৘ใ௨৴ٕज़Λ༻͍ͨ
 ηϯγϯά ੴా ൟາ ۝भେֶ γεςϜ৘ใՊֶݚڀӃ ॿڭ Feb 7, 2019 @۝भେֶ–ISITݚڀަྲྀձ

ࢿྉ͸ҎԼͰऔಘͰ͖·͢ https://speakerdeck.com/ pman0214/real-world- sensing-with-ict

ੴా ൟາʢ͍ͩ͠ ͛͠Έʣ ■ ུྺ ● 2006೥3݄ ࣳӜ޻ۀେֶ޻ֶ෦ిࢠ޻ֶՊ ● 2008೥3݄ ౦ژେֶେֶӃ৽ྖҬ૑੒ՊֶݚڀՊम࢜ྃ ● 2008೥4݄ʙ2009೥9݄ (ג) ΞΫςΟε։ൃ৬ ● 2012೥4݄ʙ2013೥9݄ ֶৼ ಛผݚڀһDC2→PD ● 2012೥9݄ ౦ژେֶେֶӃ޻ֶܥݚڀՊത࢜ྃ (޻ത) ● 2013೥5݄ʙ9݄ ถϛωιλେ Visi;ng Scholar ● 2013೥10݄ʙ ݱ৬ ■ ઐ໳ ● ແઢηϯαωοτϫʔΫɼ࣮ۭؒηϯγϯάɼ԰಺ଌҐ 3

IoT: Internet of Things ■ ਎ͷճΓͷʮϞϊʯ͕Πϯλʔωοτʹͭͳ͕Δ ੈք ● Ϟϊ͕ηϯα΍ΞΫνϡΤʔλʹͳΓɼଞͷϞϊ ͱ࿈ܞͯ͠ศརͳαʔϏεΛ࣮ݱ ● ηϯγϯάσʔλΛղੳͯ͠ΞΫνϡΤʔγϣϯ 4

IoT͸σʔλղੳʁ 5

IoT͸σʔλղੳʁ 5 Ϗοάσʔλ ਓ޻஌ೳ ػցֶश ਂ૚ֶश

IoT͸σʔλղੳʁ 5 σʔλͷऔಘͩͬͯେࣄ Ϗοάσʔλ ਓ޻஌ೳ ػցֶश ਂ૚ֶश

Ξ΢τϥΠϯ ■ ແઢηϯαωοτϫʔΫ ■ ԰಺ଌҐٕज़ ● ҟछແઢΛ࢖ͬͨηϯαଌҐ ● νϟωϧ෼཭BLEଌҐ ■ ৽͍͠ηϯγϯάٕज़ ● ं྆ηϯγϯά • ԻڹΞϓϩʔν • ແઢΞϓϩʔν ● ԰֎ਓମηϯγϯά 6 ←ίί͕ࠓ೔ͷϝΠϯ

Ξ΢τϥΠϯ ■ ແઢηϯαωοτϫʔΫ ■ ԰಺ଌҐٕज़ ● ҟछແઢΛ࢖ͬͨηϯαଌҐ ● νϟωϧ෼཭BLEଌҐ ■ ৽͍͠ηϯγϯάٕज़ ● ं྆ηϯγϯά • ԻڹΞϓϩʔν • ແઢΞϓϩʔν ● ԰֎ਓମηϯγϯά 6 ଌҐٕज़ɼηϯγϯάٕज़Λ ۦ͚଍Ͱ঺հ ←ίί͕ࠓ೔ͷϝΠϯ

ແઢηϯαωοτϫʔΫ Wireless Sensor Network (WSN) 7

ແઢηϯαωοτϫʔΫ (WSN) ■ ηϯαʴແઢ௨৴ ● औಘͨ͠ηϯασʔλΛແઢ௨৴Ͱऩू ■ ྫʣεϚʔτ೶ۀɼεϚʔτϋ΢ε ● WSNʹΑΔ؀ڥ৘ใͷऔಘ͕ඞਢ 8

ηϯαϊʔυ ■ ௒খܕίϯϐϡʔλʴ௨৴ ● ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ແઢηϯαϊʔυMICAz

ηϯαϊʔυ ■ ௒খܕίϯϐϡʔλʴ௨৴ ● ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ແઢηϯαϊʔυMICAz

ηϯαϊʔυ ■ ௒খܕίϯϐϡʔλʴ௨৴ ● ۃΊͯඇྗɼػೳ΋গͳ͍ 9 εϚʔτϑΥϯ Moto Z Play ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ͜Εʢ۝େϩΰʣ = 5,619B ແઢηϯαϊʔυMICAz

