ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2014

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1148

Opportunistic Networking

Congestion, Transfer Ordering and Resilience

FREDRIK BJUREFORS

ISSN 1651-6214ISBN 978-91-554-8953-3urn:nbn:se:uu:diva-223492

Dissertation presented at Uppsala University to be publicly examined in 2446, Polacksbacken,Lägerhyddsvägen 2, Uppsala, Monday, 9 June 2014 at 13:15 for the degree of Doctor ofPhilosophy. The examination will be conducted in English. Faculty examiner: ProfessorMario Gerla (UCLA, Computer Science Department).

AbstractBjurefors, F. 2014. Opportunistic Networking. Congestion, Transfer Ordering and Resilience.Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science andTechnology 1148. 47 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-8953-3.

Opportunistic networks are constructed by devices carried by people and vehicles. The devicesuse short range radio to communicate. Since the network is mobile and often sparse in termsof node contacts, nodes store messages in their buffers, carrying them, and forwarding themupon node encounters. This form of communication leads to a set of challenging issues that weinvestigate: congestion, transfer ordering, and resilience.

Congestion occurs in opportunistic networks when a node's buffers becomes full. To be able toreceive new messages, old messages have to be evicted. We show that buffer eviction strategiesbased on replication statistics perform better than strategies that evict messages based on thecontent of the message.

We show that transfer ordering has a significant impact on the dissemination of messagesduring time limited contacts. We find that transfer strategies satisfying global requests yield ahigher delivery ratio but a longer delay for the most requested data compared to satisfying theneighboring node's requests.

Finally, we assess the resilience of opportunistic networks by simulating different types ofattacks. Instead of enumerating all possible attack combinations, which would lead to exhaustiveevaluations, we introduce a method that use heuristics to approximate the extreme outcomes anattack can have. The method yields a lower and upper bound for the evaluated metric over thedifferent realizations of the attack. We show that some types of attacks are harder to predict theoutcome of and other attacks may vary in the impact of the attack due to the properties of theattack, the forwarding protocol, and the mobility pattern.

Keywords: Opportunistic Networking, Congestion, Transfer Ordering, Resilience, Testbed,WISENET

Fredrik Bjurefors, Department of Information Technology, Division of Computer Systems, Box337, Uppsala University, SE-75105 Uppsala, Sweden.

© Fredrik Bjurefors 2014

ISSN 1651-6214ISBN 978-91-554-8953-3urn:nbn:se:uu:diva-223492 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-223492)

to Negar

Acknowledgements

First of all I would like to thank my supervisors Professor Per Gunningbergfor his support and guidance during my work and Docent Christian Rohner forhis support and help with the experiments and articles.

Thanks to all members in the CoRe group that I had the opportunity towork with, current and previous, for all the discussions and support over theyears. Liam McNamara who has given valuable feedback on the thesis, Fred-erik Hermans, Hjalmar Wännerström, Volkan Cambazoglu, Martin Jacobsson,Lars-Åke Nordén, Edith Ngai, Olof Rensfelt, Laura Feeney. A special thanksto Ioana Rodhe who I shared office with for several years and many entertain-ing discussions.

During the work in the CoRe group I also worked with some talented masterstudent that visited our group. A thanks goes to, Tom Homewood for hissupport in an undergraduate project, Sam Tavakoli for the work on congestion,and Benjamin Langlotz for his spelling skills.

I would like to acknowledge the funders of my work; Vinnova center ofexcellence WISENET, the Haggle project, and ResumeNet. In the Haggleproject I had the chance to work with Kaustubh Phanse, Oskar Wibling, ErikNordström, and Daniel Aldman. In the ResumeNet project I had the privi-lege to work with Merkourios Karaliopoulos and Paul Smith in the work onresilience in opportunistic networks.

To our corridor mates in the Division of Signals and Systems, especiallyRikke Apelfröjd, Simon Berthilsson, Adrian Bahne, Annea Barkefors, Math-ias Grudén, Magnus Jobs, Marcus Ericsson, and Tomas Olofsson. Thanks forall the discussions, meaningful and not, and the recreational activities.

