routing in opportunistic networks - chalmers€¦ · 1 19.03.2009 © supsi-dti 1 dipartimento...

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Routing in Opportunistic Networks

Silvia [email protected] in collaboration with:

Marco Conti, Jon Crowcroft, Pan Hui, Hoang Anh Nguyen, Andrea Passarella

and the whole Haggle project

thanks to Cecilia Mascolo and Martin May

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Outline� Internet: where are we?

� ubiquitous computing/communication

� clean-slate approach

�New Networking Technologies� Haggle

� DTN

� Opportunistic Networks

� Routing in OppNets� context based routing

� C-BR classification

� SANETs

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Ubiquitous Computing: Business Users

�users can work without interrupting their work even when they move

�users must interrupt their work when they move

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Ubiquitous Computing: Other Users�visiting people

�young people

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First dream

M. Conti and S. Giordano. Multihop Ad Hoc Networking: The Theory.IEEE Communications Magazine, 2007

Example: realize the ubiquitous computing with MANET technology.

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Missing a pragmatic approach!

Example: realize the ubiquitous computing with MANET technology.

M. Conti and S. Giordano. Multihop Ad Hoc Networking: The Reality.IEEE Communications Magazine, 2007

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But pervasiveness is a reality

ChatpenDigital Information recognized and transmitted by penpen

CeivaDigital Picture Frame

Plug &Play640 x 480 VGAJPEG Image FormatStorage: 10 photos/250 on-line

Plug &Play640 x 480 VGAJPEG Image FormatStorage: 10 photos/250 on-line

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Clean-slate approach�Heterogeneous networks technologies

�Heterogeneous applications

� New architecture and communications paradigms:� DTNs

� ANA

� Haggle: �opportunistic networks,

� social networks

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The Internet Hourglass

IP

Applicationlayer

Linklayer

Ethernet, WIFI (802.11), ATM,SONET/SDH, FrameRelay,modem, ADSL, Cable, Bluetooth…

Disruptive approaches need

a disruptivearchitecture

Changing/updatingthe Internet coreis difficult orimpossible !(e.g. Multicast,Mobile IP, QoS, …)

Everythingon IP

IP on

Everything

Homogeneous networking abstraction

Voice, Video, P2P, Email, youtube, ….Protocols – TCP, UDP, SCTP, ICMP,…

IPv6

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Approach 1: ANA

�We have to widen the waist and host more paradigms

�Federation instead of homogeneous abstraction

�May the best concept wins

[email protected] 10

Applicationlayer

Linklayer

We need the framework for these multiple networks

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ANA ≠ "one-size-fits-all"� A network compartment implements the operational rules and administrative policies for a given communication context

� Publish Subscribe Compartment� PodNet compartment

ANA framework

Application

Linklayer

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Approach 2: DTN

�Support interoperabilityacross ‘radically heterogeneous’ networks

�Tolerate delay and disruption� Acceptable performance in high

loss/delay/error/disconnected environments

� Decent performance for low loss/delay/errors


Applicationlayer

Linklayer

Still IP based

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Delay-Tolerant Networking Architecture*

�Components� Flexible naming scheme

� Message abstraction and API

� Extensible Store-and-Forward Overlay Routing

� Per-(overlay)-hop reliability and authentication

*from DTN group slides

Still IP based

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Village

Internet

City

Example Routing Problem *2

3 1

bike

time (days)

Connectivity: Village 1 – City

bandwidth

bikesatellitephone

*from DTN group slides

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DTNs: What ?�Network with intermittent connectivity

�Data are disseminated exploiting host mobility

� long and variable delays

� high error rates

� heterogeneity

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DTNs: Why ?�To cover regions with no infrastructure

�Human social networks

�Intelligent highways

�Emergency rescue

�Sensor networks

�Wildlife monitoring

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Approach 3: Haggle�new autonomic networking architecture

� enables communication for intermittent network connectivity,

� exploits autonomic opportunistic communications

(i.e., when no end-to-end communication infrastructures).

