Opportunistic  Networking(or  when  there’s  mobility  there’s  a  way)

Taiwan-­Italy  Bilateral  Workshop  on  Smart  Cities

Chiara  Boldrini  – chiara.boldrini@iit.cnr.itJoint  work  with  Marco  Conti,  Andrea  Passarella

Opportunistic  Networks• Set  of  mobile  devices  of  users  that  form  self-­organizing,  on-­demand  networks  largely  

exploiting  proximity  between  users.  – No  existing  infrastructure  needed

• Falls  in  the  broad  category  of  Mobile  Ad  Hoc  Networks– Enabling  Technologies:  Bluetooth,  WiFi  Ad  Hoc,  WiFi  Direct,  …

– Messages  are  routed  following  a  multi-­hop  path  across  the  nodes  of  the  network  and  the  routers  are  the  users  themselves

– Forwarding  decisions  are  taken  opportunistically  • Whenever  two  nodes  get  in  physical  proximity  they  evaluate  if  the  other  is  a  “better  forwarder”  towards  the  final  destination  

– Store-­carry-­and-­forward• Don’t  drop  messages  when  there  is  no  path  to  the  destination

• Carry  the  message  with  you  while  you  move,  and  wait  (seconds,  minutes,  hours,  …)  for  the  next  forwarding  opportunity

– This  creates  bridge  in  time  between  temporarily  disconnected  parts  of  the  network:  space-­time  path

• Data  should  “survive”  despite  users  disconnections  

– Instance  of  the  Delay  Tolerant  Networking  paradigm

2

What  are  they  for?• Mobile  Social  Networks  

– Enable  direct  communicationbetween  users  nearby  

– Discover  transient  socialcommunities  

– Content  sharing  as  primary  service  

• Accidents  and  disaster  scenarios

– All  cases  where  infrastructures  aredamaged  or  wildly  congested  

• Communications  in  remote  areas  – KioskNet project  

• Vehicular  network  applications  

• Military/Tactical  networks  

PhysicalCommunity

VirtualCommunity  (e.g.,  Facebook,,  Myspace,  LinkedIn,  …)

Human  social  networks

Virtual  social  networks

What  are  they  for  /  Mobile  Data  Offloading

• Cellular  networks  alone  are  likely  not  enough  to  support  expected  mobile  data  traffic  demand– Exponentially  increasing  demand  vs linearly  increasing  capacity  

• Opportunistic  networks  can  provide  additional  capacity  for  free  for  localized  communications  

– Built  on  unlicensed  spectrum  (WiFi,  Bluetooth),  which  does  not  interferewith  the  cellular  spectrum  

– Enabling  technology is  pervasively  available  

– Spontaneous  networks,  that  can  self-­organise dynamically,  based  onusers  demands  and  needs

• Two  different  roles– The  infrastructure  guarantees  a  100%  hit  ratio  on  users

– The  opportunistic  delivery  guarantees  a  low  load on  the  infrastructure

• Scenario:  a  very  large  number  of  users  located  in  arelatively  small  region  (campus,  city,  etc.),  overlapping  interests  in  content.  

– What  if  only  a  subset  of  these  n users  were  sent  theitem  from  the  infrastructure  and  the  other  fractionreceived  the  item  via  opportunistic  exchanges?

When  there’s  mobility  there’s  a  way

• Opportunistic networks and  user  mobility are  tightly  intertwined– Because  messages  are  handed  over  from  node  to  node

• If  we  want  to  tell  a  story  about  opportunistic  networks,  we  need  to  talk  first  about  how  users  move.– Characterize  those  mobility  metrics  that  are  relevant  to  opportunistic  communications

• Depending  on  the  type  of  mobility,  we  may  come  across  unpleasant  surprises that  have  to  be  addressed,  when  possible,  by  forwarding  protocols

How  to  measure  human  mobility• Before  smartphones  were  widely  

available,  it  was  not  a  straightforward  problem– Researchers  had  to  find  some  

proxies,  e.g.,  banknotes!– http://www.wheresgeorge.com/

[Bro06]  Brockmann,  Dirk,  Lars  Hufnagel,  and  Theo  Geisel.  "The  scaling  laws  of  human  travel."  Nature  439.7075  (2006):  462-­‐465.[Gon08]  Gonzalez,  M.  C.,  Hidalgo,  C.  A.  and  Barabasi,  A.-­‐L.,  “Understanding  individual  human  mobility  patterns”,  Nature  ,  Vol.  453,  2008,  pp.  779–782.  