Last, I want to thank my family and Negar for their love and support throughout this long journey.

List of papers

This thesis is based on the following papers, which are referred to in the textby their Roman numerals.

I Fredrik Bjurefors, Per Gunningberg, and Christian Rohner. HaggleTestbed: a Testbed for Opportunistic Networks. In Proceedings of the7th Swedish National Computer Networking Workshop, Linköping,June 2011

II Fredrik Bjurefors, Per Gunningberg, Christian Rohner, and SamTavakoli. Congestion Avoidance in a Data-Centric OpportunisticNetwork. In Proceedings of ACM SIGCOMM Workshop onInformation-Centric Networking (ICN 2011), Toronto, Canada, August2011

III Christian Rohner, Fredrik Bjurefors, Per Gunningberg, LiamMcNamara, and Erik Nordström. Making the Most of Your Contacts:Transfer Ordering in Data-Centric Opportunistic Networks. InProceedings of the Third International Workshop on MobileOpportunistic Networking (MobiOpp 2012), Zurich, Switzerland,March 2012

IV Fredrik Bjurefors, Merkourios Karaliopoulos, Christian Rohner, PaulSmith, George Theodoropoulos, Per Gunningberg. Resilience andOpportunistic Forwarding: Beyond Average Value. Special Issue onOpportunistic Networks. Computer Communications, 2014

Reprints were made with permission from the publishers.

Publications not included in thesis

• Fredrik Bjurefors, Richard Gold, and Lars-Åke Larzon. Performance ofPastry in a Heterogeneous System. In Proceedings of the Fourth IEEEInternational Conference on Peer-to-Peer Computing, Zurich, Switzer-land, August 2004

• Christian Rohner, Fredrik Bjurefors, Henrik Andersson. Sense the Key:Key Management for Mobile Devices. Demonstration (MobiSys 2006)

• Fredrik Bjurefors, Oscar Wibling, Christian Rohner, and Kaustubh Phanse.Testbed and methodology for experimental evaluation of opportunisticnetworks. In Proceedings of the 7th Scandinavian Workshop on Wire-less Ad-Hoc Networks, Johannesberg, May 2007

• Fredrik Bjurefors and Oscar Wibling. A Testbed for Evaluating DelayTolerant Network Protocol Implementations. In Proceedings of the 5thSwedish National Computer Networking Workshop, Karlskrona, May2008

• Erik Nordström, Daniel Aldman, Fredrik Bjurefors, and Christian Rohner.Search-Based Picture Sharing With Mobile Phones. Demonstration, SwedishNational Computer Networking Workshop, Uppsala, May 2009

• Erik Nordström, Daniel Aldman, Fredrik Bjurefors, and Christian Rohner.Using search to enhance picture sharing with mobile phones. Demon-stration (MobiHoc 2009)

• Erik Nordström, Daniel Aldman, Fredrik Bjurefors, Christian Rohner.Haggle - A Search-Based Data Dissemination Architecture for MobileDevices. Demonstration (MobiSys 2009)

• Fredrik Bjurefors, Erik Nordström, Christian Rohner, and Per Gunning-berg. Interest Dissemination in a Searchable Data-Centric OpportunisticNetwork. Invited paper, European Wireless 2010, Lucca, Italy, April2010

• Maria Mehrparvar, Fredrik Bjurefors, Christian Rohner, and Paul Smith.On Resilience in Opportunistic Networks. In Proceedings of the 8th

Swedish National Computer Networking Workshop, Stockholm, June2012

• Fredrik Bjurefors, Merkourios Karaliopoulos, Christian Rohner, PaulSmith, George Theodoropoulos, Per Gunningberg. Resilience and Op-portunistic Forwarding: Beyond Average Value. In Proceedings of theInternational Workshop on Challenged Networks (CHANTS’13), Mi-ami, USA, September 2013

Implementations• Haggle Testbed. A Linux based testbed built on virtual computers where

network traffic is filtered on a virtual Ethernet segment.

