車両によるDTN通信のための適応型メッセージ抑制手法

Title

車両によるDTN通信のための適応型メッセージ抑制手法

Author(s)

吉野　悠, 中崎　省吾, 池田　誠

Citation

福岡工業大学総合研究機構研究所所報　第1巻 　P47-P51

Issue Date

2018-12

URI

http://hdl.handle.net/11478/1220

Right

Type

Departmental Bulletin Paper

Textversion Publisher

福岡工業大学　機関リポジトリ　

FITREPO

ं྆ʹΑΔ

_DTN

௨৴ͷͨΊͷదԠܕϝοηʔδ཈੍ख๏

٢໺ ༔

(

޻ֶݚڀՊम࢜՝ఔ৘ใ௨৴޻ֶઐ߈

)

த࡚ লޗ

(

޻ֶݚڀՊम࢜՝ఔ৘ใ௨৴޻ֶઐ߈

)

஑ా ੣

₍

৘ใ޻ֶ෦৘ใ௨৴޻ֶՊ

₎

An Adaptive Message Suppression Method for Vehicular DTN

Yu YOSHINO (Master’s Program of Communication and Information Networking, Graduate School of Engineering) Shogo NAKASAKI (Master’s Program of Communication and Information Networking, Graduate School of Engineering)

Makoto IKEDA (Department of Information and Communication Engineering, Faculty of Information Engineering)

Abstract

In Vehicular Delay/Disruption/Disconnection Tolerant Networking (DTN), network resource consumption becomes a critical problem due to the nodes replicate bundle messages to adjacent nodes. In our previous work, we have evalu-ated the network performance of our Message Suppression (MS) method for Vehicular DTN. In this paper, we consider a threshold-based adaptive method for MS in Vehicular DTN. We use delay and replicated bundle message as evalua-tion metrics. From the simulation results, we observe that our proposed method decreases the replicated bundle messages.

Keywords

Vehicular DTN, DTN, message suppression, bundle message.

1.

·͕͖͑

ۙ೥ɼਓʑͷ໋ྩ΍ߦಈΛֶशͯࣗ͠཯ಈ࡞͢ΔՈ ిػثɼϩϘοτɼं྆ͳͲͷ༷ʑͳ෼໺ʹਓ޻஌ೳͷ ٕज़͕Ԡ༻͞Ε͍ͯΔɽ͜ͷதͰं྆ʹؔ͢Δ෼໺Ͱ ͸ɼ҆શͳಓ࿏ަ௨؀ڥͷఏڙɼަ௨ौ଺ͷղফɼं ྆Λ༻͍ͨ৽ͨͳ௨৴ख๏ͷཱ֬ͳͲͷ՝୊͕͋Δ(1)_ɽ ं྆ؒͰͷ௨৴ख๏Ͱ͸طଘͷϚϧνϗοϓωοτ ϫʔΫͰར༻͞Ε͍ͯΔܦ࿏୳ࡧख๏Ͱ͸Ѽઌ·Ͱϝο ηʔδΛૹΔ͜ͱ͸ࠔ೉Ͱ͋Δ(2)_{ɽ͜Ε͸ɼं྆ͷҠ} ಈ଎౓ʹΑΓɼྡ઀୺຤ʢं྆ʣ͕සൟʹมԽ͢Δ͜ ͱͰ֤ं͕྆؅ཧ͍ͯ͠Δܦ࿏දͱϝοηʔδૹ৴࣌ ͷ࣮ࡍͷྡ઀ं྆ʹ͕ࠩൃੜͯ͠͠·͏͜ͱ͕ݪҼʹ ͳΔɽं͕྆ଞंͷ௨৴ΤϦΞ֎ʹҠಈͯ͠௚઀௨৴ Ͱ͖ͳ͍ঢ়گͰ͸ɼେ͖ͳ஗Ԇ͕ൃੜͯ͠ϝοηʔδ Λഁغͯ͠͠·͏͜ͱʹͳΔɽ ͦ͜Ͱɼେ͖ͳ஗Ԇ΍ܦ࿏ߋ৽͕සൃ͢ΔΑ͏ͳ؀ ڥʹ͓͍ͯ௨৴Λߦ͏ͨΊʹ