�support interoperabilityacross ‘radically heterogeneous’ networks


Applicationlayer

Linklayer

No more layers!

Physical Link

ApplicationData

Haggle

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Haggle (2006-2010)

� Started January 2006 - EU FP6 – FET� developed 3 prototypes (Infant, Child & Young Haggle)

� code on sourceforge� http://www.haggleproject.org

Give it to me, I have 1G bytes phone flash.

I have 100M bytes of data, who can carry for me?

We can also carry for you!

Thank you but you are in the opposite direction!

Don’t give to me! I am running out of storage.

Reach an access point.

Internet

Finally, it arrives…

100MB

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� a revolutionary paradigm for communicating without end-to-end connection

� relax the end-to-end paradigm

User

controlHIS

ClassifierForward

802.11 Bluetooth EthernetCradle

Incentive

Authentication

Resource control

GPRS Connectivitymanagement

Incentive

Authentication

Resource controlHagglecontrol

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� a context based routing

�fwding manager with more strategies

Search La Bonheme.mp3 for me


There is one in my pocket…

Sorry, I don’t know any singer called Bonheme!

Uh! I love La Bonheme! (My wife should have it…)


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Opportunistic NetworksOpportunistic networking: we are surrounded by communicating devices and most of applications are not real time constrained

�relax the end-to-end paradigm�communication is “on-the-fly”� heterogeneous devices

� mobile phones�laptops, palm�sensors, etc...

� exploit mobility� real best-effort

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Opportunistic Networks – The Missing Link?

In oppnets, diverse systems—not deployed originally as oppnet nodes—join an oppnet dynamically in order to perform certain tasks they have been invited (or ordered) to participate in

�Not always connected, “internet connectivity islands”�Huge amount of untapped resources in portable devices

� Local wireless bandwidth� Storages� CPUs

�A packet can reach destination using network connectivity or user mobility

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CONTEXT

� Oppnets are envisioned to provide, among others:� Bridges between disjoint communication media

� Additional platforms for offloading tasks

� Additional sensing modalities by integrating existing independent sensory systems

� Oppnets can exploit context information




There is one in my pocket…

Objectives

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OppNets routing: context information

� the context in which the users communicate. � Examples:

� users' working address and institution, personal information, habits

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OppNets routing: context information

� Thus:� given context information about the destination

� suitable forwarders can be found based on:� the probability of meeting with other users

� the probability of visiting particular places

I’ve a message for Jack

Jack is at school school bus is just coming

I’ll wait the school bus and give the message to someone in it, who is going to school, who is likely to meet Jack in few minutes

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Context aware routing classification

� context-oblivious:� basically exploit some form of flooding

� partially context-aware (aka mobility-based):� exploit some particular piece of context information to optimise the forwarding task.

� fully context-aware (aka social context-aware):� not only exploit context information to optimiserouting, but also provide general mechanisms to handle and use context information.

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Context-oblivious Protocols

� exploit some form of flooding.

� a message should be disseminated as widely as possible.

� might be the only solution when no context information is available.

� generate a high overhead

� may suffer high contention and potentially lead to network congestion

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� To limit this overhead, the common technique is to control flooding � by limiting the number of copies allowed to exist in the network

� by limiting the maximum number of hops a message can travel.

� In the latter case, when no relaying is further allowed, a node can only send directly to the destination when (in case) it is met.

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� Epidemic Routing � A. Vahdat and D. Becker. Epidemic Routing for Partially Connected Ad Hoc Networks. Technical Report CS-2000-06, CS. Dept. Duke Univ., 2000.

� Network Coding � J. Widmer and J. Le Boudec. Network coding for efficient communication in extreme networks. Proc. of the ACM SIGCOMM WDTN , 2005.