• Contact-driven simulator for opportunistic networks. An opportunisticnetwork simulator that is capable to simulate both data-centric (Haggle)and host-centric networks.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.1 Opportunistic Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.1.1 Data-Centric Opportunistic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2 Research Challenges and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.1 Congestion in Opportunistic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2 Transfer Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 Evaluating Resilience in Opportunistic Networks . . . . . . . . . . . . . . . . . . . . . . . 18

3 Forwarding Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.1 Oblivious Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Knowledge Based Forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4 Node Congestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.1 Buffer Evictions Using Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.2 Buffer Size Advertisements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.3 Data-Centric Node Congestion Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5 Transfer Ordering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6 Resilience in Opportunistic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

7 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317.1 Experimental Testbeds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

7.2.1 Message-Driven Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327.2.2 Contact-Driven Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337.2.3 Event-Driven Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

8 Summary of Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358.1 Paper I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358.2 Paper II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368.3 Paper III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378.4 Paper IV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

9 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

10 Summary in Swedish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

1. Introduction

Internet connections are ubiquitous today. At home, computers, smartphones,and tablets are connected to the Internet through the socket in the wall orthrough WiFi. When we are on the move, cellular networks combined withWiFi hotspots, in coffee shops and other public places, give us the ability touse the Internet. All these technologies that enable us to communicate arebased on infrastructure, either wired or wireless. These infrastructures have tobe implemented in a target area before communication can take place. Whena network is in place and configured, people can start to communicate.

In the current Internet, communication means that two computers in thenetwork exchange messages from one end-point to another [45] with a shortdelay and a small variation in delay on a predefined path. However, there arestill areas where infrastructure is not built, and not worth building. In these,mostly uninhabited areas, another paradigm of communication has to be im-plemented. To enable communication without infrastructure, devices can helpeach other to communicate. Data is wirelessly transmitted from one deviceto another. If the network is dense, a forwarding path can be created fromthe source to the destination. These networks are called mobile ad hoc net-works [3, 44], where routing paths are created dynamically as nodes move inphysical space. Nodes have to create end-to-end routing paths before mes-sages are sent.

So-called ad hoc routing protocols work well in dense networks where end-to-end paths can be created between the nodes in the network. On the otherhand, if the network is sparse, i.e., nodes are spread out in physical space andmost of the time nodes are out of communication range, intermediate nodeshave to store messages until forwarding opportunities arise due to node move-ment.

If we would like to extend Internet to rural areas and areas that are hardto reach, where no infrastructure exists, new types of communication is re-quired. Legacy networks use protocols that require end-to-end connectivity.To accommodate communication without infrastructure, each node has to, notonly forward, but also store messages until they can be passed on to anothernode [16]. In this manner, networks can be formed that use communicationopportunities that arise when users come close to each other and take advan-tage of the physical movement of users. Using these opportunities messagescan, not only be moved in the network by being transmitted between devices,but also by physical movement of the nodes. This type of network is called anopportunistic network.

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Opportunistic networks are used in many different applications and areas.For example, opportunistic networks have been used to collect data on andto study the behavior of animals [26, 50]. Sensors and radios are attachedto the animals and there are fixed collection points where data can be down-loaded to. Data from an animal that never comes close enough to the collec-tion point is forwarded through another animal. Opportunistic networks havealso been used to bring communication to areas in the world where no infras-tructure exists, areas such as rural India [20, 43] and the mountain regionsin northern Sweden [6]. Communication over long distances is possible bymovement of mopeds, buses and helicopters that relay data. Over the yearsseveral systems/architectures have been proposed for opportunistic networks,ranging from systems that disseminate application specific data to more gen-eral systems/architectures for communication in mobile and disruption proneconditions, e.g, PodNet [37], Haggle [46], MobiTrade [34], TACO [51], Blue-torrent [27].