_{Delay/ Disruption/}

Dis-connection Tolerant Networking

ʢ

DTN

ʣ͕஫໨͞Ε͍ͯ Δ(3), (4)_ɽ

_DTN

_{͸࿭੕ؒ௨৴ͰఏҊ͞Εͨ௨৴ख๏Ͱ͋} Γɼं྆ؒ௨৴΁ͷԠ༻͕ظ଴͞Ε͍ͯΔ(5)_ɽ ຊߘͰ͸ɼ

DTN

௨৴Λར༻ͨ͠ϝοηʔδ഑ૹʹ͓ ͚Δෳ੡ϝοηʔδྔ͕૿͑ͯ͠·͏໰୊ʹয఺Λ౰ͯ ͍ͯΔɽ

DTN

ʹΑΔϝοηʔδ഑৴ʹ͸ෳ੡ϝοηʔ δΛ཈੍͢ΔͨΊͷ

2

छྨͷख๏Λ༻͍ͨͱ͖ͷ஗Ԇ ࣌ؒͱෳ੡ͨ͠όϯυϧϝοηʔδྔʹ͍ͭͯධՁɾٞ ࿦͢Δɽ ୈ

2

ষͰ͸

DTN

௨৴ͷ֓ཁʹ͍ͭͯɼୈ

3

ষͰ͸ఏ Ҋख๏ͷϦΧόϦख๏ʹ͍ͭͯઆ໌͢Δɽୈ

4

ষͰ͸ γϛϡϨʔγϣϯʹ͍ͭͯઆ໌͢Δɽୈ

5

ষͰ͸γϛϡ Ϩʔγϣϯ݁Ռʹ͍ͭͯධՁ͢Δɽͦͯ͠ɼ࠷ޙͷୈ

6

ষͰ͸Ή͢ͼͱͯ͠ɼຊใࠂͷ·ͱΊͱࠓޙͷ՝୊ʹ ͍ͭͯड़΂Δɽ

2. DTN

DTN

௨৴Ͱ͸ɼطଘͷΠϯλʔωοτͰ༻͍ΒΕ͍ͯ Δܦ࿏୳ࡧ͸ߦΘͣɼϝοηʔδΛॴ͍࣋ͯ͠Δ୺຤͕ ྡ઀͢Δं྆ʹϝοηʔδ഑෍Λ܁Γฦ͢͜ͱʹΑΓɼ Ѽઌ୺຤·ͰϝοηʔδΛಧ͚Δ஝ੵܕసૹํࣜͰ͋ Δ(6), (7)_{ɽͦͷͨΊɼϦΞϧλΠϜ௨৴ʹ͸޲͔ͣࡂ֐࣌} ΍ं྆ؒͳͲͷେ͖ͳ஗Ԇ΍௨৴్ઈ͕සൃ͢ΔΑ͏ͳ ؀ڥͰ༻͍ΒΕΔ௨৴ٕज़ͱͯ͠஫໨͞Ε͍ͯΔɽ

DTN

ʹ͸

Epidemic

(8), (9)_ɼ

_{Spray and Wait}

(10)_ɼ

_MaxProp

(11)

ͳͲͷϓϩτίϧ͕ఏҊ͞ΕɼͦͷػೳʹϦΧόϦػ ೳΛඋ͑ͨख๏ͳͲ͕ٞ࿦͞Ε͍ͯΔ(12), (13)_ɽϦΧό Ϧػೳͱ͸ɼѼઌ·Ͱಧ͚ͨόϯυϧϝοηʔδͷෳ ੡Λॴ࣋͢Δ୺຤ʹ౸ணͨ͜͠ͱΛ௨஌͢Δ͜ͱʹΑ Γ֘౰͢ΔόϯυϧϝοηʔδΛ࡟আ͢Δػೳ΍ɼλ ΠϚʔΛར༻ͯ࣌ؒ͠ܦաʹ൐͍ෳ੡ϝοηʔδΛࣗ ಈ࡟আ͢ΔػೳͳͲͰ͋Δɽ ఏҊख๏ͷෳ੡ϝοηʔδ཈੍ख๏͸ɼطଘͷ