• But  then  it  came  the  phone!– For  6  months,  100,000  individuals  selected  randomly  

from  a  sample  of  more  than  6  million  anonymized  mobile  phone  users• Each  time  a  user  initiated  or  received  a  call  or  a  text message,  the  

location  of  the  tower  routing  the  communication  was  recorded  (CDR  – Call  Detail  Record)

• Using  cell  tower  associations,  they  reconstructed  the  user’s  trajectory

Connectivity  properties• A  contact between  two  devices  implies  that  the  corresponding  users  are  close  to  

each  other.  – The  intercontact  time and  the  contact  time are  taken  as  measures  of  how  frequent  and  for  

how  long  two  users  spend  time  together

• 3  categories– traces  that  reflect  connectivity  with  already  existing  infrastructure like  Access  Points  (APs)  or  

base  stations  in  cellular  networks

– traces  collected  with  special  devices  (for  example  iMotes)  that  communicate  directly with  each  other  typically  via  Bluetooth

– traces  collected  by  tracking  people  through  GPS units  (not  included  in  the  table  below)

Many traces publicly available athttp://crawdad.cs.dartmouth.edu/

A  brief  history  of  intercontact  time• The  distribution  of  the  aggregate intercontact  time  has  a  power-­law  nature  over  a  wide  range  of  

values  from  few  minutes  to  half  a  day  (seminal  work  [Cha07])

– Then  suggested  by  [Kar10]  to  use  truncated  Pareto to  capture  the  exponential  decay

• The  aggregate  intercontact  time  is  in  general  not  representative  of  pairwise intercontact  times  [Pas13]

• We  repeated  the  analysis  at  the  pairwise  level,exploiting  techniques  targeting  small  samples

– the  Pareto  hypothesis  for  intercontact  timescannot  be  rejected  for  80%,  97%,  and  85.5%of  pairs,  respectively,  in  the  three  traces  considered.

• Infinite  boundary  effect:  a  theoretical  result  aboutheavy-­tailed  intercontact  times  emerging  dependingon  the  relationship  between  the  size  of  the  boundaryof  the  scenario  and  the  relevant  timescale  of  the  network[Cai09]

[Cai09]  H.  Cai and  D.  Eun,  “Crossing  over  the  bounded  domain:  From  exponential  to  power-­‐law  intermeeting  time  in  mobile  ad  hoc  networks,”  IEEE/ACM  Trans.  Netw.,  vol.  17,  no.  5,  pp.  1578–1591,  Oct.  2009[Cha07]  Chaintreau,  A.,  Hui,  P.,  Crowcroft,  J.,  Diot,  C.,  Gass,  R.  and  Scott,  J.,  “Impact  of  Human  Mobility  on  Opportunistic  Forwarding  Algorithms”,  IEEE  Transactions  on  Mobile  Computing  ,  Vol.  6,  2007,  pp.  606–620.  [Kar10]  Karagiannis,  Thomas,  J-­‐Y.  Le  Boudec,  and  Milan  Vojnovic.  "Power  law  and  exponential  decay  of  intercontact  times  between  mobile  devices."  Mobile  Computing,  IEEE  Transactions  on  9.10  (2010):  1377-­‐1390.[Pas13]  Andrea  Passarella,  Marco  Conti,  “Analysis  of  individual  pair  and  aggregate  inter-­‐contact  times  in  heterogeneous  opportunistic  networks”,  IEEE  Transactions  on  Mobile  Computing,  Vol.  12,  Issue  12,  December  2013