1.1 Opportunistic NetworkingProtocols for opportunistic networks are designed to run on mobile devicessuch as mobile phones. The purpose of an opportunistic network is to delivermessages and to enable communication between islands of connectivity, i.e.,nodes or network partitions that are disconnected. There are two disseminationstrategies to deliver messages in an opportunistic network, either the single-copy case [54], or the multi-copy case [53]. The single-copy case, where onlyone message is forwarded through the network, is not resilient to node fail-ures, malicious node behavior, or dropped messages due to full buffers. In themulti-copy case several copies are forwarded which makes the network moreresilient and message delivery yield a shorter delay than single-copy since sev-eral paths are used in parallel. However, multiple copies create overhead, i.e.,additional transmissions for each delivered message. Not only does it meanthat nodes will communicate more often leading to quicker battery drainage,it also mean that nodes will have to buffer more messages than in a single-copy case. The most extreme case of multi-copy is Epidemic forwarding [56]that transmits a copy of a message to each node encountered, which yield theshortest message delivery delay, but also creates the most overhead. In densenetworks it can also lead to contention in the wireless medium which is trans-lated into larger delays.

There are several issues that have to be dealt with before an opportunisticnetwork can be used to disseminate messages. These issues are: (i) whatmessages should be stored;(ii) unpredictable contacts, i.e., what node shoulda message be forwarded to;(iii) replication that leads to congestion.

All nodes act as end-points (message creator and receiver) as well as routersin the network. This means that each node has to be able to store messages,

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forward messages and handle buffer management. Opportunistic networksare characterized by prolonged disconnections, partitioning, unpredictable andunstable topologies. Partitioning and the unpredictable nature of the net-work comes from the movement and the sparseness of the nodes. The un-predictable nature of the movement leads to unpredictable forwarding oppor-tunities, which is solved by storing messages on nodes.

Nodes that participate in opportunistic networks often have limited resources,in particular when it comes to battery power and storage. Therefore, overhead,in the form of unnecessary replication of messages, should be avoided. Flood-ing messages in the network, i.e., sending a message to each encountered nodethat does not already carry the message, may lead to overloaded storage. If thatis the case, other messages might not get the chance to be transferred or theymay be dropped from the storage due to congestion. Therefore, opportunisticnetwork protocols should not generate unnecessary overhead and overloadingnodes with messages should be avoided.

The unstructured nature of opportunistic networks and the fact that com-munication opportunities, or contacts, are unpredictable, both when it comesto when they appear and how long they are, makes it difficult to decide howthe opportunity should be managed. In networks with high diversity in nodecontact pairs lead to many available paths. It also means that messages arereplicated to many nodes if multi-copy protocols are used. If the network hasa low diversity in node contact pairs fewer paths are available and paths be-comes longer.

Buffering messages in a network with unpredictable forwarding opportu-nities may lead to unpredictable buffering times until a suitable node is metand messages can be forwarded. If the buffering time is too long, bufferswill fill up and either no more messages can be received nor forwarded ormessages have to be dropped. Buffer management and forwarding decisionsshould take the properties of the network into account. A network with highdiversity in contacts will increase the likelihood of delivering a message butif the replication budget of the forwarding protocol is high the network is atrisk of being overloaded with messages. Therefore, a limited replication bud-get should be considered for a network with high diversity. If the network isstructured and with recurring contact-pairs, the properties of the network canbe used to choose relaying nodes, a.k.a forwarders, e.g., based on contact his-tory. However, if the network is sparse with high diversity of contact-pairs,Epidemic forwarding has a high probability in dissemination with the lowestdelay but at a cost of many replicas, i.e., message overhead which can lead tocongestion. When buffers become congested and when there is no space foradditional messages, the opportunistic network protocol must decide on whatmessage to evict in order to take on additional messages.

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1.1.1 Data-Centric Opportunistic NetworksAs mentioned, in multi-copy opportunistic networks, messages are dissemi-nated through replication. This means that many nodes will carry copies ofthe same messages that may be only destined to one node. If data-centric net-work architectures are applied to opportunistic networks, the network can takeadvantage of the fact that several copies are available. Data-centric networksuse one-to-many communication, when several nodes may be requesting thesame data. Data requests are based on the content of the data item. There aretwo common ways to acquire data: by requests or subscriptions [23, 46]. Ifdata is requested, the first node that receives a data request and has the data,responds by sending the requested data back. When subscriptions are used,nodes keep track of what other nodes are subscribing to and forward matchingdata to them. A subscription can hence be seen as an ongoing request.