DTN

ϓϩτίϧͱ૊Έ߹Θͤͯར༻͢Δ͜ͱ͕Ͱ͖ɼෳ੡ ϝοηʔδΛసૹ͢Δͱ͖ʹᮢ஋Λར༻ͯ͠ϝοηʔ

ɹ٢໺ ༔ɼத࡚লޗɼ஑ా ੣ δΛ௚ͪʹૹ৴͢Δ͔Λ൑அ͢Δɽ͜ͷᮢ஋͸ྡ઀ं ྆ͷ਺Λߟྀͯ͠ɼ֘౰͢Δόϯυϧϝοηʔδͷॴ ࣋཰ͱൺֱ͢Δ͜ͱͰ཈੍੍ޚΛߦ͏ɽ

3.

ఏҊख๏

ఏҊ͢Δෳ੡ϝοηʔδ཈੍ख๏͸

6

ͭͷϝοηʔ δΛར༻͢Δɽطଘͷ

DTN

௨৴ʹ

3

ͭͷ੍ޚϝοηʔ

δʢ

_MS-HELLO

ɼ

_MS-ACK

ɼ

_MS-REQUEST

ʣΛ৽ͨʹ

Ճ͍͑ͯΔɽఏҊख๏Λ֤ं྆ͷ

OBU

ʹ࣮૷͢Δ͜ͱ Ͱɼ

_OBU

Ϣχοτʹ઀ଓ͞Εͨ௨৴ϞδϡʔϧΛར༻ ͯ͠ं྆ؒͰ཈੍੍ޚΛߦ͏͜ͱΛ૝ఆ͍ͯ͠Δɽ ֤ϝοηʔδΛड৴ͨ͠ͱ͖ͷ੍ޚϑϩʔΛਤ

₁

ʹ ࣔ͢ɽຊใࠂͰ͸ɼதܧं྆਺ͷ

10%

ͷं྆਺Ͱϝο ηʔδ཈੍ػೳΛ༗ޮʹ͍ͯ͠Δɽ༗ޮʹ͢Δ୆਺Λ੍ ݶ͍ͯ͠Δͷ͸ɼ୆਺աଟͩͱόϯυϧϝοηʔδ͕Ѽ ઌ·Ͱ౸ୡ͢Δ஗Ԇ͕࣌ؒେ͖͘ͳΔͨΊͰ͋Δ(12)_ɽ ཈੍ػೳΛ༗ޮ͢Δं྆͸पғͷं྆ʹରͯ͠

MS-HELLO

ΛఆظతʹपғʹϒϩʔυΩϟετ͢ΔɽͦΕ Λड৴ͨ͠ं྆͸

MS-ACK

ύέοτΛฦૹ͢Δɽ

MS-ACK

Λड৴ͨ͠ं྆͸όϯυϧϝοηʔδͷॴ༗཰ ʢ

PR

BundleIDʣΛྡ઀ं྆ͷόϯυϧॴ࣋ϦετΑΓܭࢉ ͢Δɽ

PR

BundleIDͷܭࢉࣜΛࣜʢ

1

ʣʹࣔ͢ɽ

PR

BundleID

=

ಉ͡

BundleID

ͷϝοηʔδݕग़਺

N

(1)