ηϯαϊʔυ ■ ௒খܕίϯϐϡʔλʴ௨৴ ● ۃΊͯඇྗɼػೳ΋গͳ͍ 㱺 ͍͔ʹޮ཰ྑ͘࢖͏͔ 9 εϚʔτϑΥϯ Moto Z Play ηϯαϊʔυ MICAz CPU 8-core Cortex-A53, 64bit ATmega128L, 8bit ΫϩοΫप೾਺ 2.0 GHz 8 MHz ROM༰ྔ 32 GB 128 kB ϝϞϦ༰ྔ 3 GB 4 kB ͜Εʢ۝େϩΰʣ = 5,619B ແઢηϯαϊʔυMICAz

WSNʹ͓͚Δ՝୊ͷྫ ■ ηϯαϊʔυ͸Ͳ͜ʹઃஔ͞Ε͍ͯΔʁ ● શηϯαϊʔυͷҐஔΛखಈͰ؅ཧ͢Δͷ͸΄΅ෆ Մೳ 㱺 ଌҐٕज़ (ಛʹ԰಺͕໰୊) ■ ηϯγϯά͕೉͍͠৘ใΛͲ͏΍ͬͯऔಘ͢Δʁ ● ैདྷͷηϯαͰ೉͔ͬͨ͜͠ͱΛ࣮ݱ͢Δ৽͍͠η ϯγϯάٕज़ ● লిྗͳηϯγϯάٕज़ ※ ηϯαϊʔυͰಈ͔͘Ͳ͏͔͸࡞͔ͬͯΒߟ͑Δ 㱺 ৽͍͠ηϯγϯάٕज़ 10

԰಺ଌҐٕज़ Indoor LocalizaHon 11

AP1 = –48dBm AP2 = –70dBm : : QUERY Estimation Phase - ଌҐྖҬ֤ॴͰ৴߸ڧ౓Λଌఆ - ଌҐ࣌ʹ͸ଌҐ஍఺Ͱଌఆͨ͠ ৴߸ڧ౓ͱ΋ͬͱ΋ࣅ͍ͯΔ஍ ఺Λ୳ࡧͯ͠Ґஔਪఆ ԰಺ଌҐͷجຊ ■ 2ͭͷํࣜ 12 DB AP1 = –52dBm AP2 = –62dBm : : INSERT Training Phase ● ଟลଌྔ๏ ● ϑΟϯΨʔϓϦϯτ๏ - Ґஔͷ෼͔͍ͬͯΔج४ہ ͔Βͷڑ཭Λਪఆ - ෳ਺ج४ہ͔Βͷڑ཭ͰҐ ஔਪఆ

ηϯαϊʔυͷઃஔҐஔਪఆ ■ WiFi APͷ৴߸Λ࢖ͬͯηϯαΛଌҐ [1,2] ● ηϯαΛஔ͍͚ͨͩͰͲ͜ʹ͋Δ͔͕෼͔Δ ※ ηϯαϊʔυ͸ZigBee 㱺 WiFi৴߸Λड৴Ͱ͖ͳ͍ 13 RSS1=–40 RSS2=–50 RSS3=–45 RSS1=–60 RSS2=–54 RSS3=–42 AP 2 AP 1 AP 3 Sensor node Localiza;on Server WiFi Fingerprint DB ZigLoc Demo [1] S. Ishida et al., “WiFi AP-RSS Monitoring using Sensor Nodes toward Anchor-Free Sensor Localiza;on”, IEEE VTC-Fall, Sep 2015. [2] T. Yamamoto et al., “Accuracy improvement in sensor localiza;on system u;lizing heterogeneous wireless technologies”, ICMU, Oct 2017.

