián khấng dƠy c s dng cĂc i 4.2.1 4.2.2 4.2.3 4.3 Kát luên chẽng KT LUN DANH MữC CặNG TRNH CặNG Bẩ TI LIU THAM KHO vi BNG THUT NG⁄ VIT TT Ch˙ vi¸t tt IoT WSNs MWSNs SWSNS HWSNS LoS VFA MVFA GA PSO CS ICS FPA CFPA DPSO ACB MCT SCAN ITS MR RADA MDC ROM RAM LX AMXO TC NCFT SSCAT FS USP vii KT LUN Trong mÈi ch˜Ïng cıa luên Ăn ãu c mc tng kát khĂ chi tiát cĂc kát quÊ Ôt ềc ca tng chẽng Do , ph¦n n y t¡c gi£ ¡nh gi¡ tÍng quan v· c¡c ‚ng g‚p mĨi cıa luªn ¡n v h˜Ĩng nghiản cu tiáp theo CĂc ng gp mểi Luên Ăn giÊi quyát hai vĐn ã bao ph diằn tẵch v bao ph ậi tềng mÔng WSNs C th, vểi vĐn ã bao ph diằn tẵch luên Ăn quan tƠm nghiản cu i toĂn Ôi diằn tẵch bao ph vểi cĂc cÊm bián c ẻ ph khĂc trèng hềp c v khấng c chểng ngÔi vêt mÔng WSNs Vểi vĐn ã bao ph ậi tềng luên Ăn ã xuĐt hai b i toĂn giÊi quyát vĐn ã bao ph ậi tềng Êm bÊo kát nậi v chu lẩi mÔng WSNs v mÔng WSNs c s dng cĂc im thu phĂt di ẻng Chi tiát cıa t¯ng ‚ng g‚p luªn ¡n ˜Ịc thº hi»n nh sau: b Nghiản cu b i toĂn Ôi diằn tẵch bao ph mÔng cÊm bián khấng dƠy khÊng Áng nh§t ˜Ịc · xu§t [19] v · xuĐt mẻt sậ thuêt toĂn meta-heuristic (DPSO, ICS, CFPA v MIGA) giÊi quyát b i toĂn CĂc giÊi xuĐt ã xuĐt ềc so sĂnh Ănh giĂ vểi cĂc thuêt toĂn tật nhĐt trểc (IGA) vã diằn tẵch bao ph, thèi gian tẵnh toĂn v ẻ lằch chuân Kát quÊ nhên thĐy cĂc giÊi thuêt ã xuĐt ãu tật hÏn v· di»n t½ch bao phı, thÌi gian t½nh to¡n v ẻ n nh ca thuêt toĂn so sĂnh vểi IGA c biằt MIGA cho kát quÊ tật nhĐt sậ tĐt cÊ cĂc thuêt toĂn ã xuĐt B i vẳ, MIGA s dng kát hềp nhiãu toĂn t lai ghp a dÔng vã kiu gen Thảm v o ‚, MIGA s˚ dˆng heuristic ph¦n kh i tÔo l m cho cĂc cĂ th ềc sinh c‚ °c t½nh di truy·n tËt °c bi»t, t¡c gi£ luên Ăn  ã xuĐt cĂch tẵnh diằn tẵch chẵnh xĂc m cha c cấng trẳnh nghiản cu n o trểc ã xuĐt giÊi quyát cho b i toĂn Ôi diằn tẵch bao ph mÔng cÊm bián khấng dƠy khấng ng nhĐt Trong thác tá, vng trin khai mÔng thèng c cĂc chểng ngÔi vêt, tĂc giÊ ã xuĐt b i toĂn Ôi diằn tẵch bao ph mÔng cÊm bián khấng dƠy khấng ng nhĐt c r ng buẻc chểng ngÔi vêt Vẳ ¥y l b i to¡n 135 NP-kh‚ vªy t¡c giÊ tiáp cên theo phẽng phĂp giÊi xĐp x v ã xuĐt hai giÊi thuêt tiảu biu lểp cĂc thuêt toĂn meta-heuristic (GA, PSO) giÊi quyát Ănh gi¡ £nh h˜ ng cıa c¡c y¸u tË tĨi k¸t quÊ ca b i toĂn TĂc giÊ Â xƠy dáng cĂc kch bÊn mÔng ph thuẻc v o tng mc ẵch trin khai mÔng Kát quÊ thác nghiằm cho thĐy hai giÊi thuêt ã xuĐt cho kát quÊ tật vã diằn tẵch bao ph Qua thác nghiằm cng chng minh ềc sá ph hềp ca giÊi thuêt ã xuĐt cho b i to¡n °t VÓi b i to¡n bao phı Ëi t˜Ịng t¡c gi£ · xu§t hai b i to¡n: b i to¡n bao phı Ëi t˜Òng £m b£o kát nậi v chu lẩi mÔng cÊm bián khấng dƠy vểi sậ lềng cÊm bián trin khai l tậi thiºu v b i to¡n bao phı Ëi t˜Òng £m bÊo tẵnh kát nậi mÔng cÊm bián khấng dƠy c‚ s˚ dˆng c¡c iºm thu ph¡t di Ỵng C£ hai bai to¡n · xu§t ˜Ịc ch˘ng minh l b i toĂn NP-kh v ã xuĐt cĂc giÊi thuêt heuristic (USP, UTSP, PGA v SGA) º gi£i quy¸t T¡c gi£ luên Ăn  xƠy dáng thác nghiằm Ănh gi¡ t¯ng y¸u tË b i to¡n £nh h˜ ng ¸n k¸t qu£ cıa b i to¡n T¯ ‚, gip cho cĂc nh trin khai mÔng cƠn nhc quyát ‡nh t¯ng ˘ng dˆng cˆ thº n¶n triºn khai mÔng cÊm bián nh thá n o cho tiát kiằm chi phẵ v thèi gian thác hiằn HÔn chá ca luên Ăn CĂc b i toĂn ềc giÊi quyát luªn ¡n ·u l c¡c b i to¡n NPkh‚ Do , náu thảm nhiãu r ng buẻc ca b i toĂn thẳ rĐt kh