͜ͷྡ઀ं྆ͷόϯυϧॴ࣋Ϧετٴͼྡ઀ं྆਺ʢ

N

ʣ ͸ɼଞͷं͔྆ΒૹΒΕͯ͘Δ

HELLO

ʹΑΓ൑அ͠ ͍ͯΔɽ࣍ʹ͜ͷॴ࣋཰͕

Adaptive Threshold

ʢ

AT

ʣҎ ্Ͱ͋Ε͹཈੍࣌ؒΛࢉग़͠ɼ

MS-REQUEST

Λฦૹ͢ Δɽैདྷख๏Ͱ͸ɼ͜ͷᮢ஋͕ݻఆͷͨΊɼपғͷं྆ ਺Λߟྀ͢Δ͜ͱ͕Ͱ͖ͳ͔ͬͨ(14)_ɽ

_MS-REQUEST

Λड৴ͨ͠ं྆͸֘౰͢ΔόϯυϧΛࢦఆ࣌ؒ͸ෳ੡ ͢ΔͷΛఀࢭ͠ɼωοτϫʔΫ಺ʹॏෳ͢Δόϯυϧ ϝοηʔδ͕૿͑ͯ͠·͏໰୊Λ௿ݮ͍ͤͯ͞Δɽ

AT

͸࣍ࣜͰܭࢉ͢Δɽ

Adaptive Threshold(AT )

=

2

N

(N

> 1)

(2)

PR

BundleID

≥ AT

ͷ৚݅Ͱ཈੍ཁٻΛߦ͏͔൑அ͓ͯ͠ Γɼྡ઀ं྆਺͕ଟ͘ͳΔͱϝοηʔδ཈੍͢Δᮢ্͕ ͕ΔΑ͏ʹઃܭ͍ͯ͠Δɽͨͩ͠ɼྡ઀ं྆਺͕

0

୆ ͋Δ͍͸

1

୆ͷ৔߹͸཈੍ॲཧΛߦΘͳ͍ɽྡ઀ं྆ ਺͸

₂

ඵຖʹϦηοτ͢ΔɽຊߘͰ͸཈੍࣌ؒʢ

_MST:

Message Suppression Time

ʣͱͯ͠

30

ඵɼ

60

ඵɼ

120

ඵ

ͷ

₃

छྨΛར༻ͯ͠ఏҊख๏ΛධՁ͢Δɽྫ֎ॲཧͱ

ͯ͠ɼ཈੍தʹ࠷ऴͷѼઌं྆Λݕग़ͨ͠৔߹͸ɼ཈ ੍தͰ΋όϯυϧϝοηʔδΛసૹ͢Δɽ

ਤ

1

ఏҊख๏ͷϑϩʔνϟʔτ

fig.1. Flowchart of proposed method.

4.

γϛϡϨʔγϣϯ

ຊߘͰ͸ఏҊख๏ͷධՁʹɼैདྷͷϝοηʔδ཈੍ ख๏(12)_{ͱϦΧόϦػೳΛར༻͠ͳ͍}

_Epidemic

(8)_Λ༻ ͍ͨɽ֤ख๏ΛҎԼͷΑ͏ʹදه͢Δɽ

1.

ఏҊ͢Δ

Adaptive Threshold

ख๏ʢ

AT

ʣ

2.

ैདྷͷϝοηʔδ཈੍ख๏ʢ

Conv

ʣ

3.

ϦΧόϦػೳΛར༻͠ͳ͍

Epidemic

ʢ

Epidemic

ʣ ఏҊख๏ͱैདྷख๏ΛωοτϫʔΫγϛϡϨʔλͷ

Sce-nargie

(15)_{ʹ࣮૷ͯ͠ੑೳධՁΛߦ͍ͬͯΔɽಓ࿏Ϟσ} ϧͱͯ͠Ϛϯϋολϯͷࢢ֗஍ͷΑ͏ʹ֨ࢠঢ়ͷಓ࿏ Ͱɼ֨ࢠঢ়ͷಓ࿏ؒʹ͸

10m

ͷߏ଄෺ʢϏϧʣΛ഑ஔ ͍ͯ͠Δʢਤ

₂

ࢀরʣɽ͜ͷಓ࿏Ϟσϧʹ͓͍ͯྡ઀ं ͕྆ϝοηʔδΛసૹ͍͖ͯ͠ɼѼઌ୺຤·Ͱϝοηʔ δΛ఻ୡ͢ΔγφϦΦͰ͋Δɽૹ৴ݩͱѼઌ͸ݻఆͰ ௚ઢڑ཭͸໿

_{1, 414m}

Ͱ͋Δɽं྆͸ಓ࿏ͷަ௨ϧʔ ϧΛߟྀͨ͠

Random Way-point

ҠಈϞσϧͰɼ

_0km/h

͔Β

60km

/h

ͷؒͷϥϯμϜͳ଎౓ͰҠಈ͢Δɽͦͷଞ ͷγϛϡϨʔγϣϯύϥϝʔλΛද

1

ʹࣔ͢ɽ

ਤ

2

ಓ࿏Ϟσϧ

fig.2. Road model.

