Itrt Prpagati I Namd Data MANE Yu-ig Yu 1, Rahlh B. Dilmaghai 2, Sraphi Cal 2, M. Y. Saadidi 1, ad Mari Grla 1 Cmputr Scic Dpt, 2 IBM. J. Wat Rarch Ctr, UCLA, L Agl, CA 995, USA Yrktw Hight, NY, USA yutigyu@c.ucla.du, {rbdilmag, cal}@u.ibm.cm, {grla, mdy}@c.ucla.du Abtract Namd Data MANE (NDM) i a rctly mrgig rarch ara. h i-twrk chuk-bad cachig fatur f NDN i bficial i cpig with th mbility ad itrmittt cctivity challg i MANE. I thi papr, w cduct bth imulati ad mulati tudy f NDM frwardig dig: NDN Frwardig (NDNF), Lit-Firt, Bradcat-Latr (LFBL), ad th prpd Nighbrhd-Awar Itrt Frwardig (NAIF). NAIF aim fr rducig th badwidth uag iducd by idicrimiat itrt fldig i NDNF. It dcra th itrt traffic by lttig ligibl rlay wrk cprativly, ach frward ly a fracti f itrt packt. h rult hw that NAIF achiv th bt prfrmac i multi-cumr cari. I particular, cmpard t NDNF, NAIF rduc badwidth uag by up t 54%, hrt rp tim i lw-mbility cari, ad achiv high cmplti rati. Idx rm Namd-Data Ntwrk, MANE I. INRODUCION Mbil Ad-Hc Ntwrk (MANE) i a ifratructurl twrk architctur ctitutd by mbil dvic. h mai advatag f MANE i that it ca b frmd at lw ct i rp t tmprary d, ad thu i ft ud i battlfild ad diatr-rcvry twrk. h majr challg i MANE ar mbility ad itrmittt cctivity. Nd ar aumd t mv at varyig pd, rultig i fat-chagig tplgi ad xtrm packt l, ad cqutly high vrhad fr rut ctructi ad maitac ct. Mrvr, th prfrmac may uffr frm tmprary twrk partitiig. Namd Data Ntwrk (NDN) [1], a twrk architctur that u th data am itad f ht addr t lcat data, i a atural fit t MANE data rtrival. Evry NDN data chuk ha a uiquly idtifid am. hi uiquly idtifid chuk amig abl th chuk-bad i-twrk cachig. h NDN architctur trictly aum pull-bad frwardig ad -itrt--data pricipl. iitiat a data trafr, a data cumr mut d a Itrt Packt (ItP) t rqut th crrpdig Data Packt (DatP). All d cach th data chuk thy rlay ad bcm rvr f th cachd chuk. With ditributd cach, a data rtrival failur ariig frm itrmittt cctivity ca b quickly rcvrd ad th calability ad fficicy data prpagati ca b imprvd. Furthrmr, th am-bad frwardig i cmpatibl with multi-path frwardig ad thu i pttially mr rbut tha ht-bad rutig i MANE. I [1], NDN d lct which itrfac() t bradcat th ItP. h crrpdig DatP fllw th bradcrumb trail f th ItP back t th data cumr. hi dig aum multipl itrfac at a d. Hwvr, i typical MANE dplymt whr a d u ly itrfac, th itrfaclctig dig rult i all d fldig all ItP thy rcivd. h fldig traffic will ct xtrm badwidth ad rgy cumpti. O luti i t ctruct a vrlay twrk tp f IP layr a i CCNx [2]. Hwvr, implmtig vrlay NDN i ctly i MANE bcau (1) d-t-d rut ctructi ad maitac btw vrlay d iduc high ctrl vrhad ad (2) th vrlay dig d up prfrmig pit-t-pit tramii withut xplitig th bradcatig atur f wirl chal. Athr challg i MANE NDN i cachd fragmt phm. hat i, althugh th cmplt data bjct may rid i a igl phyical d, th cach ar likly t hld ly partial data bjct. hrfr, xitig igl-path rutig algrithm that kp dig packt t th firt fud