� Spray&Wait� T. Spyropoulos, K. Psounis, and C. Raghavendra. Efficient routing in intermittently connected mobile networks: The multiple-copy case, ACM/IEEE Transactions on Networking, 16, 2007.

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� Epidemic Routing � When two nodes meet exchange summary vectors with compact unambiguous representation of the messages currently stored in their local buffers.

� Then, each node requests from the other the messages it is currently missing.

� hop count limits dissemination (hc = 1 -> only to dest.).

A BV[msg]a,b,d

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� Network Coding � an original approach to limit message flooding.

� In general, network coding-based routing outperforms flooding, as it is able to deliver the same amount of information with fewer messages injected into the network.

A B Ca acc a c a c+ +

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� Spray&Wait� two phases: the spray phase and the wait phase.

� spray phase: L copies of the same message are spread over the network

� wait phase: each relay node stores its copy and eventually delivers it to the destination when (in case) it comes within reach.

� L can is chosen based on a target average delay.� spray can be performed in multiple ways (e.g. binary)

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Partially Context-aware

� exploit some particular piece of context information to optimise the forwarding task.� e.g. mobility of nodes

� Main difference with fully context-aware:� generally is customized for a specific type of context information

� does not provide a full-fledged set of algorithms to gather and manage any type of context information

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Partially Context-aware� Probabilistic ROuting Protocol using History of Encounters and Transitivity (PROPHET)� A. Lindgren, A. Doria, and O. Schelen. Probabilistic routing in intermittently connected networks. ACM Mobile Computing and Communications Review, 2003.

� MV and MaxProp� B. Burns, O. Brock, and B. Levine. MV routing and capacity building in disruption tolerant networks. Proc. of the 24th INFOCOM,2005.

� J. Burgess, B. Gallagher, D. Jensen, and B. Levine. MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks. Proc. of the 25th INFOCOM, 2006.

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Partially Context-aware� Last Encounter Routing and Spray&Focus

� M. Grossglauser and M. Vetterli. Locating nodes with EASE: last encounter routing in ad hoc networks through mobility diffusion. Proc. of the 22nd INFOCOM, 2003.

� T. Spyropoulos and K. Psounis. Spray and focus: Efficient mobility-assisted routing for heterogeneous and correlated mobility. Proc. of IEEE Percom International Workshop on Intermittently Connected Mobile Ad hoc Networks, March 2007.

� MobySpace Routing� T. C. V. Leguay, J.; Friedman. Evaluating mobility pattern space routing for

dtns. Proc. of the 25th INFOCOM, 2006.

� Bubble Rap� P. Hui and J. Crowcroft. Bubble rap: Forwarding in small world dtns in

every decreasing circles. Technical report, Technical Report UCAM-CL-TR684, Univ. of Cambridge, 2007.

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Partially Context-aware� PROPHET

� evolution of Epidemic: during a contact, nodes also exchange their delivery predictability (DP) to destinations of messages they store in their buffers

� messages are requested only if DP is higher than that of the node currently storing the message.

� DP is the probability for a node to encounter a certain destination� increases when the node meets the destination

� decreases (according to an ageing function) between meetings

� context information used by PROPHET is the frequency of meetings between nodes

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Partially Context-aware� MV and MaxProp

� in addition, also exploit information about the frequency of visits to specific physical places.

� Last Encounter Routing and Spray&Focus� exploit the fact that the decreasing gradient of the time lag identifies a suitable path towards the destination.

� MobySpace Routing� the mobility pattern of nodes is the context information used for routing

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Bubble-Rap� automatically inferring the parameters of the underlying

social structure.

� exploiting the structure properties to select paths.

� dynamically identifies users’ communities, and ranks the nodes ”sociability” (measured as the number of links) inside each community.

