OPS – An Opportunistic Networking Protocol Simulator for OMNeT++

OMNeT++ Community Summit 2017 University of Bremen, Bremen, Germany

September 07 – 08, 2017

Asanga Udugama, Anna Förster, Jens Dede, Vishnupriya Kuppusamy and Anas bin Muslim

University of Bremen, Germany

Contents   Motivation

  Opportunistic Networks

  Opportunistic Networking Protocol Simulator (OPS)

  Evaluations

  Summary and Future Work

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Motivation

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Motivation   Internet of Things (IoT)

  Over 50 billion devices by 2020 [1]

  Architecture for communications in the IoT   Opportunistic Networking

IoT Scenarios   Social networking to emergencies   Nature of applications – higher value of information in locality

  Importance of information propagation   Forwarding protocols – Epidemic Routing, ODD, etc.

  Necessity for large-scale evaluations   Require simulators – OMNeT++

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Opportunistic Networks (OppNets)

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Characteristics of OppNets   Information dissemination

  Interested parties wanting information   Value of information higher around the source

  Store-and-Forward architecture   Communicate when there is an opportunity to communicate   Delayed delivery of information

  Use of peer-to-peer communication technologies   E.g., Bluetooth, IEEE 802.15.4

  Importance of the information propagation   Capabilities of the forwarding protocol

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OppNets Use-case   Propagation of information about an event

  Street performers   Interested people gather (flash crowd)

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City Center

Visitor Street performersBuilding

time

Direction of messagesIntensity of the performance

Opportunistic Networking Protocol Simulator (OPS)

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Objectives   Pluggable protocol layer architecture

  Node model can handle new protocol implementations   Clear interface between layers

  Large-scale simulations IoT-scale devices

  Mobility   Synthetic, trace-based and hybrid

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Protocol Stack   Node model – 4 layer protocol stack

  Protocol layers   Application layer – Data generators   Forwarding layer – Data propagation mechanisms   Link Adaptation layer – Conversions to different link technologies   Link layer – Link technology coupled with mobility

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Application Layer

Opportunistic Forwarding Layer

Link Adaptation Layer

Link Layer Mobility

Models   Application layer

  Promote – Generates random data as constant traffic, uniformly distributed traffic or exponentially distributed traffic   Herald – Generates pre-determined set of data where nodes assigned “likeness” value to data

  Opportunistic forwarding layer   Caching data – Employs store-and-forward   Neighborhood communications – Communications with the changing neighborhood   Epidemic Routing – Nodes negotiate and exchange data [2]   Organic Data Dissemination (ODD) – Dissemination of data based on popularity of data [3]   Randomized Rumor Spreading (RRS) – Random dissemination of data

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Models …contd   Link adaptation layer

PassThru – Simple packet traversal

  Link layer WirelessInterface – Simple wireless interface that models bandwidth, delays, wireless range (with UDG) and queuing

  Interfaces   Use of an extensible packet format

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Node Model Implementation   An example node model used in an experiment

  Use of trace based mobility BonnMotion – Cartesian trace of an actual GPS trace – SFO Taxi trace [4]

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Evaluation Metrics   Focus of performance evaluations is slightly different compared to classical networks

  Data related metrics   Liked Data – Preferred data received   Non-liked Data – Not preferred but still received   Traffic Spread – How well is packet traffic spread in the network   Data Delivery Ratio – Delivery ratio of destined data   Delivery Time – Delivery time of destined data

  Mobility related metrics   Average Contact Time – Duration of a contact   Number of Contacts – Number of times in contact

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Evaluations

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Evaluation Scenario   OPS is being used extensively in our research

  Results of some evaluations   Used in an IEEE Survey on OppNets [5]

  General scenario details   Nodes – 50-node network   Mobility – SFO Taxi Trace [4]   Data generation – 2 hour interval   Run for 24 days

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Influence of Traffic Models & Caching   Scenario specific parameters

  Different traffic generation models and different cache sizes   Evaluation of data delivery times

  Analysis   Traffic generation model has no influence   But, caching policy influences delay

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0 h 6 h 12 h 18 h 24 h 30 h 36 h 42 h 48 h 54 h 60 hDelay

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Constant Tra�c, 100KB Cache Sizes