ଌҐٕज़ͷԠ༻ྫ (1) ■ BLEʢBluetooth Low Energyʣ ● iBeaconͳͲɼଌҐਫ਼౓͕ѱ͍ ● Separate Channel Fingerprin;ng • νϟωϧ෼཭BLEଌҐ [3] 14 Receiver MacBook Pro BLE Beacon BLED112 +Mobile ba7ery [3] S. Ishida et al., “Proposal of Separate Channel Fingerprin;ng Using Bluetooth Low Energy”, IIAI AAI, Jul 2016. Beacon1 RSS Beacon2 RSS Beacon3 RSS Fingerprints in DB Fingerprint x at unknown location Find nearest neighbor k Beacon1: –52dBm@ch37 Beacon1: –62dBm@ch38 Beacon1: –60dBm@ch39 Beacon2: –61dBm@ch37 Beacon2: –70dBm@ch38 Beacon2: –58dBm@ch39 38 Beacon1 Beacon2 Channel gain Frequency Frequency ch37 39

ଌҐٕज़ͷԠ༻ྫ (2) ■ ΦϯσϚϯυҐஔ৘ใαʔϏε [4] 15 RSS=–48 RSS=–52 RSS=–58 RSS=–55 WiFi AP Core AP WiFi Device Localization Server On-demand Loc Demo [4] S. Ishida et al., “On-Demand Indoor Loca;on-based Service using Ad-Hoc Wireless Posi;oning Network”, IEEE ICESS, Aug 2015.

ଌҐܭࢉͷ෼ࢄ ■ ΦϯσϚϯυҐஔ৘ใγεςϜ [5] ● Ϣʔβ͕૿Ճ͢Δͱॏ͘ͳΔ 㱺 BBϧʔλΛ࢖ͬͯ௨৴Ͱ෼ࢄ 16 Router WNDR4300 WiFi Mesh Node PCWL-0100 172.17.0.0/16 192.168.0.0/16 10.0.0.1 10.0.0.2 Router Localiza-on Servers WiFi APs Shuffle Map Reduce 192.168.0.1 192.168.0.2 dest 10.0.0.0/9 DNAT to 192.168.0.1 dest 10.128.0.0/9 DNAT to 192.168.0.2 NAT Rule WiFi APs Localiza.on Servers 10.0.0.0 : 10.127.0.0 10.128.0.0 : 10.255.255.255 [5] J. Kajimura, “Design of distributed calcula;on scheme using network address transla;on for ad-hoc wireless posi;oning network”, Springer CCIS, vol.760, ISIP Post Proc., Oct 2017.

ं྆ηϯγϯά Vehicle Sensing 17

എܠ ■ ITS (Intelligent Transporta;on System) ● ಓ࿏্ͷं྆ͷݕग़͸ॏཁͳج൫ٕज़ ͷ1ͭ ■ طଘͷं྆ݕग़ηϯα ● ϧʔϓίΠϧɼޫిηϯαɼ௒Ի೾ɼ ੺֎ઢͳͲ 㱺 ઃஔɾ؅ཧʹಓ࿏޻ࣄ͕ඞཁͰߴίετ 㱺 ݕग़ൣғ͕ڱ͍ͨΊೋྠंͷݕग़͕ࠔ೉ 㱺 ௿ίετ͔ͭं྆λΠϓʹΑΒͣߴਫ਼౓ʹं྆Λ ݕग़͢ΔγεςϜ͕ٻΊΒΕ͍ͯΔ 18

Իڹं྆ηϯγϯά ■ εςϨΦϚΠΫͰं྆ݕग़ [6] ● ं྆૸ߦԻͷ౸ୡ࣌ؒࠩΛඳ ͍ͨʮα΢ϯυϚοϓʯΛར༻ • ૸ߦं྆͸SࣈΧʔϒΛඳ͘ 㱺 SࣈΧʔϒΛݕग़͢Δ 19 Microphones Recorder 2-lane Road α΢ϯυϚοϓ [6] S. Ishida et al., “SAVeD: Acous;c Vehicle Detector with Speed Es;ma;on capable of Sequen;al Vehicle Detec;on”, IEEE ITSC, Nov 2018. Δtmax t Δt –Δtmax

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R

α΢ϯυϚοϓ ■ 2୆ͷϚΠΫͷं྆Իͷ࣌ؒࠩ ● ࣌ؒࠩͷมԽ
 = α΢ϯυϚοϓ Time t t Sound Delay 20 L R L R t = 1 c 8 < : s✓ x + D 2 ◆2 + L 2 s✓ x D 2 ◆2 + L 2 9 = ;

α΢ϯυϚοϓͷྫ ■ ϚΠΫؒͷڑ཭ D = 50 [cm] ● ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms] 21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s]

α΢ϯυϚοϓͷྫ ■ ϚΠΫؒͷڑ཭ D = 50 [cm] ● ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms] 21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s] ࠨ͔Βӈ ӈ͔Βࠨ