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Fully Context-aware

� provide general mechanisms to handle and use context information

� more general than PCA:� works with any context information

� can be customized for the specific environment it must work

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Fully Context-aware� Context-Aware Routing (CAR)

� M. Musolesi, S. Hailes, and C. Mascolo. Adaptive routing for intermittently connected mobile ad hoc networks. Proc. of WoWMoM 2005.

� History Based Opportunistic Routing (HiBOp)� C. Boldrini, M. Conti, I. Jacopini, A. Passarella, and P. IIT-CNR. HiBOp: a

History Based Routing Protocol for Opportunistic Networks. Proc. of WoWMoM 2007

� Probabilistic Routing Protocol for Intermittently Connected Mobile Ad hoc Network (PROPICMAN/Spatio-Tempo)� H. Anh Nguyen, S. Giordano, A. Puiatti. Probabilistic Routing Protocol for

Intermittently Connected Mobile Ad Hoc Networks (PROPICMAN). Proceedings of IEEE WoWMoM/AOC 2007.

� H. Anh Nguyen, S. Giordano, Spatiotemporal Routing Algorithm in Opportunistic Networks. Proceedings of IEEE WoWMoM/AOC 2008

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Context-Aware Routing (CAR)� Nodes are divided in groups

� Nodes inside the same group havesocial links between each other

� Are connected by an underlying MANET routing protocol

� Nodes of different groups can have social links as well

� Nodes reconfigure

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CAR� A node selects the fwder node/group

� Probabilistically

� Based on the social attraction among nodes

� multi-attribute utility-based framework is defined to combine context information for computing the delivery probabilities

� CAR compute delivery probabilities proactively, and disseminate them in their ad hoc cloud.

� context information is exploited to evaluate probabilities just for known destinations

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CAR: conjunction beteewn PCA & FCA � The framework is general enough to accommodate different types of context information.

� Context information in CAR consists of the logical connectivity of nodes, such as the rate of connectivity change, the delivery probability of the neighbor nodes to the destination, and device information, such as residual battery life.

� However, the social context information is not taken into consideration as in social context-based category

� CAR can be seen as a conjunction between mobility-based protocols and social context-based protocols.

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HiBOp� Mimic the establishment of social relationships among people

� Users describe themselves� Users decide what information about themselves and their habits they want to make publicly available

� users store this information into their own devices

� HiBOp is not bound to any predefined set of information

Personal InformationName DonaldSurname DuckEmail [email protected] 3403434398WorkStreet Via Moruzzi, 1City PisaInstitute IIT-CNR

attributes values

Identity Table

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HiBOp in a nutshell� Sender does several copies (k)of the message

� To achieve atarget delivery probability

� Messages are discarded after being handed over to the next hop� i.e., message spread is controlled by the target delivery probability

k = min K | (1− psucc( i ) )

i =0

K

∏ ≤ plmax

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Propicman: Probabilistic Routing Protocol for Intermittently Connected Mobile Ad hoc Network

�Exploit the context information of nodes to select the best next hop candidate.

�Each node has a common node profile withevidence/value pairs.

�Evidences have different weights.

�Compute the deliverity probability to thedestination.

�Zero-conf

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Propicman social approach� Common set of evidences for every node - T

� Node profile:

W2E2

……

WnEn

W1E1

Weights(W)Evidence names(E)

H(En,Vn)VnEn

………

H(E2,V2)V2E2

H(E1,V1)V1E1

Hashed valuesValuesEvidence names

5City

7Profession

……

2Nationality

10Work place

Weights(W)Evidence names(E)

An example:

14a12de02cd9c0c5fd94…

LuganoCity

536a195c27b76cc2b175…

SwissNationality

………

c056817c8f7177163c83…

Profession

26a48976c47c0d2b2383…

SUPSIWork place

Hashed valuesValuesEvidence names

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Sender

A

A1

A2

A3

hM

……

An

hMhMhMhM

PA1

PA2

PA3

PAn

PA*max{PAi}

Propicman

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Propicman� Neighbors do local matching between received hM with its node profile.