lcati may d up r-fldig mt ItP du t th iability t prdict th cctivity ad chuk cmplti tat f th ItP dtiati. A a rult, fficit frwardig dig i NDM ar f igificat itrt. I thi papr, w ivtigat th prfrmac f thr NDM frwardig dig: th rigial NDN frwardig (NDNF) [1], th prpd Nighbrhd- Awar Itrt Frwardig (NAIF), ad th rprtativ f igl-path ad-hc rutig, Lit-Firt, Bradcat Latr (LFBL) [3]. Our rult hw that althugh LFBL, which prvid lw rp tim ad badwidth utilizati, prfrm wll i igl-cumr data rtrival cari, NDNF ad NAIF ar mr rbut i multi-cumr cari. Furthrmr, NAIF achiv igificat imprvmt vr NDNF. h badwidth cumpti i rducd arly by half i itrmitttly cctd cari. h rgaizati f thi papr i a fllw. Scti II dicu th rlatd wrk. W brifly itrduc th NDM frwardig apprach i Scti III. I Scti IV, th dtaild NAIF algrithm i prtd. h xprimt rult ar rprtd i Scti V. W cclud thi papr i Scti VI. II. RELAED WORK NDM rutig ca bt rlat t pprtuitic frwardig, which i widly ud i dlay-tlrat twrk, i which it i difficult t btai full tplgy ifrmati. Mt rutig prtcl i uch virmt ar variati f Epidmic Rutig (ER) [4]. ER diffu mag it twrk i a imilar way a dia uig th cach-ad-frward tchiqu. rduc th vrhad ad dlay f ER, MV rutig [5] pprtuitically lct mag t frward t cutrd d. Fllw-up wrk [6][7] utiliz mbility pattr r
ctact hitry t imprv th prfrmac. Hwvr, th ht-bad prtcl cat b applid t NDN dirctly. Mbil NDN i a rctly mrgig rarch ara. I [8], Wag t al. prp a NDN-bad data cllcti ytm fr vhicular ifratructur twrk. L t al. prp a prxybad chm fr icraig fficicy f mbil rtrival [9]. A fr MANE, i [1], Oh t al. prp a tactical NDM apprach uig OLSR ad Patry ad tudy it faibility. I [11], Varvll t al. cmpar th prfrmac f ractiv fldig, practiv fldig, ad Ggraphic Hah abl (GH) i NDM aalytically. hir rult hw that th ractiv fldig apprach utprfrm th thr tw. III. NDM FORWARDING DESIGNS A. NDNF h rigial frwardig dig i [1], which w call NDNF, firt bradcat (fld) ItP, whil DatP fllw th bradcrumb trail f th ItP back t th data cumr. h advatag f thi apprach i it rbut udr mbility ad itrmittt cctivity, ad cachd fragmt phm. h widprad DatP ar bficial fr data i high dmad ic th cumr ar likly t cutr dird cachd chuk i thir prximity. Hwvr, NDNF cau larg vrhad i ad-hc twrk du t rdudat data prpagati. B. Nighbrhd-Awar Itrt Frwardig (NAIF) Big awar f th xciv vrhad f NDNF, w prp a adaptiv ItP prpagati mchaim, NAIF, which aim at rducig th fldig vrhad whil maitaiig th rbut. A i NDNF, NAIF tramii ar all bradcat ad ithr ItP r DatP ar xplicitly rutd. h ratial f NAIF i that itad f lctig a d t frward th ItP t, rlay cprativly prpagat ItP btw th cumr ad pttial data urc. Rlay dig duplicat ItP may cau cllii ad cgti, i particular if th dr ar hidd frm ach thr. NAIF mitigat hidd trmial uprfluu frwardig by ctrllig tramii bad d frwardig tatitic, which ar th ud t adjut frwardig rat. h frwardig rat i th fracti f packt a d huld frward fr th giv am prfix. A rlay lcally dcid t bradcat r t drp th rcivd ItP bad th