giÊi quyát Vẳ vêy, luên Ăn văn cãn nhng hÔn chá sau: Luên Ăn mểi ch quan tƠm án mấ hẳnh cÊm bián ắa nh phƠn cha giÊi quyát vểi mấ hẳnh cÊm bián quÔt nh phƠn v mấ hẳnh suy giÊm Vểi b i toĂn bao ph diằn tẵch, tĂc giÊ mểi quan tƠm án vĐn ã bao ph cha quan tƠm án vĐn ã truyãn tin ca cĂc nt cÊm bián Hểng nghiản cu tiáp Trong nhng nghiản cu tiáp theo, tĂc giÊ tiáp tc m rẻng nghiản cu vã cĂc vĐn ã: Vểi vĐn ã bao ph diằn tẵch mÔng cÊm bián khấng dƠy c r ng buẻc chểng ngÔi vêt: TĂc giÊ s m rẻng nghiản cu giÊi quyát b i toĂn vểi chểng ngÔi vêt l hẳnh dÔng bĐt k TĂc giÊ s xem xt án yáu tậ nông lềng ca cĂc nt cÊm bián nhơm ko d i thèi gian sậng ca mÔng 136 Vểi vĐn ã bao ph ậi tềng Êm bÊo kát nậi mÔng cÊm bián khÊng d¥y c‚ s˚ dˆng c¡c iºm thu ph¡t di ẻng TĂc giÊ s m rẻng nghiản cu vã tẵnh ch‡u lÈi cıa b i to¡n nhm mˆc ti¶u tËi thiºu h‚a sË l˜Òng nÛt s˚ dˆng v k²o d i thèi gian sậng ca mÔng giÊm thiu chi phẵ xƠy dáng mÔng 137 DANH MữC CặNG TRNH CặNG Bẩ CĂc cấng trẳnh  cấng bậ ca tĂc gi£ luªn ¡n: Nguyen Thi Hanh, Nguyen Hai Nam, Huynh Thi Thanh Binh, 2016, Swarm Optimization Algorithms for Maximizing Area Coverage in Wireless Sensor Networks, Proceedings of SAI Intelligent Systems Conference (IntelliSys), pp 1145-1151 Nguyen Thi Hanh, Le Quoc Tung, Nguyen Thanh Hai, Huynh Thi Thanh Binh, Ernest Kurniawan, 2016, Connectivity Optimization Problem in Vehicular Mobile Wireless Sensor Networks, International Conference on Com-putational Intelligence and Cybernetics, pp 55-61 Nguyen Thi Hanh, Phan Hong Hanh, Huynh Thi Thanh Binh, Nguyen Duc Nghia, 2016, Heuristic Algorithm for Target Coverage with Connectivity Fault-tolerance Problem in Wireless Sensor Networks, Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp 235-240 Nguyen Thi Hanh, Nguyen Phi Le, Phan Thanh Tuyen, Ernest Kurniawan, Yusheng Ji, Huynh Thi Thanh Binh, 2018, Node Placement for Target Cov-erage and Network Connectivity in WSNs with Multiple Sinks, IEEE Con-sumer Communications and Networking Conference - CCNC, Las Vegas, NV, USA, pp 1-6 Huynh Thi Thanh Binh, Nguyen Thi Hanh, La Van Quan, Nilanjan Dey, 2018, Improved Cuckoo Search and Chaotic Flower Pollination Algorithms for Maximizing Area Coverage in Wireless Sensor Networks, Neural Com-puting and Applications) October 2018, Volume 30, Issue 7, pp 23052317, 2018, (SCI-E Index, IF: 4.664) Nguyen Thi Hanh, Huynh Thi Thanh Binh, Nguyen Xuan Hoai, Marimuthu Swami Palaniswami, 2019, An Efficient Genetic Algorithm for Maximizing Area Coverage in Wireless Sensor Networks, Journal Information Sciences, Volume 488, pp.58-75, 2019, (SCI-E Index, IF: 5.524) 138 Nguyen Thi Hanh, Huynh Thi Thanh Binh, Nguyen Van Son, Phan Ngoc Lan, 2019, Minimal Node Placement for Ensuring Target Coverage with Network Connectivity and Fault Tolerance Constraints in Wireless Sensor Networks, 2019 IEEE Congress on Evolutionary Computation Conference (CEC 2019), pp.2924-2931, 2019 Nguyen Phi Le, Nguyen Thi Hanh, Nguyen Tien Khuong, Huynh Thi Thanh Binh, Yusheng Ji, 2019, Node placement for connected target cov-erage in wireless sensor networks with dynamic sinks, Journal Pervasive and Mobile Computing, Volume 59, pp 1-21, 2019 (SCI, IF: 2.769) C¡c cÊng trẳnh cấng bậ khĂc c liản quan: Dinh Thi Ha Ly, Nguyen Thi Hanh, Huynh Thi