ද

₁

γϛϡϨʔγϣϯύϥϝʔλ

Table 1. Simulation parameters.

Parameter Value γϛϡϨʔγϣϯ࣌ؒ 600 ඵ ΤϦΞαΠζ 1000 m× 1000 m ं྆਺ 50, 100, 150 ୆ όϯυϧ: ૹ৴։࢝࣌ؒ 10 ඵ όϯυϧ: ૹ৴ऴྃ࣌ؒ 410 ඵ όϯυϧ: ૹ৴ִؒ 10 ඵ όϯυϧ: ϝοηʔδαΠζ 500 bytes PHY Ϟσϧ IEEE 802.11p प೾਺ 5.9 GHz ి೾఻ൖϞσϧ ITU-R P.1411 ΞϯςφϞσϧ ແࢦ޲ੑ Ξϯςφߴ 1.5 m

5.

ධՁ

஗Ԇ࣌ؒͷγϛϡϨʔγϣϯ݁ՌΛਤ

3

ʹࣔ͢ɽ͢΂ ͯͷέʔεͰதܧं྆਺͕૿͑Δͱ஗Ԇ͕࣌ؒԼ͕ͬͯ ͍Δɽέʔεຖͷࠩ͸ं྆਺͕૿͑Δͱݮগ͍ͯ͠Δɽ ͜Ε͸தܧं྆਺͕૿͑ͨ͜ͱʹΑΓόϯυϧϝοηʔ δΛѼઌ·Ͱಧ͚Δܦ࿏͕૿͑ͨͨΊͰ͋Δɽ

Epidemic

ͷ஗Ԇ͕࣌ؒ୹͍ͷ͸ϦΧόϦػೳ͕ແޮͳͨΊͰ͋ ΔɽͦͷͨΊɼं྆਺͕

50

୆ͱগͳ͍ͱ͖ʹ͸͕ࠩେ ͖͔͕ͬͨɼதܧं͕྆૿͑ͯωοτϫʔΫ಺Ͱόϯυ ϧͷෳ੡͕૿͍͑ͯ͘ঢ়گʹͳΔͱࠩ͸ͳ͘ͳΓɼத ܧं྆਺ʹΑͬͯ͸ͦͷੑೳ͕ࠩٯస͢ΔՄೳੑ͕͋ Δͱ༧૝͢ΔɽఏҊख๏ͷ