frwardig rat. h ituiti f frwardig rat adjutmt i: th mr DatP f th am am prfix a d vrhar frm it ighbr, th mr ItP crrpdig t that am prfix it ca drp a it judg thm t b uprfluu. I NAIF, a d gradually lwr it frwardig rat if it har it ighbr dig th DatP crrpdig t th ItP it drppd. Cvrly, wh a d dtct it ha drppd t may ItP, it icra it frwardig rat t cmpat. I thi way, th rlay cprativly frward th ItP withut cgti. W will dcrib dtaild NAIF ctrl algrithm i Scti IV. C. LFBL LFBL [3] i a lw-vrhad frwardig prtcl fr Namd Data MANE aimig at ctructig igl path t data prvidr. It i digd a a NDN rplacmt. I LFBL, ach d maitai ditac t kw data bjct am ad d am. If th data bjct ditac i ukw, th ItP i fldd. Othrwi, th ItP i bradcat but prpagatd t th art data hldr via implicitly ch hrtt path uig pprtuitic rutig [12] bad th dtiati ditac timatd by rlay. DatP ar prpagatd via th hrtt path back t th cumr i th am way. h timatd ditac t th dpit ar rfrhd at th rlay alg th hrtt path wh th rlay har w packt. LFBL achiv lwr badwidth uag du t it path lcti atur. Hwvr, a pttial prblm li i th accuracy f ditac timati. Mrvr, th paiv bt path lcti prc al d t prtct LFBL frm th cachd fragmt phm. h bjct lcati fud by th firt ItP ca actually b a cach hldig ly part f th data bjct. Hc, i th wrt ca, fllwig th path t th am dtiati rult i ctiuu r-fldig f idividual ItP fr th miig chuk. I additi, LFBL rquir igificat mdificati f currt NDN. IV. NAIF FORWARDING CONROL h tw-pa NAIF algrithm i applid ly t itrt frwardig at rlay d. At th firt pa, iligibl frwardr ar filtrd ut. h ligibility f a rlay d i dcidd by it data rtrival rat fr th giv am prfix ad it ditac t th data cumr. I thr wrd, th rlay which cat rach ay pttial frwardr prud thmlv frm th frwardig t. If th data rtrival rat i lw r th d i t far away frm th data cumr, th icmig ItP will b dicardd lcally. I rdr t hadl tmprary twrk partiti, th rlay with lw data rtrival rat prb th twrk vry PROBE cd t rcvr th frwardig tatitic. At th cd pa, th ligibl frwardr prbabilitically drp th icmig ItP bad th updatd mthd frwardig rat, F %, f it am prfix. h frwardig rat updat prc i dcribd blw. h NAIF frwardig ctrl i bad tw lcal frwardig paramtr at rlay d: th data rtrival rat R, which i th rati f th umbr f DatP uccfully rtrivd t th umbr f ItP t; ad th frwardig rat F, which i th fracti f icmig ItP that a giv d will frward. h frwardig paramtr ar pridically adjutd bad th frwardig tatitic. Fur frwardig tatitic fr ach am prfix ar cllctd durig a updat itrval: th umbr f ditict ItP t it, th umbr f ditict DatP rcivd r data, th umbr f ditict ItP clard c, which i icrad by wh a PI try i clard, ad th umbr f ditict -cachd ItP rcivd r it. A -cachd ItP i dfid a a ItP rqutig a DatP that i t cachd at th giv d. Nd paivly cllct frwardig tatitic by mitrig ItP ad DatP frwardd by itlf ad -hp ighbr. Pridically, th rtrival rat ad th frwardig rat fr ach am prfix ar updatd pridically, th th frwardig tatitic ar rt. h data rtrival rat i ud t maur th ffctiv f a d i rtrivig a DatP fr a am prfix. At th d f a updat itrval t, th data rtrival rat i cmputd by