Thanh Binh, Nguyen Duc Nghia, An Improved Genetic Algorithm for Maximizing Area Coverage in Wireless Sensor Networks, SoICT 2015, pp 61-66 Le Khac Tuan, Nguyen Hai Nam, Nguyen Thi Hanh, Huynh Thi Thanh Binh, Integrated and heuristic methods for maximizing the lifetime of wire-less sensor networks with optimal base station location for disaster forecast, R10-HTC-2015, pp.1-6 (Best paper award) 139 TI LIU THAM KHO [1] Ian F Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci Wireless sensor networks: a survey Computer networks, 38(4): 393422, 2002 [2] Z Chen, X Gao, F Wu, and G Chen A ptas to minimize mobile sensor movement for target coverage problem In IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communica-tions, pages 19, April 2016 [3] Hande Alemdar and Cem Ersoy Wireless sensor networks for healthcare: A survey Comput Netw., 54(15):26882710, October 2010 ISSN 1389-1286 [4] Aqeel ur Rehman, Abu Zafar Abbasi, Noman Islam, and Zubair Ahmed Shaikh A review of wireless sensors and networks' applications in agricul-ture Computer Standards & Interfaces , 36(2):263 270, 2014 [5] Feng Xia, Laurence T Yang, Lizhe Wang, and Alexey Vinel Internet of things Int J Commun Syst., 25(9):11011102, September 2012 ISSN 1074-5351 doi: 10.1002/dac.2417 URL http://dx.doi.org/10.1002/ dac.2417 [6] Dazhi Chen and Pramod K Varshney Qos support in wireless sensor net-works: A survey In International conference on wireless networks , volume 233, pages 17, 2004 [7] Zhao Cheng, Mark Perillo, and Wendi B Heinzelman General network life- time and cost models for evaluating sensor network deployment strategies IEEE Transactions on mobile computing , 7(4):484497, 2008 [8] Al-Sakib Khan Pathan, Hyung-Woo Lee, and Choong Seon Hong Security in wireless sensor networks: issues and challenges In Advanced Communi- cation Technology, 2006 ICACT 2006 The 8th International Conference , volume 2, pages 6pp IEEE, 2006 [9] Chuan Zhu, Chunlin Zheng, Lei Shu, and Guangjie Han A Journal of survey on coverage and connectivity issues in wireless sensor networks Network and Computer Applications , 35(2):619632, 2012 140 [10] Kemal Akkaya and Mohamed Younis A survey on routing protocols for wireless sensor networks Ad hoc networks, 3(3):325349, 2005 [11] Bang Wang Coverage problems in sensor networks: A survey ACM Com- puting Surveys (CSUR), 43(4):32, 2011 [12] Anju Sangwan and Rishi Pal Singh Survey on coverage problems in wireless sensor networks Wireless Personal Communications , 80(4):1475 1500, 2015 [13] Shancang Li, Li Da Xu, and Shanshan Zhao The internet of things: a survey Information Systems Frontiers , 17(2):243259, 2015 [14] Li Deying and Hai Liu Wireless networks: Research, technology and ap- plications, chapter sensor coverage in wireless sensor networks, 2009 [15] Weiyi Zhang, Guoliang Xue, and Satyajayant Misra Fault-tolerant re-lay node placement in wireless sensor networks: Problems and algorithms In INFOCOM 2007 26th IEEE International Conference on Computer Communications IEEE, pages 16491657 IEEE, 2007 [16] Mohamed F Younis and Kemal Akkaya Strategies and techniques for node placement in wireless sensor networks: A survey Ad Hoc Networks, 6:621655, 2008 [17] H Salarian, K W Chin, and F Naghdy An energy-efficient