� Then neighbor node (A) can compute its PA:

0≤ PA = ≤1

� : sum of the weights of all the evidences that S knows about D.∑

∑Mi

i

W

Wmatched )(

∑ iMW

H(ED1,VD1) H(ED2,VD2) H(ED3,VD3) H(ED4,VD4) H(EDn,VDn) MACS SN

Message header hM

…

Hashed values in node profile

H(E1,V1) H(E2,V2) H(E3,V3) H(E4,V4) H(En,Vn)…

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Morning

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Afternoon

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Evening

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One cycle

a period

7am-8am 8am-9am 9am-12am 12am-1pm 1pm-5pm 5pm-8pm 8pm-7am

Similarity!

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SpatioTempo – Design

�Each period can have different contact opportunities. In some periods source nodeencounters few devices, some encounterbunch of devices with high DP.

: period ith of the cycle c-1

: set of encountered nodes in period ith of the cycle c-11−ciS

1−ciT

11−

+c

iT1−ciT

One cycle

ciT 1−

1−ciS 1

1−

+ciS c

iS 1−

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Variation of the sets of encounternodes

�The variation of the set of encounter nodes in a period is indicated by

� is computed with respect to the past cycle (same period) and to the previous period (samecycle)

iEV

iEV

ci

ci

ci

ci

ci

ci

SS

SSSS

11

11

11

−−

−−

−−

∪

∩−∪=

11−

+c

iT1−ciT c

iT 1−

1−ciS 1

1−

+ciS c

iS 1−

iEV

iEV

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SpatioTempo – Design

�Each person is supposed to have a set offrequent contact persons (FD).

�The source node S wants to send a message M to the destination D. There are 2 cases:� D belongs to FD

� D is an occasional destination (D belongs to OD)

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Recording task

11−

+c

iT1−ciT c

iT 1−

Slidingwindowcycle

1−ciS

11−

+ciS

1−ciS

11−

+ciS

1−ciS

12

−+ciS

ciS 1−

11−

+ciS

1−ciS

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Compute the number of retransmissions

�D belongs to OD: Basically use Propicmanto select the next hop with the number (F) of rebroadcasting , depends on thebattery level (b), the priority level ofmessage ( ) and in each period.

0 if b < (0< <1)F=

round( * * ): number of units of time in the period

iEV

HM

iEV

µ

ββ

µit

it

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D belongs to FD� Delivery probability of a period:

Delivery probability to reach the destination if the sendersends the message in this period.

� The number of retransmissions:

F= round( )

, : maximum and minimum number of times that S isable to send during the periodmF

1*

*)(**

++−+

ic

i

mc

imMic

iM

EVDP

FDPFFEVDPF

ciDP

ciDP

ηη+

+= −−

1

* 11 c

ici DPDP

MF

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D belongs to FD�Given is number of units of time in the period

�And if S can resend each unit of time in theperiod, then equals

F = round ( ) 1*

1*)1(**

++−+

ic

i

cii

cii

EVDP

DPtEVDPt

MF itHM

it

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11−

+c

iT1−ciT

Cycle

ciT 1−

S S S S S SHM

HM

HMHM

HM

HM

HM

DP

DP DP

DP

maxDP

maxDP

maxDP

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OD/FD management & Message lifetime� After one cycle, nodes review its OD, FD lists� Each member in FD, OD of S has a frequency level (FL), whichindicates how often S has a message to it.

� Message lifetime:

: The duration of time of a cycle

TTTL CM *µ=

OD FD

: demotedδ>DFL

: promotedδ>DFL: removedδ>DFL

TC

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Predict next best interval

�Having information on the past cycle and past interval, we can predict next best interval

11−

+c

iT1−ciT c

iT 1−

1−ciS 1

1−

+ciS c

iS 1−

ciDP

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Opportunistic Sensor Networks� Context oblivious: Data Mules, SWIM, ZebraNet, Message Ferrying, DFT-MSN

� Z. Zhang. Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. IEEE Communications Surveys & Tutorials, 2006.