Rt = c (1) it Nt that w u c itad f r data bcau a d may rciv DatP which it had t rqutd ic all packt ar bradcat. h frwardig rat i dfid a th fracti f ItP t by a d. At th d f a updat itrval t, th actual frwardig rat durig t i Ft = it r (2) it h mai ida f frwardig rat adjutmt i, t acquir a DatP f itrt, d i a ighbrhd ca har th frwardig wrklad fr a data bjct. A th ighbrhd i rtrivig th DatP f th d itrt uccfully, th d may rduc it w frwardig rat. Wh a d drp a ItP ad rli it ighbr t d it, thi ItP i cidrd mid if th rtrival f th crrpdig DatP i uuccful. Aumig th umbr f mid ItP, δ, i kw. h frwardig rat i adjutd by mi ( it 1 rit, F% t 1 ) if t = = t 1 t F% α δ β L δ = δ = (3) t = ( it + δt ) rit thrwi If thr i mid ItP i th prviu β itrval, th d gradually rduc it frwardig rat. Othrwi, th d adjut it frwardig rat upward bad δ. Each d lcally timat δ. If a d rcivd a ItP ad drppd it, ad th d d t vrhar th crrpdig DatP latr, th ItP may hav b mid. hrfr, th umbr f mid ItP i btaid by δ = r r c (4) ( ) ( ) it it data Exptial avragig i ud t mth th variati i th frwardig paramtr. h mthd avrag rtrival rat R % ad frwardig rat F % ar rt t iitial valu if th kw data prfix i t hard fr a cfigurabl prid f tim. A. Applicati Dig V. PERFORMANCE EVALUAION Fr validati purp, a am-bad fil trafr applicati i implmtd. Each data bjct i gmtd it m gmt. Each gmt i amd fil/1, fil/2,, fil/m, rpctivly. h fil trafr applicati ca b xcutd i tw md: Ctat Bit Rat (CBR) md r widw md. I CBR md, th ItP ar t i a ctat rat. I widw md, th data cumr iitially d a batch f ItP. A fixd-iz mvig widw calld th itrt widw ctrl th t f uttadig ItP. h prc i imilar t CP with a fixd widw iz. If DatP ar rcivd ut-f-rdr, th itrt widw i advacd t th xt gmt it had t rqutd r rcivd yt. B. Mtric ad Exprimt Sttig h xprimt ar cductd via bth imulati ad mulati. Our imulati i d with QualNt 4.5. h mulati i ru a Lv 61 laptp with Cmm Op Rarch Emulatr (CORE) [13]. W maur fur mtric: rp tim, umbr f tramii, cmplti tim, ad cmplti rati. h rp tim i th tim itrval frm th mmt a data cumr firt d a ItP t th tim wh th data cumr rciv th firt crrpdig DatP. h umbr f tramii iclud all th tramii i th ytm. h cmplti tim i th tim durig which all fil trafr ar cmpltd. h cmplti rati i th fracti f chuk uccfully rcivd by th data cumr ut f all chuk. LFBL i itiv t th ditac mtric. h mt ituitiv ditac mtric i hp cut, which ha b widly ud i rutig prtcl. It wa rprtd that th rcivd igal trgth RXPOWER i th bt mtric [3]. hu, w prt th rult f bth hp cut ad RXPOWER. ivtigat th impact f cachd fragmt phm, w tudy tw vri f LFBL: LFBL-A ditiguih cach cpi by idicatig th full am f th cachd chuk (.g. /jh/prtati/1) a prfix. LFBL-B d t ditiguih btw cach cpi ad prducr cpi, ad alway carry th fil prfix (.g. /jh/prtati) a urc am i DatP. W kp mt f th ttig ud i [3] fr LFBL ad u idtical ttig fr all crrpdig paramtr fr NDNF