AT

ͱैདྷख๏ͷ

Conv

Λൺ ֱ͢Δͱɼ

Conv

ͷ஗Ԇ͕࣌ؒ୹͍ɽ͜Ε͸

AT

͸ྡ઀ ं྆ͷ୆਺ʹΑͬͯ཈੍ཁٻΛ͢Δ͔൑அ͓ͯ͠Γɼ཈ ੍͢Δճ਺͕

Conv

ͱൺֱͯ͠ଟ͔ͬͨͨΊɼ஗Ԇ࣌ؒ ͱ͕ͯࠩ͠Ͱͨɽ͞Βʹɼ

MST

ͷࠩ͸

Conv

Ͱ͸݁Ռ ʹࠩ͸΄ͱΜͲݟΒΕͳ͔͕ͬͨɼ

AT

Ͱ͸

MST

Λ

120

ඵʹͨ͠৔߹ʹଞͷέʔεͱൺֱͯ͠஗Ԇ͕େ͖͘ͳ Δ͜ͱΛ֬ೝͨ͠ɽ ਤ

4

ʹෳ੡͞Εͨόϯυϧϝοηʔδͷૹ৴ྔΛࣔ ͢ɽं྆਺͕૿͑Δͱෳ੡͞Εͨόϯυϧͷσʔλྔ ΋૿͍͑ͯΔ͕

100

୆ͱ

150

୆ͷ֤έʔεͷੑೳࠩ͸ খ͍͞ɽෳ੡σʔλྔ͕Ұ൪཈͑ΒΕ͍ͯΔͷ͸

AT

Ͱɼ

MST

͕େ͖͍΄Ͳσʔλྔ͕গͳ͍͜ͱ͕෼͔Δɽ

MST

ʹൺྫͯ͠ෳ੡σʔλྔ͕ݮΔͷ͸ɼ཈੍࣌ʹઃ ఆ͢Δ཈੍࣌ؒͷܭࢉʹ༻͍͍ͯΔύϥϝʔλͷͨΊ Ͱ͋Δɽ ֤έʔεͱ΋γϛϡϨʔγϣϯ࣌ؒ಺ʹ͢΂ͯͷό ϯυϧϝοηʔδʢ

₄₀

छྨʣΛѼઌ·ͰૹΓಧ͚͓ͯ ΓɼఏҊख๏Ͱ͸όϯυϧϝοηʔδͷෳ੡ճ਺Λ཈ ͑Δ͜ͱΛՄೳʹ͍ͯ͠Δɽ͔͠͠ɼѼઌ·Ͱಧ͚Δ ͨΊͷ஗Ԇ͕࣌ؒ૿Ճ͢Δ໰୊͕՝୊ͱͯ͋͠Δɽ

6.

Ή͢ͼ

ຊߘͰ͸ɼ֨ࢠঢ়ͷࢢ֗஍؀ڥʹ͓͚Δ

DTN

௨৴ͷ ෳ੡ϝοηʔδ͕૿͑ͯ͠·͏໰୊Λղܾ͢ΔͨΊͷ ख๏ʹ͍ͭͯใࠂͨ͠ɽఏҊख๏Ͱ͸όϯυϧͷෳ੡ ճ਺Λ཈੍͢Δ͜ͱ͕Ͱ͖Δ͜ͱΛ໌Β͔ʹͨ͠ɽ཈ ੍࣌ؒͱͯ͠͸

₆₀

ඵҎ಺ʹ͢Δ͜ͱͰ஗Ԇ࣌ؒͷ্ঢ Λ͋Δఔ౓཈͑Δ͜ͱ͕Ͱ͖ͨɽ ࠓޙͷ՝୊ͱͯ͠ɼෳ੡ϝοηʔδͷ཈੍͚ͩͰ͸ ͳ͘ɼ֤ं྆ͷετϨʔδ಺ʹอ͍࣋ͯ͠Δόϯυϧ ϝοηʔδͷ༗ޮతͳ࡟আख๏ͳͲʹ͍ͭͯ΋ݕ౼͠ ͍͖͍ͯͨɽ

ँࣙ

ຊݚڀͷҰ෦͸ຊֶ૯߹ݚڀػߏͷฏ੒

₂₉

೥౓ݚ ڀһݚڀඅʹΑΓ࣮ࢪͨ͠΋ͷͰ͋Δɽ͜͜ʹँҙΛ ද͢ɽ

ɹɹɹɹɹɹɹจɹɹɹݙ

(1) Gerla, M., Lee, E.-K., Pau, G. and Lee, U.: Internet of ve-hicles: From intelligent grid to autonomous cars and vehic-ular clouds, Proceedings of the 2014 IEEE World Forum on

Internet of Things (WF-IoT-2014),, pp. 241–246 (2014).

(2) Harri, J., Filali, F. and Bonnet, C.: Moblility Models for Ve-hicular Ad Hoc Networks: A Survey and Taxonomy, IEEE

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0

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Epidemic AT, MST=30s AT, MST=60s AT, MST=120s Conv, MST=30s Conv, MST=60s Conv, MST=120s ਤ

3

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fig.3. Delay.

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4

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