� R. C. Shah, S. Roy, S. Jain, W. Brunette. Data MULEs: Modeling a three-tier architecture for sparse sensor networks. SNPA, 2003.

� Muhammad Mukarram Bin Tariq, Mostafa Ammar, Ellen Zegura. Message Ferry Route Design for Sparse Ad Hoc Networks with Mobile Nodes. In Proceedings of ACM Mobihoc06, 2006.

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Opportunistic Sensor Networks

�Context aware� SCAR = Sensor based Context-Aware Routing

�Opportunistic Mobile Sensor Data Collection with SCAR. Bence Pasztor, Mirco Musolesi and Cecilia Mascolo. In Proceedings of MASS 2007

� PROSAN: PRobabilistic Opportunistic Routing in SANets

� Probabilistic Opportunistic Routing in SANETs. A.H. Nguyen, S. Giordano. In Proceedings of ACM SANET – MobiCOM2007.

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Motivation�Pervasiveness and repeating behavior are true also in SANET:

� people who repeatedly stay and pass in area A are the ones who most likely want, need and will support a reaction.

�By naturally acting, people can enable the communication between sensors and actuators, and trigger the reaction.

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Motivating Scenario

�same for animals

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SCAR�Mobile sensors collect information and try to deliver this opportunistically to one of the sinks� Energy is an issue (batteries cannot be changed so easily)

�They exploit “best” carriers to deliver the information back to sinks� Choice of the best carriers is key to the protocol: SCAR uses utility theory and Kalman Filter based prediction of various attributes to choose

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Assumptions

�Data is reasonably delay tolerant

�Clocks of the sensors are (reasonably) synchronized and the sensors wake up at regular intervals

�Sensors are all cooperating (possibly owned by same developer)

�Mobility is not source of energy consumption

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The key ideas of SCAR�Each sensor collects and stores its data

�Each sensor will try to deliver its data in bundles to a number of neighbouring sensors which seem to be the best carriers to reach a sink

�Decision process based on the prediction of the evolution of the system:

� mobility of nodes

� colocation with the sinks

� battery level

�The forecasted values of the context attributes are then combined to define a probability P(si) of the sensorsi of delivering the bundles to one of the sinks

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Multiple-carrier Selection�Each node maintains a list of the neighbouring nodes (including itself) ordered according to their delivery probabilities

�Each source node then replicates the bundle to the first R nodes (R-1 nodes if the node itself is in the first Rpositions of the list)

� Data bundles are only replicated on R nodes by the source, while they are forwarded (deleted from a node and copied on another) if the carriers, while roaming, find either a sink or abetter carrier.

� The value of R is specified by the user and can be considered as a priority level associated to the data retrieved by the sensor

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Bundle Forwarding

�Each node keeps monitoring if there are neighbours with better probability of delivery than its own

�If this is the case the data bundles are shifted to the other nodes

�Key problem: intelligent replication

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Forecasting Techniques for Probabilistic Routing

�Each node calculates its delivery probability using multi-criteria decision theory over the evolution of its context described by a set of attributes.

�In particular, we consider three attributes:� Colocation with the sinks� Change degree of connectivity (ie how many nodes have been

seen in a fraction of time)� Battery level

�Our aim is to maximize the combined utility function(i.e., selection of the nodes with the best trade-off between these attributes)

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Prosan design�Based on user profiles

� Using users’ social behavior – node profile - to predict the mobility of nodes.

�Routing with Zero Knowledge� Sender does not need to know any information about other nodes – no synchronizing, no local table.

� Only senders broadcast helloing messages.

�No sharing user information� Nodes does not need to share, periodically update their information to others.

� Keep privacy amongst members.

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Forwarding strategy�Delivery probability (DP): The probability of a node to meet and forward a message to other nodes toward destination.