ad NAIF. Ul thrwi tatd, th imulati cit f 5 d iitially uifrmly ditributd i a 1m 1m ara, mvig bad radm waypit mdl. 5% f th data chuk ar uifrmly ditributd t rlay cach i advac. h phyical layr i IEEE 82.11b. CBR applicati that iitiat tw ItP pr cd ar ud. h imulati tim i 6 cd. h data chuk iz i 128 byt. All xprimt ar ru t tim with diffrt radm d ad th avrag rult ar rprtd. h rtramii limit (R LIMI) i 4. h rtramii itrval ( RX) i 1 cd. Fr NAIF, PROBE i 2 cd, th updat itrval i 1 cd, R * i 3%, α i.5, ad β i 1. C. Exprimt 1: Sigl Cumr I thi xprimt, data cumr dwlad a fil frm data prducr. Figur 1 prt th cmplti rati. NDNF ad NAIF bth achiv almt prfct cmplti rati. h rult f LFBL prtcl ar citt with th rult rprtd i [3]: th ditac mtric RXPOWER prfrm much bttr tha HOP-COUN. LFBL-B prfrm wr tha LFBL-A du t it iability t idtify cachd cpi. h ra i that LFBL-B data cumr frqutly d it ItP t a cach d that ly ha partial fil. hi impact i mr prucd i tatic twrk. Itrtigly, LFBL-B prfrmac i much imprvd with mbility. h ra i that a d mv, th data cumr i mr likly t rach cachd chuk if th ItP wr t t t th data prducr. h rp tim ad umbr f tramii ar prtd i Fig. 2 ad Fig. 3. LFBL-bad prtcl maitai lw rp tim ad tramii ic thy uually lct ly frwardig path. I ctrat, NDNF ad NAIF bradcat mr rdudat packt, rultig i rlativly highr rp tim ad tramii. Hwvr, cmpard t NDNF, NAIF rduc th umbr f tramii by abut 25%, albit at lightly highr rp tim. D. Exprimt 2: Multipl Cumr I thi xprimt, thr data cumr dwlad th am fil i paralll. h cmplti rati i rprtd i Fig. 4.
1.% 8.% ti 6.% r a 4.% 2.%.% ti l p m C tim ( p R.4.3.2.1 c ) LFBL-A (HOP-COUN) LFBL-B (RXPOWER) LFBL-B (HOP-COUN) Fig. 1. Exprimt 1: Cmplti rati LFBL-B (RXPOWER) LFBL-A (HOP-COUN) LFBL-B (HOP-COUN) Fig. 2. Exprimt 1: Rp tim Whil NDNF ad NAIF achiv almt prfct cmplti rati, LFBL-A (RXPOWER), which i th bt prfrmig amg all LFBL vri, achiv l tha 7% cmplti rati. h ra i that i LFBL, c th ditac t th data prducr i btaid by iitial fldig, th ditac ar ly rfrhd at th rlay alg th iitial hrtt path. hrfr, th ditac updat frm tw diffrt path may frqutly m ach thr up. A xampl i hw i Fig. 5. C1 ad C2 ar tw data cumr rqutig th am fil frm fil. C1 d it ItP via th path C1-R2-R1-fil; C2 rciv it DatP via fil-r3-r4-c2. Fr implicity, w u HOP-COUN a th ditac mtric. Nt that th ditac mtric d t affct th iu dcribd blw. R1 crrct ditac t fil i 1 ad R2 ditac i 2. hr ar tw pttial iu i th frwardig prc. Firt, upp th prducr d a DatP via R3 dtid t C2 whil R2 i dig a ItP t R1, ic R2 ad th fil prducr ar hidd t ach thr, it i vry likly that R1 will l th ItP. C1 may fix thi failur by r-iitiatig th am ItP. Hwvr, ditac updat itrfrc may happ bfr th r-iitiati. Supp R3 firt bradcat th DatP dtid t C2. Sic thi DatP carri dtdit=1 i it hadr ad th ifrmati i wr tha prviu ditac updat at R1, R1 updat it ditac t 2. Nt that R1 d t prpagat it w ditac t R2 bcau it i t a ligibl frwardr fr C2 packt. R1 ad R2 th bth bliv thir ditac t th prducr ar 