Haggle [46] is a data-centric architecture that can be used for opportunisticnetworks. It has been used in this thesis to evaluate data-centric opportunisticnetworks. In Haggle, data is disseminated based on subscriptions. The twomain subscription entities in Haggle are attributes and interests. Attributesare metadata that describes the content of the data item. To subscribe to data,nodes express their interest in attributes. The interests of a node is dissemi-nated in the network in such a way that other nodes can forward data to theinterested node. Several nodes can subscribe to the same data, and therebymake use of several of the replicated messages.

Coupled with the interest is a weight that declares the level of interest ina certain attribute. This means that two nodes can have different levels ofinterest in the same attribute and therefore also different levels of interest inthe same data item. If a data item has several attributes, a combined level ofinterest is calculated. The level of interest can be used to rank and prioritizedata items.

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2. Research Challenges and Contributions

This thesis is concerned with congestion, transfer ordering, and resilience inopportunistic networks. More specifically, we are concerned about what datashould be transferred between two nodes and in what order, and at conges-tion, what should be evicted when too much data are in storage. The thirdand last concern is how to evaluate network protocol resilience when the net-work is attacked by unintentional or deliberate disruption actions. We use acareful method of emulation and simulation in our evaluations of methods andalgorithms for congestion, transfer ordering and resilience.

2.1 Congestion in Opportunistic NetworksGenerally, nodes in opportunistic networks have limited resources, but arestill willing to altruistically forward messages for other nodes in the network.When forwarding is done through replication, there is a risk for congestion atthe nodes. In opportunistic networks, no end-to-end connection is establishedand therefore congestion cannot be detected and controlled by a feedback loop.The challenge is to avoid congestion without the feedback loop, using just lo-cal information on nodes. Avoiding congestion can be done by pre-emptiveeviction of data items from the buffers of the nodes. We study how conges-tion can be avoided by using metadata attached to data items or by replicationstatistics. Well balanced congestion algorithms have the potential to increasedelivery ratio as well as decrease average delay. With an uncontrolled evictionpolicy there is a potential risk that all replicated copies of the data may beevicted before all destinations have been reached, hence decreasing the deliv-ery ratio.

ContributionWe were the first to propose how to handle congestion in data-centric oppor-tunistic networks. Since no feedback loop is available, our strategies use localinformation at a node to ease congestion.

2.2 Transfer OrderingContacts in opportunistic networks do not only begin unpredictably, but arealso of an unknown duration due to mobility. Since contacts are both short

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and sparse it is important to select what data to be transferred and transfer thedata in an order such that the most important is transferred first. The questionwe ask ourself in this thesis – is it possible to achieve global disseminationobjectives by only considering transfer ordering?

We focus on data-centric opportunistic networks. Here the user interests inthe data is associated with a weight. The higher the weight the more importantis the data. During a contact the node may decide to deliver the most importantdata first or employ another strategy such as random or the least replicateddata, or the most recently received data. The challenge is not only to selectthe exchange strategy but also to find an evaluation criteria that measures theoverall application objectives.

ContributionIn a data-centric opportunistic network context, we show how ordering of databefore pairwise communication can yield different global dissemination ob-jectives. Objectives like delivering the most interesting data items with a lowdelay or eventually deliver all data items of interest to all nodes in the networkare compared. In Paper III, we promote a metric, nDGC [25], from infor-mation retrieval to assess how well the proposed strategies perform and whatproperties they provide.