mobilesink path selection strategy for wireless sensor networks IEEE Transactions on Vehicular Technology , 63(5):24072419, Jun 2014 [18] Mirela Marta and Mihaela Cardei Improved sensor network lifetime with multiple mobile sinks Pervasive and Mobile Computing , 5(5):542 555, 2009 [19] Y Yoon and Y H Kim An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks IEEE Transactions on Cybernetics, 43(5):14731483, Oct 2013 [20] Jon S Wilson Sensor technology handbook Elsevier, 2004 [21] John E Brignell and Neil M White Intelligent Sensor Systems: 2nd in-stitute of Physics Publishing, 1996 [22] Bang Wang Coverage control in sensor networks Springer Science & Business Media, 2010 [23] Anish Arora, Prabal Dutta, Sandip Bapat, Vinod Kulathumani, Hongwei Zhang, Vinayak Naik, Vineet Mittal, Hui Cao, Murat Demirbas, Mohamed Gouda, et al A line in the sand: a wireless sensor network for target detection, classification, and tracking Computer Networks, 46(5):605634, 2004 141 [24] Guang-Zhong Yang and Guangzhong Yang Body sensor networks, vol-ume Springer, 2006 [25] S Mini, Siba K Udgata, and Samrat L Sabat Sensor deployment and scheduling for target coverage problem in wireless sensor networks IEEE Sensors Journal, 14(3):636644, 2014 [26] C Y Chang, C T Chang, Y C Chen, and H R Chang Obstacleresistant deployment algorithms for wireless sensor networks IEEE Trans-actions on Vehicular Technology , 58(6):29252941, July 2009 ISSN 0018-9545 doi: 10.1109/TVT.2008.2010619 [27] Yong Xu and Xin Yao A ga approach to the optimal placement of sen-sors in wireless sensor networks with obstacles and preferences In CCNC 2006 2006 3rd IEEE Consumer Communications and Networking Con-ference, 2006., volume 1, pages 127131, Jan 2006 doi: 10.1109/CCNC 2006.1593001 [28] Haisheng Tan, Yuexuan Wang, Xiaohong Hao, Qiang-Sheng Hua, and Francis C M Lau Arbitrary obstacles constrained full coverage in wire-less sensor networks In Gopal Pandurangan, V S Anil Kumar, Gu Ming, Yunhao Liu, and Yingshu Li, editors, Wireless Algorithms, Systems, and Applications, pages 110, Berlin, Heidelberg, 2010 Springer Berlin Heidel-berg ISBN 978-3-642-14654-1 [29] Jae-Hyun SEO, Yong-Hyuk KIM, Hwang-Bin RYOU, Si-Ho CHA, and Minho JO Optimal sensor deployment for wireless surveillance sensor networks by a hybrid steady-state genetic algorithm IEICE Transactions on Communications, E91.B(11):35343543, 2008 doi: 10.1093/ietcom/ e91-b.11.3534 [30] Andrew Howard, Maja J Mataric, and Gaurav S Sukhatme Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem In Distributed Autonomous Robotic Systems 5, pages 299308 Springer, 2002 [31] Yi Zou and Krishnendu Chakrabarty Sensor deployment and target localization in distributed sensor networks ACM Transactions on Embedded Computing Systems (TECS), 3(1):6191, 2004 [32] Yao Zou and Krishnendu Chakrabarty Sensor deployment and target localization based on virtual forces In INFOCOM 2003 Twenty-Second Annual Joint Conference of the IEEE Computer and Communications IEEE Societies, volume 2, pages 12931303 IEEE, 2003 [33] Colin R Reeves and Jonathan E Rowe Genetic algorithms: Principles and perspectives, vol 20 of Operations Research/Computer Science Interfaces Series, 2003 142 [34] Qin Xu and Qianping Wang Coverage optimization