Constant Tra�c, 10KB Cache Sizes

Constant Tra�c, Infinite Cache Sizes

Constant Tra�c, 20KB Cache Sizes

Constant Tra�c, 50KB Cache Sizes

Poisson Tra�c, 100KB Cache Sizes

Poisson Tra�c, 10KB Cache Sizes

Poisson Tra�c, Infinite Cache Sizes

Poisson Tra�c, 20KB Cache Sizes

Poisson Tra�c, 50KB Cache Sizes

Uniform Tra�c, 100KB Cache Sizes

Uniform Tra�c, 10KB Cache Sizes

Uniform Tra�c, Infinite Cache Sizes

Uniform Tra�c, 20KB Cache Sizes

Uniform Tra�c, 50KB Cache Sizes

Performance of Mobility Models   Scenario specific parameters

  3 different mobility models (synthetic, trace-based and hybrid)   Models parameterized as closely as possible to trace-based model

  Analysis   Trace-based takes the longest time (but realistic)   Closest performance is given by the hybrid model (SWIM)

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Fig. 4. CDF of the Delivery Delay of Data to Recipients using DifferentTraffic Generation Models and Cache Sizes

mobility of the nodes is performed using the BonnMotionmobility model of INET that is configured to use a 50-nodeextract of the San Francisco taxi cab trace (SFO trace) [11].The simulation time was 30 days and the applications deployedin each of these nodes generate data with a mean interval of2 hours.

Table I shows a comparison of the performance of thenodes in an OppNets network when using different mobilitymodels, viz., a simple random model, a trace based modeland a hybrid model implemented through Random Waypoint(RWP), Bonn Motion [12] and Small Worlds in Motion(SWIM) [13] [14], respectively. The Bonn Motion model usesthe SFO trace. As before, the network consist of 50 nodes anda simulation duration of 30 days.

Model RWP SWIM Bonn MotionSimulation Time 4 min 59 min 109 min

Memory used 74 MB 86 MB 127 MBAverage Delivery Rate 3 % 96% 92 %Average Delivery Delay 20.6 h 16.25 h 13.16 h

Total Number of Contacts 190 46,752 155,757Average Contact Duration 117.14 sec 150.12 sec 584.39 sec

Table I. Performance results of different mobility models consisting of asimple random model (RWP), a trace based model (Bonn Motion) and a

hybrid model (SWIM), with OPS

The applications deployed on the nodes generate data every2 hours and the data carry a specific destination to whichthe data must be delivered. Further, the RWP and the SWIMmodels are configured as closely as possible to represent thebehaviour of the SFO trace. The results show that mobility withreal traces require a higer level of resources (simulation clocktime and memory usage) compared to other mobility models.When considering the performance of the nodes, it is seen thatthe SWIM model provides the most closest results (AverageDelivery Rate, Average Delivery Delay, etc.) to the trace basedmodel. The trace based model is considered the most realisticdue to the traces being collected from real mobility patterns.

The survey [10] referenced before included the perfor-mance results of other widely used OppNets simulators [15],[16] in addition to OPS. The results show that OPS provides

a comparatively close performance to these simulators whenconsidering the metrics listed in Section IV.

VI. CONCLUSION

The Opportunistic Protocol Simulator (OPS) is an OM-NeT++ based modular simulator to evaluate the performanceof OppNets. The models in OPS implement the functionalityof the different layers of the protocol stack of an OppNetsbased node. The work presented here provides the details ofthe different models available in OPS.

OPS is used by us to simulate OppNets based scenarios[10] to evaluate the performance of dissemination protocols,traffic generation models, etc., to improve the performance ofOppNets in the IoT. OPS is released at Github under a GPLLicense (https://github.com/ComNets-Bremen/OPS).

OPS is being extended with new functionality on a regularbasis. Currently, there are a number of on-going projectsfocusing on areas related to forwarding protocols, applications,mobility models and user behaviour models. A long-term goalis to integrate OPS with the link technologies available withthe INET framework.