Intercontact  times  &  message  delay

• The  message  generation  process  and  the  contact  process  are  typically  assumed  to  be  independent– Thus,  the  time  when  a  node  generates  a  message  can  be  assumed  as  a  random  time  with  

respect  to  the  encounter  process

• Residual  time:  the  time  a  random  observer  has  to  wait  from  its  arrival  until  it  sees  the  next  event  of  a  stochastic  process– In  our  case  is  the  time  Rij (starting  from  a  random  point  in  time  t)  that  two  nodes  i and  j (not  in  

contact  at  t)  have  to  wait  before  meeting  again

– Rij is  called  residual  intercontact  time

• The  delay  experienced  by  a  message  is  the  weighted  sum  of  the  residual intercontact  times  along  all  possible  paths

Pareto  intercontact  times• A  Pareto  intercontact  time  Mij has  a  CCDF

• The  residual is  again  Pareto  distributed,  but  with  exponent  aij-­1

• Expectation of  Pareto  r.v.– Converges  only  for  aij>1  (ICT)– Converges  only  for  aij>2  (Residual  ICT)

• The  mean  of  a  Pareto  intercontact  time  can  diverge,  hence  the  expected  delay  itself  can  diverge

• Looking  at  the  exponents  found  in  traces,  this  is  a  serious  issue  for  opportunistic  networks

Shape

Scale

Cambridge Infocom RollerNet

1.5

2.0

2.5

3.0

Pareto Exponent

The  stability  region  of  opportunistic  routing  protocols

• Stability  region:  the  Pareto  exponent  values  of  pairwise  intercontact  times  for  which  finite  expected  delay  is  achieved

Are  there  routing  approaches  that  guarantee  a  larger  stability  region?– i.e.  that  can  achieve  convergence  for  lower  exponent  values

• Chaintreau et  al.  [Cha07]  answered  the  question  assuming  homogeneous  intercontact  times,  i.e.  all  user  pairs  with  the  same  exponent  a

• However,  traces  tell  us  that  real  networks  are  not  homogeneous:  will  exponent  heterogeneity  help  the  convergence  of  the  expected  delay?

[SPOILER  ALERT:  Yes,  it  will]

[Bol15]  Chiara  Boldrini,  Marco  Conti,  Andrea  Passarella,  "The  Stability  Region  of  the  Delay  in  Pareto  Opportunistic  Networks",  IEEE  Transactions  on  Mobile  Computing,  vol.14,  no.  1,  pp.  180-­‐193,  Jan.  2015

a > 2 1<  a < 2 a < 1

always it  depends on  the  number  of  copies never

Goal  &  Network  Model• Classes  of  routing  strategies:

– Randomised (aka  social-­oblivious)  vs utility-­based  (aka  social-­aware)  routing  schemes• In  SO  schemes,  the  message  is  handed  over  to  the  first  node  encountered

• In  SA  schemes,  only  “better  forwarders”  are  picked

– Single  vs multi-­copy  schemes– Single  vs multi-­hop  schemes

• Questions  to  be  answered  (remember:  we  look  at  convergence  issues  only)– Are  social-­oblivious  schemes  better  than  social-­aware  schemes?– Is  it  convenient  to  send  more  than  one  copy  of  the  message?– Is  it  convenient  to  let  messages  traverse  up  to  n hops?

• We  assume:– N nodes  in  the  network– Copies  can  be  generated  by  the  source  node  only,  messages  can  be  exchanged  only  at  the  beginning  of  a  

contact– Bandwidth much  larger  than  data  transmitted,  buffer space  isn’t  an  issue– Pi – set  of  all  nodes  that  can  be  encountered  by  node  i– Social-­aware  policies  are  abstracted  into  the  following:

• Node  i hands  over  the  message  to  node  j  if  E[Mjd]  <  E[Mid]

• Ri – set  of  all  nodes  that  encounter  the  destination  more  frequently  than  node  i

