taiwan italy boldrini v1 - cnr · what+are+theyfor? • mobile+social+networks –...
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Opportunistic Networking(or when there’s mobility there’s a way)
Taiwan-Italy Bilateral Workshop on Smart Cities
Chiara Boldrini – [email protected] 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
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
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
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