REFERENCES

[1] D. Evans, Cisco, The Internet of Things: How the Next Evolution of theInternet Is Changing Everything, April 2011

[2] A. Forster et al, A Novel Data Dissemination Model for Organic DataFlows, MONAMI 2015, September 1618, 2015, Santander, Spain

[3] L. Pelusi and A. Passarella and Marco Conti, Opportunistic Network-ing:Data Forwarding in Disconnected Mobile Ad Hoc Networks, IEEECommunications Magazine, November 2006

[4] A. Vahdat and D. Becker, Epidemic Routing for Partially-Connected AdHoc Networks, Technical Report, June 2000

[5] O. R. Helgason and K. V. Jonsson, Opportunistic Networking in OM-NeT++, SIMUTools 2008, March 03 - 07, 2008, Marseille, France

[6] O. R. Helgason and S. T. Kouyoumdjieva, Enabling Multiple Control-lable Radios in OMNeT++ Nodes, SIMUTools 2011, March 21 - 25,2011, Barcelona, Spain

[7] S. T. Kouyoumdjieva et al, Caching Strategies in Opportunistic Networks,WoWMoM 2012, June 25 - 28, 2012, San Francisco, U.S.A.

[8] R. Zhang et al, OppSim: A Simulation Framework for OpportunisticNetworks Based on MiXiM, CAMAD 2014, December 1 - 3, 2014,Athens, Greece

[9] Z. Zhao et al, Performance Evaluation of Opportunistic Routing Pro-tocols: A Framework-based Approach using OMNeT++, LANC 2012,October 4 - 5, 2012, Medellen, Colombia

[10] J. Dede et al, Simulating Opportunistic Networks: Survey and FutureDirections, IEEE Communications Surveys and Tutorials, Accepted forpublication in 2017

[11] Michal Piorkowski at al, CRAWDAD dataset epfl/mobility (v. 20090224),downloaded from http://crawdad.org/epfl/mobility/20090224, https://doi.org/10.15783/C7J010, February 2009

[12] N. Aschenbruck et al, BonnMotion - A Mobility Scenario Generationand Analysis Tool, SIMUTools 2010, March 15 19, 2010, Torremolinos,Malaga, Spain

[13] A. Mei and J. Stefa, SWIM: A Simple Model to Generate Small MobileWorlds, IEEE INFOCOM 2009, April 19 - 25, 2009, Rio de Janeiro,Brazil

[14] A. Udugama et al, Implementation of the SWIM Mobility Model inOMNeT++, OMNeT Community Summit 2016, September 15 - 16,2016, Brno, Czech Republic

[15] A. Keranen et al, The ONE Simulator for DTN Protocol Evaluation,SIMUTools 2009, March 2 - 6, 2009, Rome, Italy

[16] N. Papanikos et al, Adyton: A network simulator for opportunisticnetworks, [Online]. Available: https://github.com/npapanik/Adyton, 2015

Verification of the Models   Survey compared OPS with 3 other OppNets implementations

  ONE [6], Adyton [7] and ns-3

  Analysis   OPS provides a comparatively close performance (in metrics listed above)

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Summary and Future Work

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Summary

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  OPS – OMNeT++ based modular simulator to evaluate the performance of OppNets

  Node model architecture with pluggable protocol layers

OppNets focused evaluation metrics

  Available at Github https://github.com/ComNets-Bremen/OPS

Future Work

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  Constant improvements, additions to OPS

  Current projects   Forwarding protocols (e.g. Spray and Wait)   Applications   User behavior models   Mobility models

References

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References

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[1] D. Evans, Cisco, The Internet of Things: How the Next Evolution of the Internet Is Changing Everything, April 2011 [2] A. Vahdat and D. Becker, Epidemic Routing for Partially-Connected Ad Hoc Networks, Technical Report, June 2000 [3] A. Förster et al, A Novel Data Dissemination Model for Organic Data Flows, MONAMI 2015, September 2015, Santander Spain [4] Michal Piorkowski at al, CRAWDAD dataset epfl/mobility (v. 20090224), downloaded from http://crawdad.org/epfl/mobility/20090224, https://doi. org/10.15783/C7J010, February 2009 [5] J. Dede et al, Simulating Opportunistic Networks: Survey and Future Directions, IEEE Communications Surveys and Tutorials, Accepted for publication in 2017 [6] A. Keránen et al, The ONE Simulator for DTN Protocol Evaluation, SIMUTools 2009, March 2 - 6, 2009, Rome, Italy [7] N. Papanikos et al, Adyton: A network simulator for opportunistic networks, [Online]. Available: https://github.com/npapanik/Adyton, 2015

Thank You.

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Questions?