SINGLE-­COPY  SCHEMES

1 copy,  1 hop  (Direct  Transmission)

• Consider  a  message  with  source  S and  destination  D

• A  message  generated  by  S  has  to  wait  a  time  Rsd before  it  is  delivered

• The  expectation  of  Rsd is  finite  only  if  its  exponent  is  greater  than  2

Residual  intercontact  time

SO:  1 copy,  2 hops  (Two-­Hop)

2

1

4

3

min{Rsj}→ Par (αsjj∈Ps

∑ −1)%

&''

(

)**⇒

⇒ αsjj∑ − P s >1⇒ αsj

j∑ >1+ P s

Par α jd −1( )⇒α jd −1>1⇒

⇒α jd > 2

C1

C2

SA:  1 copy,  2 hops  (Two-­Hop)

2

1

4

3

min{Rsj}→ Par αsjj∑ −R s$

%&&

'

())⇒

⇒ αsjj∑ −R s >1⇒ αsj

j∑ >1+R s

Par α jd −1( )⇒α jd −1>1⇒

⇒α jd > 2

C1

C2

SO:  1  copy,  n hops

2

1

4

3

2

1

4

3

C1 C2

SA:  1  copy,  n  hops

s

i

zj

d

…

R s

R i

R j

R z

Convergent “circles”

In the worst case it takes n hop to reach the convergent circles

C6 guarantees the convergent delivery to any node of the next inner set

C7 guarantees that the convergent circles are reached

MULTI-­COPY  SCHEMES

SO:  m copies,  2 hops

2

1

4

3

Intuitive  proof

1st step

2nd step

3rd step

Convergence becomes more difficult at the first hop: the source cannot send more than

maxi convergent copies

Convergence becomes easier at the second hop: there needs to be mini copies for the second hop to

converge

Overall convergence is

achieved ifmaxi ≥ m ≥ mini

Overview  of  main  results

• First,  we  can  write  off  those  schemes  that  do  not  improve  convergence  and  consume  more  resources

• Then,  we  can  answer  the  questions  that  motivated  this  work:– Are  social-­oblivious  schemes  better  than  social-­aware  schemes,  or  vice  versa?

We  are  able  to  prove  mathematically  that there  is  no  clear  winner  between  the  two,  since  either  one  can  achieve convergence  when  the  other  one  fails,  depending  on  the  underlying  mobility  scenario  

– Is  it  convenient  to  send  more  than  one  copy  of  the  message?

For  both  SO  and  SA,  we  find  that  multi-­copy  strategies  improve  convergence  over  single-­copystrategies

– Is  it  convenient  to  let  messages  traverse  up  to  n hops?

SO:  if  convergence  can  be  achieved,  two  hops  are  enough  for  achieving  it

SA:  using  n hops  can  help SA  schemes,  and  make  them  converge  in  some  cases  when  all  other  social-­aware  or  social-­oblivious  schemes  diverge.  

From  theory  to  practice

Cambridge Infocom RollerNet

1.5

2.0

2.5

3.0

Pareto Exponent

Nodes can evaluate online whether a policy

can achieve convergence or not,

and then decide which one is to be preferred.

Long  story  short• Opportunistic  networks  are  a  recent  evolution  of  mobile  ad  hoc  networks.  They  can  

be  used  as:– Standalone  solution  for  localized  communications

– Capacity  enhancers  when  offloading  the  cellular  network

• Opportunistic  networks and  user  mobility are  tightly  intertwined– The  intercontact  time  plays  a  crucial  role  in  the  delay  experienced  by  messages

– With  Pareto  intercontact  times,  there  can  be  unpleasant  surprises  regarding  the  expected  delay

• We  need  to  make  sure  that  the  routing  protocol  we  choose  for  our  network  is  able  to  address  convergence  issues  successfully  – We  have  derived  the  stability  regions  for  a  representative  class  of  forwarding  strategies

– These  conditions  can  be  used  both  online  and  offline  to  select  the  strategy  that  can  achieve  convergence  for  the  target  scenario