2.3 Evaluating Resilience in Opportunistic NetworksCommunication in opportunistic networks can be disrupted by attacks, mali-cious or not, that will lower the performance and the usability of the network.Attacks often take the form of deliberately dropping messages, radio jamminga physical area, but the performance could also be degraded by incompati-bilities between nodes. All these attack scenarios lead to loss of forward-ing opportunities. Opportunistic networks have two inherent mechanisms thatmitigate such attacks. The first is multi-copying messages, one sole attackercannot, with certainty, remove all copies in the network. Second, there is alarge diversity in paths and they are unpredictable which make it harder for anattacker to find them.

When evaluating attacks, using average values on delay and level of dissem-ination do not give a complete picture of how well a protocol can cope with anattack. The alternative, to generate all attack scenarios is also not feasible foran evaluation since we have to perform

N

∑n=0

N!(N−n)!(n!)

(2.1)

experiments, where N is the size of the network and n is the number ofaffected nodes. Note also that the mobility pattern will also have an impact on

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Random caseWorst case Best caseProb

abilit

y of

oc

curre

nces

Metric

Figure 2.1. Illustration of possible results for a studied metric. Worst- and best-caseindicate the area where we aim to yield results using approximation through heuristics.

how wide spread an attack is. Our approach to evaluate is to find heuristicsthat can approximate the extreme cases for an attack. Figure 2.1 illustrateswhere in the distribution of possible results that we aim to capture with ourheuristics.

ContributionFor the purpose of evaluating an opportunistic network under attack, we pro-pose heuristics that approximate a worst- and a best-case under the assumptionthat forwarding opportunities affect the performance of the network. By ap-plying these heuristics for all permutations of the number of attacked nodes,we yield an upper and a lower bound with respect to the studied performancemetric.

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3. Forwarding Protocols

In [24] it is shown that traditional ad hoc routing cannot cope with the char-acteristics of opportunistic networks, such as long delays and connection loss.Ad hoc networks require an end-to-end path between the source and the desti-nation. In opportunistic networks, links between nodes are not persistent andthe connectivity graph is dynamic, i.e., nodes change neighbors and the net-work may become partitioned. Therefore, traditional ad hoc routing protocolshave to be adapted to the dynamic evolution of the connectivity in opportunis-tic networks.

Opportunistic forwarding protocols are used to find dynamic paths in thenetwork. The most simplified protocols replicate messages to every node thatit meets. The more advanced protocols make decisions as to which nodesthat a message should be forwarded to increase the probability of delivering amessage. Decisions made by forwarding protocols are generally based on thefinal destination of the message. Normally, neither prioritization nor transferordering of messages is done by the forwarding protocol. In this section, wewill look at two major classes of forwarding protocols — one oblivious toacquired knowledge of contacts and one class of heuristics that use knowledge— and discuss some of their properties.

Most forwarding protocols are replication based, some can be used to for-ward a single copy. A reliable single copy case, the so-called custody transfers,was introduced by Fall et al. [16]. In custody transfers, each hop in the net-work takes custody of a message promising the previous custody node thatthe message will not be dropped and eventually forwarded closer or directlyto the final destination. The protocol guarantees that the message will not belost. However, the source node cannot know if a message has been receivedby the final destination or not. Spyropoulos et al. [54] propose a theoreticalframework to analyze the performance of single copy protocols. They derivethe upper and the lower bounds on the expected delay. Single copy protocolswork well when nodes are dependable and not prone to failure, since nodesguaranty custody until it can be forwarded.

Networks such as opportunistic networks are prone to churn, i.e., nodesare coming and going in the network, depending on battery exhaustion, fullbuffers, wireless interfaces turned on-off, etc. Under heavy churn, multi-copy protocols are a better match to achieve robustness. In this thesis weconsider multi-copy forwarding since it suits opportunistic networking betterthan single-copy.

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3.1 Oblivious ForwardingIn oblivious forwarding, messages are copied and forwarded to any node thatcomes into contact. Epidemic forwarding [56] simply floods the network toincrease the probability of message delivery. Nodes replicate every messageto all nodes that they come into contact with under the condition that the re-ceiving node has not seen the message before. By flooding the network, theprotocol ensures that the delivery delay is the shortest possible as well as thedelivery ratio is the maximum within at any given time. However, this con-sumes a lot of resources, in principle unlimited bandwidth between node pairsand infinite buffer space at nodes.