deployment based on virtual force directed in wireless sensor networks In International Con- ference on Computer Technology and Science ICCTS , 2012 [35] Ines Khoufi, Pascale Minet, and Anis Laouiti Oa-dvfa: A distributed virtual forces-based algorithm to monitor an area with unknown obstacles In Consumer Communications & Networking Conference (CCNC), 2016 13th IEEE Annual, pages 10361041 IEEE, 2016 [36] Marco Locatelli and Ulrich Raber Packing equal circles in a square: a deterministic global optimization approach Discrete Applied Mathematics , 122(1-3):139166, 2002 [37] Ines Khoufi, Pascale Minet, Anis Laouiti, and Erwan Livolant A simple method for the deployment of wireless sensors to ensure full coverage of an irregular area with obstacles In Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, pages 203210 ACM, 2014 [38] Haisheng Tan, Yuexuan Wang, Xiaohong Hao, Qiang-Sheng Hua, and Francis CM Lau Arbitrary obstacles constrained full coverage in wire-less sensor networks In International Conference on Wireless Algorithms, Systems, and Applications, pages 110 Springer, 2010 [39] Nguy¹n ẳnh Thc Lêp trẳnh tián ha, nh xuĐt bÊn giĂo dˆc 2001 [40] Dinh Thi Ha Ly, Nguyen Thi Hanh, Huynh Thi Thanh Binh, and Nguyen Duc Nghia An improved genetic algorithm for maximizing area coverage in wireless sensor networks In SoICT, 2015 [41] Salma Begum, Nazma Tara, and Sharmin Sultana Energyefficient target coverage in wireless sensor networks based on modified ant colony algo-rithm International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol, 1:2936, 2010 [42] Omar Banimelhem, Moad Mowafi, and Walid Aljoby Genetic algorithm based node deployment in hybrid wireless sensor networks Communica-tions and Network, 5(04):273, 2013 [43] Van-Dai Ta, Shih-Chang Huang, and Huynh Thi Thanh Binh Cover-ing the target objects with mobile sensors by using genetic algorithm in wireless sensor networks JCP, 10(5):300308, 2015 [44] Qun Zhao and Mohan Gurusamy Lifetime maximization for connected target coverage in wireless sensor networks IEEE/ACM Transactions on Networking (TON), 16(6):13781391, 2008 143 [45] Qun Zhao and Mohan Gurusamy Maximizing network lifetime for connected target coverage in wireless sensor networks In Wireless and Mobile Computing, Networking and Communications, 2006.(WiMob'2006) IEEE International Conference on , pages 94101 IEEE, 2006 [46] Deying Li, Jiannong Cao, Ming Liu, and Yuan Zheng K-connected target coverage problem in wireless sensor networks In International Conference on Combinatorial Optimization and Applications , pages 2031 Springer, 2007 [47] Koushik Kar and Suman Banerjee Node placement for connected coverage in sensor networks In WiOpt'03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks , pages 2pages, 2003 [48] Javad Rezazadeh, Marjan Moradi, and Abdul Samad Ismail Mobile wireless sensor networks overview International Journal of Computer Com- munications and Networks, 2(1):1722, 2012 [49] Joshua Reich, Vishal Misra, Dan Rubenstein, and Gil Zussman Connec- tivity maintenance in mobile wireless networks via constrained mobility IEEE Journal on Selected Areas in Communications , 30(5):935950, 2012 [50] Uichin Lee and Mario Gerla A survey of urban vehicular sensing platforms