2. hu, R1 tp frwardig th ubqut ItP it rcivd frm R2 bcau it ditac i t hrtr tha R2. hi tat prit util th ditac at C1 xpir r R1 crrct it ditac wh it har w DatP frm fil. hi ituati cau may ItP l wh multipl cumr d thir ItP t th am d. h currt LFBL dig cat idtify thi ituati ad hc cat fix th brk path i a timly mar. h rp tim ad th ttal umbr f tramii ar prtd i Fig. 6 ad Fig. 7. W fid that wh th twrk i tatic, LFBL prtcl hav lgr rp tim i multicumr cari a th hidd trmial ad ditac updat itrfrc lad t mr rtramii. hi iu i mr prucd fr LFBL-A prtcl ic thy dirct all ItP t th am prducr whil LFBL-B prtcl may dirct m t diffrt cach d. Althugh NDNF ad NAIF hav largr 2 15 1 5 x l ta Fig. 5. Ditac itrfrc i multi-cumr data rtrival rp tim ad badwidth cumpti, thy ar rbut ugh t maitai high cmplti rati. I thr wrd, th xtra badwidth cumpti i acrificd i xchag f highr rliability. I particular, NAIF maitai prfct cmplti rati whil rducig NDNF badwidth cumpti by apprximatly 2%. E. Exprimt 3:Itrmittt Ntwrk I thi xprimt, th cari cit f tw grup f d with pd 5 t 15 m/, ach ctaiig t d uifrmly placd i a 1m 1m ara. h tw grup ara ar adjact, frmig tw itrmitttly cctd partiti. O data prducr ad data cumr ar placd i th firt grup. w data cumr ar placd i th thr. All rlay cach ar mpty wh th imulati tart. Widw-md applicati with widw iz 1 i ud ic it prvt all ItP big rtramittd durig partitiig. h imulati rult ar prtd i Fig. 8. I Fig. 8(a), NAIF igificatly rduc th umbr f tramii at all d-travlig pd, ad cqutly av badwidth. h ra i that NAIF dtct wh th data hldr ar urachabl ad thu rduc ucary tramii. Fig. 8(b) hw that NAIF achiv cmparabl cmplti tim a NDNF d. NAIF achiv hrtr avrag rp tim at lw mbility, a hw i Fig. 8(c). NAIF ability f larig data urachability al ctribut it hrtr rp tim at lw mbility. Wh d mv at highr pd, NAIF rpd lightly lwr bcau it tak a hrt tim prid t rcvr tramii wh th ub-twrk ar rcctd. I ummary, NAIF av 3%-54% i umbr f tramii. F. Emulati Rult LFBL-B (RXPOWER) LFBL-A (HOP-COUN) LFBL-B (HOP-COUN) Fig. 3. Exprimt 1: Numbr f tramii 1.% ti 8.% a 6.% R 4.% 2.% ti l.% p m C LFBL-B (RXPOWER) LFBL-A (HOP-COUN) LFBL-B (HOP-COUN) Fig. 4. Exprimt 2: Cmplti rati W cductd mulati ad prt th prfrmac f NAIF ad NDNF udr th itrmitttly-cctd cari.
.6 c ) (.4.2 im p R d ) 6 7 a5 u 4 3 (th 2 x1 l ta 25 x2 l 15 ta 1 5 LFBL-B (RXPOWER) LFBL-A (HOP-COUN) LFBL-B (HOP-COUN) 2 3 4 5 6 Fig. 6. Exprimt 2: Rp im LFBL-B (RXPOWER) LFBL-A (HOP-COUN) LFBL-B (HOP-COUN) Fig. 7. Exprimt 2: Numbr f tramii NDN NAIF (a)numbr f tramii 2 (m ) 16 12 tim 8 4 p R 25 c ) ( 2 15 tim 1 ti 5 l p m C Fig. 8. Exprimt 3 rult I th mulati, th tw grup f d ach ctai fiv d uifrmly placd i a 45x4 ara. h thr ttig ar th am a that i th imulati. h mulati rult ar cmpild i abl I. h cmplti tim f NAIF i prtd rlativly t th cmplti tim f fldig. h rult hw that NAIF rduc th umbr f tramii by abut 45% cmpard t th f NDNF. Mawhil, NAIF achiv cmparabl cmplti tim ad avrag rp tim. VI. CONCLUSIONS 2 3 4 5 6 