�The sender S wants to send a message M to the destination actuator D, S will choose the route that has the highest delivery probability (DP) to send M to D.

�Before sending the message content M to the destination D, S sends M’s header to its neighbors.

hM = (H(EDi,VDi), MACS, SNM) (1)Example:26a48976c47c0d2b2383a34010eae937ebcf8d1f6c554b9f7214a12de02cd9c0c5fd9486e7967327b60f6d2b008ff1c110c09b62a536a195c27b76b17564e92f25260a3c6599f05fef87f3110b7f171871aef40a2b17fc1bbf88053aacea724c0a08419a9f211b077bcc244c056817c8c4ca4238a0b923820dcc509a6f75849b

n

iionConcatenat

1=

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Forwarding strategy (cont)

Sender

AhM

……

B

hMhMhMhMhMhMhM

H(En,Vn)VnEn

………

H(E2,V2)V2E2

H(E1,V1)V1E1

Hashed values

Values

Evidence names

Node profile

H(ED1,VD1)

H(ED2,VD2)

H(ED3,VD3)

H(ED4,VD4)

H(EDn,VDn)

MACS SN

Message header hM …


H(E1,V1)

H(E2,V2)

H(E3,V3)

H(E4,V4)

H(En,Vn)

…

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Forwarding strategy (cont)� Neighbors do local matching between received hM with its node profile.

� With each matching, neighbor nodes look up in T to get the corresponding weight. Then neighbor node (A) can compute its DPAas follows:

0≤ DPA = ≤1

� : sum of the weights of all the evidences that S knows about D.

H(ED1,VD1)

H(ED2,VD2)

H(ED3,VD3)

H(ED4,VD4)

H(EDn,VDn)

MACS SN

Message header hM

…


H(E1,V1) H(E2,V2) H(E3,V3) H(E4,V4) H(En,Vn)…

∑∑

Mi

i

W

Wmatched )(

∑ iMW

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Forwarding strategy (cont)

Sender

AhM

……

B

hMhMhMhMhMhMhM

0≤ DPN = ≤1

DP

DP

DPDP

DP

DP

DP

∑∑

Mi

i

W

WMatched )(

Delivery Probability DP

� S will choose the route(s) with highest DP it receives from its neighbor nodes

� S only sends the message to these routes if DProute > DPS

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Simulation environment� Hybrid P-MULEs

� nodes with higher capacity� use Prosan� move as MULEs

� Simulation area� rectangle 870x520 meters� divided in 6 areas� 50 nodes (1 p-mule)� use sleep mode

� Simulation approach� static actuator� mobile actuator

trajectory of p-mule

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Overhead

0200400600800

10001200140016001800

1 30

Tests

Byt

es

P-Mules

PROSAN

Hybrid P-Mules

Delay

0

10000

20000

30000

40000

50000

1 30

Tests

Mse

c P-Mules

PROSAN

Hybrid P-Mules

Simulation results for static actuator

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Overhead

0

200

400

600

800

1000

1200

1400

1 30

Tests

Byt

es

P-Mules

PROSAN

Hybrid P-Mules

Delay

0

10000

20000

30000

40000

50000

60000

70000

1 30

Tests

Mse

c

P-Mules

PROSAN

Hybrid P-Mules

Simulation results for mobile actuator

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Conclusion�context information in opportunistic routing can be very advantageous

�exploit effectively the mobility, the social connections and the community relationships:

� reduce significantly the number of nodes involved in� reduce the number of broadcasts� efficiently control message lifetime

�assumption for effectively using context information� people move in a predictable fashion based on repeating behavioral

patterns at different timescales (day, week, and month) � or these patterns are known

� -> mobility as expression of the users social behavior�Haggle architecture for supporting context aware routing

routing in opportunistic networks - chalmers€¦ · 1 19.03.2009 © supsi-dti 1 dipartimento...

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