Computer Networks, 54(4):527544, 2010 [51] Uichin Lee, Eugenio Magistretti, Mario Gerla, Paolo Bellavista, and Anto-nio Corradi Dissemination and harvesting of urban data using vehicular sensing platforms IEEE transactions on vehicular technology , 58(2):882 901, 2009 [52] Mohamed Amine Kafi, Yacine Challal, Djamel Djenouri, Messaoud Doudou, Abdelmadjid Bouabdallah, and Nadjib Badache A study of wireless sensor networks for urban traffic monitoring: applications and architectures Procedia computer science , 19:617626, 2013 [53] Giuseppe Anastasi, Marco Conti, and Mario Di Francesco An analyti-cal study of reliable and energy-efficient data collection in sparse sensor networks with mobile relays In European Conference on Wireless Sensor Networks, pages 199215 Springer, 2009 [54] Kunal Shah, Mario Di Francesco, Giuseppe Anastasi, and Mohan Kumar A framework for resource-aware data accumulation in sparse wireless sen-sor networks Computer Communications, 34(17):20942103, 2011 [55] Qingguo Zhang and Mable P Fok A two-phase coverage-enhancing algo-rithm for hybrid wireless sensor networks Sensors, 17(1):117, 2017 144 [56] Guo-Hui Lin and Guoliang Xue Steiner tree problem with minimum num-ber of steiner points and bounded edgelength Information Processing Letters, 69(2):53 57, 1999 [57] Dejun Yang, Satyajayant Misra, Xi Fang, Guoliang Xue, and Junshan Zhang Two-tiered constrained relay node placement in wireless sensor networks: Computational complexity and efficient approximations IEEE Transactions on Mobile Computing , 11(8):13991411, 2012 [58] Zhiyin Chen, Xiaofeng Gao, Fan Wu, and Guihai Chen A ptas to minimize mobile sensor movement for target coverage problem In INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Com-munications, IEEE, pages 19 IEEE, 2016 [59] Yinying Yang, Mirela I Fonoage, and Mihaela Cardei Improving network lifetime with mobile wireless sensor networks Computer communications, 33(4):409419, 2010 [60] Mirela Marta and Mihaela Cardei Improved sensor network lifetime with multiple mobile sinks Pervasive and Mobile computing , 5(5):542555, 2009 [61] Athanasios Kinalis, Sotiris Nikoletseas, Dimitra Patroumpa, and Jose Rolim Biased sink mobility with adaptive stop times for low latency data collection in sensor networks Information fusion, 15:5663, 2014 [62] Saim Ghafoor, Mubashir Husain Rehmani, Sunghyun Cho, and Sung- Han Park An efficient trajectory design for mobile sink in a wireless sensor network Computers & Electrical Engineering , 40(7):20892100, 2014 [63] Hamidreza Salarian, Kwan-Wu Chin, and Fazel Naghdy An energy- efficient mobile-sink path selection strategy for wireless sensor networks IEEE Transactions on vehicular technology , 63(5):24072419, 2014 [64] Nguyen Thi Thanh Nga, Nguyen Kim Khanh, and Son Ngo Hong Entropy based correlation clustering for wireless sensor network in multi-correlated regional environment In 2016 International Conference on Electronics, Information, and Communications (ICEIC) , pages 14 IEEE, 2016 [65] Nguyen Thi Thanh Nga, Nguyen Kim Khanh, and Son Ngo Hong Entropy correlation and its impact on routing with compression in wireless sensor network In Proceedings of the Seventh Symposium on Information and Communication Technology , pages 