NDN NAIF (b)cmplti tim 2 3 4 5 6 NDN NAIF (c)rp tim W prp NAIF, a NDM frwardig prtcl aimig at rducig th high vrhad f NDN frwardig dig, i thi papr. W valuat th prfrmac f NAIF alg with th xitig NDM frwardig mchaim, NDNF ad LFBL. NDNF ad NAIF grally utiliz all uful rlay, whil LFBL implicitly lct path btw a cumr ad a data hldr. Our rult hw that NDNF ad NAIF achiv 3-6% highr cmplti rati i multi-cumr data rtrival cari tha LFBL. NAIF igificatly rduc badwidth uag by up t 54% whil maitaiig cmplti tim ad avrag rp tim cmparabl t NDNF i bth imulati ad mulati. hrfr, NAIF ca b viwd a a tartig pit t dig rliabl am-bad frwardig mthd fr havy-lad data rvic fr amd data MANE. ABLE I. EMULAION RESULS NDNF NAIF Numbr f x 93649 51868 Cmplti tim 1% 99.73% Avrag rp tim 236.94 m 25.32 m ACKNOWLEDGMEN hi papr i bad wrk upprtd by th Natial Scic Fudati udr Grat N. CNS-125757, Sigapr DSO Natial Lab udr Grat N. DSCCO135, ad th U.S. Army Rarch Labratry (ARL) ad th U.K. Miitry f Dfc udr Agrmt Numbr W911NF-6-3-1. REFERENCES [1] V. Jacb, D. K. Smttr, J. D. hrt, M. F. Pla, N. H. Brigg, ad R. L. Brayard, "Ntwrkig amd ctt," i CNEX 29, 5th itratial cfrc Emrgig twrkig xprimt ad tchlgi, Dc. 29, pp. 1-12. [2] Prjct CCNx M, Sp. 29, http://www.ccx.rg [3] M. Mil, V. Pappa, ad L. Zhag, "Ad hc twrkig via amd data," i Prcdig f th fifth ACM itratial wrkhp Mbility i th vlvig itrt architctur, Nw Yrk, NY, USA: ACM, 21, pp. 3-8. [4] A. Vahdat ad D. Bckr, Epidmic Rutig fr Partially Cctd Ad Hc Ntwrk, ch. Rp. CS-26, Dpartmt f Cmputr Scic, Duk Uivrity, Durham, NC, 2. [5] B. Bur, O. Brck, ad B. N. Lvi, "MV rutig ad capacity buildig i dirupti tlrat twrk," i Prcdig IEEE 24th Aual Jit Cfrc f th IEEE Cmputr ad Cmmuicati Sciti., vl. 1. IEEE, 25, pp. 398-48. [6] P. Mudur ad M. Sligma, "Dlay tlrat twrk rutig: Byd pidmic rutig," i 28 3rd Itratial Sympium Wirl Prvaiv Cmputig. IEEE, May 28, pp. 55-553. [7] E. C. R. d Olivira ad C. V. N. d Albuqurqu, "NECAR: a DN rutig prtcl bad ighbrhd ctact hitry," i Prcdig f th 29 ACM ympium Applid Cmputig, Nw Yrk, NY, USA: ACM, 29, pp. 4-46. [8] J. Wag, R. Wakikawa, ad L. Zhag, "DMND: Cllctig data frm mbil uig amd data," i 21 IEEE Vhicular Ntwrkig Cfrc. IEEE, Dc. 21, pp. 49-56. [9] J. L, D. Kim, M.-W. Jag, ad B.-J. L, "Prxy-bad mbility maagmt chm i mbil ctt ctric twrkig (CCN) virmt," i 211 IEEE Itratial Cfrc Cumr Elctric (ICCE), pp. 595-596. [1] S. Y. Oh, D. Lau, ad M. Grla, "Ctt ctric twrkig i tactical ad mrgcy MANE," i Wirl Day (WD), 21 IFIP. IEEE, Oct. 21, pp. 1-5. [11] M. Varvll, I. Rimac, U. L, L. Grwald, ad V. Hilt, "O th dig f ctt-ctric MANE," i Eighth Itratial Cfrc Wirl O-Dmad Ntwrk Sytm ad Srvic (WONS), IEEE, Ja. 211, pp. 1-8. [12] S. Biwa ad R. Mrri, "Opprtuitic rutig i multi-hp wirl twrk," SIGCOMM Cmput. Cmmu. Rv., vl. 34,. 1, pp. 69-74, Ja. 24. [13] Jff Ahrhlz, Claudiu Dailv, hma R. Hdr, ad Ja H. Kim. 28. CORE: A ral-tim twrk mulatr. I Prcdig f th 27th military cmmuicati cfrc (MILCOM 8). IEEE