Several oblivious protocols have been proposed [18, 52, 15] that limit thereplication rate to decrease the load on the network that Epidemic forwardinggenerates. For example, Two-hop forwarding [18] limits the path length totwo hops, but does not limit the number of copies. This means that the re-laying node is only allowed to forward the message to the destination. Sprayand wait [52] uses the same basic principal as Two-hop but limits the num-ber of copies the source is allowed to forward. Binary Spray and Wait uses alimited number of copies L. At the first encounter, the node that created themessage forwards dL/2e copies of the replication budget. The receiving nodenow has n copies and will forward dn/2e at each encounter until the num-ber of copies n has reached one, then the node will only forward the messageto the destination, if it is encountered. Self Limiting Epidemic Forwarding(SLEF) [15] takes advantage of the shared medium, all nodes that are in rangeto hear a message will store the message. Nodes then forward each messagethey receive (overhear) with a given probability. The probability of forwardinga message decreases with every transmission, overheard or initiated.

Spray and Wait, SLEF and Two-hop all aim to decrease the number of mes-sages in the network compared to Epidemic. Messages are forwarded withouttaking into account congestion and how likely the neighboring node can de-liver the message to the destination.

In early work (2004), Jain et al. [24] proposed a method to assess rout-ing protocols where there is no end-to-end path. They show that obliviousforwarding protocols tend to perform poorly compared to protocols that usecollected knowledge about other nodes. Knowledge based protocols increasedelivery rate with the same number of messages compared to oblivious proto-cols.

3.2 Knowledge Based ForwardingSeveral protocols have been proposed [8, 10, 12, 13, 41] that make informedforwarding decisions in order to lower the overhead in the network. Sincecontacts are unpredictable and often short, the number of messages transferredbetween the nodes are limited and often the algorithms are based on heuristics.

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The messages that are forwarded should therefore have a higher probability ofbeing delivered to the destination than messages forwarded using uninformedprotocols.

Prophet [41] is probably the most widely used heuristic forwarding pro-tocol. It is used in this thesis to represent heuristic forwarding protocols.Prophet is using contact history on pairwise node encounters to predict deliv-ery probability to a node. The history is aggregated into a delivery probabilityvalue. This value is transitive, meaning that the encountered node’s routingtable is used to calculate delivery probability to nodes that are never met. Thevalue ages over time to reduce the risk of keeping old forwarding information.Heuristic forwarding is used to limit message replication and buffer utilization,compared to Epidemic forwarding, while striving to get close to the Epidemicoptimal delivery ratio.

Other heuristic forwarding schemes use other metrics to make forwardingdecisions. MV routing [12] uses the frequency of previous meetings betweenpeers and their visits to geographical locations. MaxProp routing [10] does notonly base forwarding on contact history, but also uses additional mechanisms,including acknowledgments, priority to new messages based on hop count, andlists of intermediate nodes. Acknowledgements are flooded in the network inorder to remove the corresponding messages from nodes. Lists of intermediatenodes are used to avoid forwarding the same message to a node twice, in thecase of forwarding through a relaying node.

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4. Node Congestion

Congestion in the Internet occurs when routers in the network get overwhelmedwith packets, leading to long queues and eventually dropped packets. Accord-ing to Floyd and Fall [17], the Internet can reach different forms of congestedcollapse.• Classical congestion collapse occurs in closed loop systems that rely on

acknowledgements. When no acknowledgements have been receivedafter a certain time, packets that could still be in transit are retransmittedwhich adds more packets to an already congested network.• Congestion collapse is aggregated by undelivered packets that wastes

bandwidth on packets that are dropped before they are delivered.• Collapse through fragmentation is a result from a fragmented piece of a

packet being dropped that belongs to a larger packet. This will result inthe receiver not being able to reassemble the complete packet.• Congestion collapse from increased control traffic.• Congestion collapse from stale and unwanted packets caused by large