235242 ACM, 2016 [66] Lả Trng Vắnh ng Thanh HÊi Tậi u vng ph sng ca mÔng cÊm bián khấng dƠy bơng thuêt toĂn voronoi mấi trèng 3d Ôi hc LÔt, 6(2):187196, 2016 145 [67] Nguyen Thi Tam, Dang Thanh Hai, et al Improving lifetime and network connections of 3d wireless sensor networks based on fuzzy clustering and particle swarm optimization Wireless Networks, 24(5):14771490, 2018 [68] Nguyen Thanh Tung and Huynh Thi Thanh Binh Base station locationaware optimization model of the lifetime of wireless sensor networks Mo- bile Networks and Applications , 21(1):1017, 2016 [69] Stephen Boyd and Lieven Vandenberghe Convex optimization Cambridge university press, 2004 [70] Xin-She Yang Cuckoo search and firefly algorithm: theory and applica-tions, volume 516 Springer, 2013 [71] c Nghắa Nguyạn and Tấ Th nh Nguyạn ToĂn rèi rÔc Ôi hc Quậc gia H Nẻi, 2007 [72] Alexander Schrijver Theory of linear and integer programming John Wiley & Sons, 1998 [73] Alexander Souza Combinatorial algorithms Lecture Notes, Winter Term 2010/2011, 2010 [74] Ailsa H Land and Alison G Doig An automatic method of solving dis-crete programming problems Econometrica: Journal of the Econometric Society, pages 497520, 1960 [75] Andries P Engelbrecht Computational Intelligence: An Introduction Wi-ley Publishing, 2nd edition, 2007 ISBN 0470035617 [76] Zbigniew Michalewicz and David B Fogel How to solve it: modern heuris-tics Springer Science & Business Media, 2013 [77] H Holland John Adaptation in natural and artificial systems: an intro-ductory analysis with applications to biology, control, and artificial intel-ligence USA: University of Michigan, 1975 [78] Ernst Mayr Populations, species, and evolution: an abridgment of animal species and evolution Harvard University Press, 1970 [79] J Kennedy and R Eberhart Optimization particle swarm In Proceedings of the 1995 IEEE International Conference on Evolutionary Computing , pages 19421948, 1995 [80] Yuhui Shi and Russell Eberhart A modified particle swarm optimizer In Evolutionary Computation Proceedings, 1998 IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on , pages 6973 IEEE, 1998 146 [81] Xin-She Yang and Suash Deb Cuckoo search via l²vy flights In Nature & Biologically Inspired Computing, 2009 NaBIC 2009 World Congress on , pages 210214 IEEE, 2009 [82] Rosario N Mantegna and H Eugene Stanley Stochastic process with ultra-slow convergence to a gaussian: the truncated l²vy flight Physical Review Letters, 73(22):2946, 1994 [83] Andy M Reynolds and Mark A Frye Free-flight odor tracking in drosophila is consistent with an optimal intermittent scale-free search PloS one, (4):e354, 2007 [84] Changlei Liu and Guohong Cao Spatial-temporal coverage optimization in wireless sensor networks IEEE Transactions on Mobile Computing , 10 (4):465478, 2011 [85] Celal Ozturk, Dervis Karaboga, and Beyza Gorkemli Probabilistic dy-namic deployment of wireless sensor networks by artificial bee colony al-gorithm sensors, 11(6):60566065, 2011 [86] D Henderson, SH Jacobson, and AW Johnson The theory and practice of simulated annealing handbook of metaheuristics f glover and ga kochen-berger, 2003 [87] Qinghai Bai Analysis of particle swarm optimization algorithm Computer and information science, 3(1):180, 2010 [88] Bahareh Nakisa and Mohammad Naim Rastgoo A survey: Particle swarm optimization based algorithms to solve premature convergence problem Journal of Computer Science , 10(9):17581765, 2014 [89] Ali Kaveh Advances in metaheuristic algorithms for optimal design of structures Springer, 2014 [90] Xin-She Yanga, Mehmet Karamanoglua, and Xingshi Heb Multi-objective flower algorithm for optimization a school of science and technology Mid-dlesex University, London NW4 4BT, UK school of Science, Xi'an Poly-technic University, Xi'an, PR China ICCS , 2013 [91] El-Ghazali Talbi Metaheuristics: from design to implementation , vol- ume 74 John Wiley & Sons, 2009 [92] Edmund K Burke, James P Newall, and Rupert F Weare Initialization strategies and diversity in evolutionary timetabling Evolutionary compu-tation, 6(1):81103, 1998 [93] Aki Sorsa, Riikka Peltokangas, and Kauko Leiviska Real-coded genetic algorithms and nonlinear parameter identification In Intelligent Systems, 147 2008 IS'08 4th International IEEE Conference , volume 2, pages 1042 IEEE, 2008 [94] Kusum Deep and Manoj Thakur A new crossover operator for real coded genetic algorithms Applied mathematics and computation , 188(1):895 911, 2007 [95] Aki Sorsa, Riikka Peltokangas, and Kauko Leiviska Real-coded genetic algorithms and nonlinear parameter identification In Intelligent Systems, 2008 IS'08 4th International IEEE Conference , volume 2, pages 1042 IEEE, 2008 [96] Y Zou and Krishnendu Chakrabarty Sensor deployment and target lo-calization based on virtual forces In IEEE INFOCOM 2003 Twenty- second Annual Joint Conference of the IEEE Computer and Communica-tions Societies (IEEE Cat No.03CH37428) , volume 2, pages 12931303 vol.2, March 2003 [97] Dorit S Hochbaum and Wolfgang Maass Approximation schemes for cov-ering and packing problems in image processing and vlsi Journal of the ACM (JACM), 32(1):130136, 1985 [98] Thomas H Cormen Section 24.3: Dijkstra's algorithm Introduction to algorithms, pages 595601, 2001 [99] Dorit S Hochbaum and Wolfgang Maass Approximation schemes for covering and packing problems in image processing and vlsi J ACM, 32 (1):130136, January 1985 ISSN 0004-5411 [100] James MacQueen et al Some methods for classification and analysis of multivariate observations In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , volume 1, pages 281297 Oak-land, CA, USA., 1967 [101] S Lloyd Least squares quantization in pcm IEEE Transactions on In-formation Theory, 28(2):129137, March 1982 [102] K Buchin and W Mulzer Delaunay triangulations in o(sort(n)) time and more In 2009 50th Annual IEEE Symposium on Foundations of Computer Science, pages 139148, Oct 2009 [103] J B Kruskal On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem In Proceedings of the American Mathematical Society, 7, 1956 148 ... tẵch bao ph ca hai giÊi thu c¡c k‡ch b£n 1,3 v PhƯn trôm diằn tẵch bao ph ca kch bÊn PhƯn trôm diằn tẵch bao ph ca kch bÊn PhƯn trôm diằn tẵch bao ph ca kch bÊn PhƯn trôm diằn tẵch bao. .. toĂn bao ph mÔng cÊm bián khấng dƠy CĂc yáu tậ Ênh h ng án vĐn ã bao ph ca mÔng cÊm bián khấng dƠy CĂc giÊi thuêt metaheuristic CĂc nghiản cu liản quan b i to¡n tËi ˜u h‚a bao phı di»n tẵch v bao. .. 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