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

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, 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