α΢ϯυϚοϓͷྫ ■ ϚΠΫؒͷڑ཭ D = 50 [cm] ● ౸ୡ࣌ؒࠩͷ࠷େ஋͸໿1.47 [ms] 21 -1.5 -1 -0.5 0 0.5 1 1.5 30 35 40 45 50 Sound delay ∆t [ms] Time t [s] ࠨ͔Βӈ ӈ͔Βࠨ RANSACͰSࣈΧʔϒΛݕग़ 㱺 Ϟσϧ͔ࣜΒ଎౓΋ਪఆ t = 1 c 8 < : s✓ x + D 2 ◆2 + L 2 s✓ x D 2 ◆2 + L 2 9 = ;

α΢ϯυϚοϐϯά σϞ 22 Real Sound Mapping Demo

Ԡ༻ ■ লిྗԽ ● Waveletม׵Λ࢖ͬͨলిྗं྆ݕग़ख๏ [7] ■ ϊΠζରࡦ ● ఆৗϊΠζՃࢉʹΑΔϊΠζ෼཭ [8] ● ෩ϊΠζରࡦ [9] ■ ྻंݕग़ ● ྻं૸ߦԻΛػցֶशͯ͠ݕग़ [10] ● ྻं৐ंҐஔਪఆ [11] 23 [7] ٱอ ଞ, “཭ࢄ΢ΣʔϒϨοτม׵Λ༻͍ͨলϦιʔεं྆ݕग़γεςϜͷઃܭͱධՁ”, IPSJ ITSݚڀձ, Feb-Mar, 2019. (will appear) [8] ཥ ଞ, “ϚΠΫΛ༻͍ͨं྆ݕग़γεςϜʹ͓͚Δ؀ڥϊΠζ࡟ݮख๏ͷఏҊ”, IEICE ASNݚڀձ, Jan 2019. [9] M. Uchino et al., “Ini;al design of acous;c vehicle detector with wind noise suppressor”, PerVehicle, Mar 2019 (will appear) [10] K. Sato et al., “Proposal of acoustic train detection system for crowdsensing”, ITS-AP Fukuoka Post Proc, 2019 (will appear) [11] ࠤ౻ ଞ, “ϚΠΫϩϑΥϯΛ༻͍ͨమಓ৐ंҐஔਪఆख๏ͷઃܭͱධՁ”, IPSJ ITSݚڀձ, Feb-Mar 2019 (will appear)

ԻڹΞϓϩʔνͷݶք ■ ࣭ٙʹͯ ● ʮࣗసं͸ݕग़Ͱ͖ͳ͍ͷʁʯ ● ʮาߦऀ͸ݕग़Ͱ͖ͳ͍ͷʁʯ 24

ԻڹΞϓϩʔνͷݶք ■ ࣭ٙʹͯ ● ʮࣗసं͸ݕग़Ͱ͖ͳ͍ͷʁʯ ● ʮาߦऀ͸ݕग़Ͱ͖ͳ͍ͷʁʯ ■ ͜ͷΑ͏ͳ࣭໰Λ͞ΕΔ͕ɼݪཧతʹࠔ೉ ● ͦ΋ͦ΋ൃͤΒΕΔԻ͕খ͍͞ ● ଎౓͕஗͍ͷͰα΢ϯυϚοϓ্Ͱͷղੳ͕ࠔ೉ 24

طଘͷंɼࣗసंɼาߦऀݕग़ ■ ं ● ϧʔϓίΠϧɼϨʔβɼ௒Ի೾ ■ ࣗసंɾาߦऀ [12,13] ● Χϝϥ΍Ϩʔβڑ཭ܭ (LiDAR)ʹΑΔݕग़ ■ ઃஔʹ͸ಓ࿏޻ࣄ͕ඞཁ ● ಋೖɾ؅ཧίετ͕ߴ͍ 㱺 ௿ίετͳηϯα͕ٻΊΒΕ͍ͯΔ 25 [12] F. García et al., “Context aided pedestrian detec;on for danger es;ma;on based on laser scanner and computer vision”, Expert Systems with Applica;ons, Nov 2014. [13] P. Dollár et al., “Pedestrian detec;on: an evalua;on of the state of the art”, IEEE Trans. Parern Analysis and Machine Intelligence, Apr 2012.

■ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά ● ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ৽͍͠Ξϓϩʔν 26

■ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά ● ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ճંɾ൓ࣹͯ͠౸ୡ ৽͍͠Ξϓϩʔν 26

■ WiFi௨৴ͷ఻ൖ࿏ͷมԽʹΑΔηϯγϯά ● ंɼࣗసंɼਓͰճંɾ൓ࣹ͕ൃੜ ճંɾ൓ࣹͯ͠౸ୡ ৽͍͠Ξϓϩʔν 26 ো֐෺ͷҐஔ΍େ͖͞ ʹΑͬͯճંɼ൓ࣹঢ় ଶ͸มԽ

ؔ࿈ݚڀ: ԰಺ແઢηϯγϯά ■ E-eyes [14] ● ԰಺ʹ͓͍ͯ఻ൖ࿏ͷมԽͰਓؒͷߦಈΛਪఆ ■ Smokey [15] ● NLOS (Non Line-of-Sight)؀ڥͰ٤Ԏಈ࡞Λݕग़ ■ RF-Pose [16] ● นӽ͠ʹෳ਺ਓͷ࢟੎Λਪఆ (ઐ༻ແઢػΛ࢖༻) ■ SignFi [17] ● WiFiͷ఻ൖ࿏มԽͰख࿩Λೝࣝ 27 [14] Y. Wang et al., “E-eyes: In-home device-free ac;vity iden;fica;on using fine-grained WiFi signatures”, ACM MobiCom, Sep 2014. [15] X. Zheng et al., “Smokey: Ubiquitous smoking detec;on with commercial WiFi infrastructures”, IEEE INFOCOM, Jul 2016. [16] M. Zhao et al., “Through-wall human pose es;ma;on using radio signals”, CVPR, Jun 2018. [17] Y. Ma et al., “SignFi: sign language recogni;on using WiFi”, ACM IMWUT, Mar 2018.

■ ACT-Iʮ৘ใͱະདྷʯʹ࠾୒ʢ2018೥10݄ʙʣ ● ແઢ௨৴Λ༻͍ͨं྆ɾࣗసंɾาߦऀݕग़ٕज़ ● ʮʙʢલུʣʙຊݚڀͷ໨త͸ɺࢢൢ ͷແઢ௨৴ػثΛվ଄͢Δ͜ͱͳ͘༻ ͍ͯं྆΍ࣗసंɺาߦऀΛݕग़͢Δ γεςϜΛ։ൃ͢Δ͜ͱͰ͢ɻແઢ௨ ৴͸पғͷ؀ڥมԽͷӨڹΛड͚Δ͜ ͱ͔Βɺແઢ௨৴͕ड͚ͨӨڹΛղੳ ͢Δ͜ͱͰं྆ɺࣗసंɺาߦऀͷݕ ग़Λ࣮ݱ͠·͢ɻʯ 㱺 ݚڀΛ։࢝ͨ͠͹͔ΓͷͨΊ੒Ռͷ͘͘͝͝Ұ෦Λ ঺հ ઓུత૑଄ݚڀਪਐࣄۀ 28 ग़ల: ACT-I 平成30年度採択課題

WiFiΛ༻͍ͨं྆ݕग़ [18] ■ ۝େ಺ͷಓ࿏Ͱ࣮ݧ ● ಓ࿏ͷ྆ଆʹૹड৴ػΛઃஔ ● ఻ൖ࿏৘ใΛղੳͯ͠௨աं྆Λݕग़ 29 TX RX [18] M. Cong et al., “Proposal of On-road Vehicle Detection Method Using WiFi Signal”, IPSJ ITSݚڀձ, Feb-Mar 2019 (will appear)

WiFiʹΑΔं྆ݕग़ͷ֓ཁ 1. WiFi (802.11n)୺຤ؒͰ50msִؒͰ௨৴ 2. ఻ൖ࿏৘ใͷҐ૬৘ใΛநग़ 3. ิਖ਼΍ϊΠζআڈͳͲ্ͨ͠Ͱಛ௃ྔΛநग़ ● Window಺ͷ࠷େ஋ɼ࠷খ஋ɼ෼ࢄͳͲ 4. ػցֶश 㱺 ं྆छผ͝ͱʹݕग़Ͱ͖Δ͔Λࢼߦ ● େܕंɼόϯɼී௨ंɼখܕंͷ4छผ 30 Phase Extraction Phase Calibration PCA Feature Extraction Machine Learning Windowing

■ ं྆छผ͝ͱͷݕग़ (ͨͩ͠unbalanced data) ● Precision = 54.4%, Recall = 48.5%, Accuracy = 55.3% 㱺 ୯७ͳݕग़ํ๏Ͱ͸શવμϝ (Work in Progress) ं྆ݕग़݁Ռ 31 EsHmated Actual େܕं όϯ ී௨ं খܕं େܕं 9 0 2 0 όϯ 2 3 5 0 ී௨ं 1 0 14 2 খܕं 1 0 8 0

԰֎ਓମηϯγϯά Outdoor Human Sensing 32

԰֎ਓମηϯγϯά [19] ■ WiFi (802.11ac)Λ࢖ͬͨ԰֎ਓମηϯγϯά ● ηϯγϯάྖҬ಺ʹਓ͕͍Δ͔Λਪఆ ● ं྆ݕग़ͱ΄΅ಉ͡࢓૊Έ (ͨͩ͠802.11acΛ࢖༻) 33 Training Phase Estimation Phase Data Acquisition Block Pre-Process Block Machine Learning Block Machine Learning Block AP CSI monitoring station CSI measuring station Target area Human Location Actual human location Angles ij, ij AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq AAAB/nicdVDLSsNAFL2pr1pf8bFzM1gKLkpJ3Oiy4MZlBfuAJoTJdNKOnUzCzESooeCnuBIUxK3/4cq/cfpQWsUDFw7n3Mu5nDDlTGnH+bQKK6tr6xvFzdLW9s7unr1/0FJJJgltkoQnshNiRTkTtKmZ5rSTSorjkNN2OLyc+O07KhVLxI0epdSPcV+wiBGsjRTYR146YEHObsdV5KVqRgO77NacKZDzh3xbZZijEdgfXi8hWUyFJhwr1XWdVPs5lpoRTsclL1M0xWSI+7RrqMAxVX4+/X6MKkbpoSiRZoRGU3XxIsexUqM4NJsx1gP125uIP15lKUpHF37ORJppKsgsKco40gmadIF6TFKi+cgQTCQzzyIywBITbRorLbbwP2md1Vyn5l475Xp13kcRjuEETsGFc6jDFTSgCQTu4RGe4cV6sJ6sV+tttlqw5jeHsATr/Qsol5Vq [19] M. Miyazaki et al., “Ini;al arempt on outdoor human detec;on using IEEE 802.11ac WLAN signal”, IEEE SAS, Mar 2019. (will appear)

࣮ݧ؀ڥ ■ 9ͭͷΤϦΞ + ਓͳ͠ (ϥϕϧ0)Λ
 ଟΫϥε෼ྨ໰୊ͱͯ͠ػցֶश 34 CSI measuring station CSI monitoring station AP 4 1 7 5 2 8 6 3 9 30 meters 30 meters 6 meters 6 meters 0 : no human STA1 STA2 ΤϦΞ ൪߸

■ ਫ਼౓ (ਖ਼͍͠ݕग़ͷׂ߹) ● 99.86% (STA2ͷΈ࢖ͬͨ৔߹) ● 56.01% (STA1ͷ Έ࢖ͬͨ৔߹) ݕग़݁Ռ 35 0 1 2 3 4 5 6 7 8 9 Estimated Area 0 1 2 3 4 5 6 7 8 9 Actual Area 547623 50 1 11 15 0 0 0 0 0 7 551917 27 0 330 0 19 0 0 0 0 69 550299 368 1552 4 0 8 0 0 3 0 520 546006 18 297 42 114 0 0 0 30 1711 36 550511 0 112 0 0 0 3 0 6 566 0 547076 0 577 47 25 3 0 5 45 221 0 546626 0 0 0 0 0 1 92 0 703 4 546500 0 0 0 0 0 17 0 1 5 11 546966 0 0 0 0 0 0 5 0 0 2 553393

·ͱΊ 36

·ͱΊ ■ IoT࣮ݱʹ޲͚ͯ͸ηϯγϯά΋େ੾ ■ ࣮ۭؒ৘ใΛऔಘ͢ΔແઢηϯαωοτϫʔΫ ● Ͳ͜ʹஔ͍ͨͷ͔஌Βͳ͍ͱ͍͚ͳ͍ ● ηϯγϯάର৅Λ֦େ͍ͨ͠ ■ ԰಺ଌҐٕज़ ● ҟछແઢؒ௨৴Λ༻͍ͨηϯαଌҐ ● νϟωϧؒಛੑࠩΛར༻ͨ͠BLEଌҐ ■ ৽͍͠ηϯγϯάٕज़ ● ं྆ηϯγϯά (Ի, ແઢ [Work in Progress]) ● ਓମηϯγϯά (ແઢ) 37

Thank you!! QuesHons & Answers 38

© 2019 Shigemi ISHIDA licensed under CC BY-NC 4.0