delay between the end-points.Classical congestion collapse and collapse by control traffic are unlikely in

opportunistic networks since end-to-end acknowledgements or control mes-sages are not feasible due to unpredictable delays. However, congestion col-lapse by undelivered messages manifests itself in the following way: in oppor-tunistic networks, messages can be evicted before a forwarding opportunityoccurs, thereby wasting bandwidth. Fragmentation occurs when there is notenough time to send the whole bundle to the new node and only a fragment issuccessfully transferred. The fragmented message will then stay in the bufferuntil all pieces of the fragmented message have been received. Fragmenta-tion is a technique used in PodNet [37], BlueTorrent [27], and bundles [47].Congestion collapse caused by stale messages is a likely scenario in an oppor-tunistic network since a node cannot know when messages have been deliveredand when the copy at the node can be deleted. Replication collapse is uniqueto multi-copy, store-carry-and-forward networks, and does not manifest itselfin the Internet since only one copy of a message should exist in the network atany time.

Congestion in an opportunistic network occurs when a node’s buffer is over-whelmed with messages. The node then has to evict messages that may nothave been forwarded/replicated to another node. Keeping the number of itemsin the buffer small in order to reduce the waiting time is not as important inopportunistic networks as it is in the Internet. The queue is changed with each

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encounter based on the destination and the messages in the buffer. The timebetween encounters dominates over the waiting time when there are severalmessages to be sent. Therefore, having a low buffer utilization is not impor-tant. What should be strived for instead is few evictions.

In opportunistic networks, nodes cannot rely on acknowledgements since acontemporaneous end-to-end connection may never exist. The nodes that cre-ate messages have no way of knowing that a node in the network is congested.The congested node itself has to mitigate the problem, basing its decision onwhat to drop from the buffer using local information available to the node.Information could be collected from other nodes in the network.

Most existing systems make sure that a whole message is transferred at eachhop (e.g., DTN, Haggle), thereby avoiding congestion collapse from fragmen-tation. Since contacts are unpredictable in length, control traffic for maintain-ing knowledge about other nodes should be kept small in comparison withmessage traffic. To avoid stale messages and messages that are never for-warded, time out on messages has been introduced in [22, 36].

Several proposals have been developed on how to deal with congestion inopportunistic networks [19, 21, 31, 35, 48]. The solutions range from bufferadvertisement beacons aimed at neighboring nodes to algorithms to offloaddata. In the single-copy case when custody of messages is used, custody initself could make congestion worse. If a node’s buffer is full and cannot takeany more messages in custody it is likely that nodes upstream will accumulatemessages while waiting for space downstream. This type of congestion is lesssever when there is mobility and messages may be able to take an alternativepath.

When communication is cheap and the network is dense, the primary costof flooding the network with messages is in the storage of messages at inter-mediate nodes.

Upon congestion there must be a mechanism to select and eliminate mes-sages. One way is to remove messages after a period of time, another is touse acknowledgments. In the following sections we will discuss these twostrategies to identify messages that are of no use anymore.

4.1 Buffer Evictions Using AcknowledgmentsThe use of acknowledgements in opportunistic networks is different comparedto legacy networks. In the Internet, TCP acknowledgments are used to indi-cate that a message has arrived at the destination and hence that the messagedoes not have to be retransmitted. In opportunistic networks end-to-end ac-knowledgments are only used when communication is one-to-one. Just likein the Internet, acknowledgments can be used to confirm that a message hasarrived correctly at the destination. Receiving an acknowledgment triggers theeviction of the corresponding message from the nodes’ buffers. One key point

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with using acknowledgments is that they have to have a time-to-live that islonger than the message it is acknowledging. If not, a copy of the messagecan still live on in the network. Harras et al. [21] introduce active and pas-sive acknowledgements to achieve reliability in mobile DTNs. In the processof disseminating an acknowledgement, either active or passive, back to thesource node, the message is evicted from intermediate buffers. With the pas-sive approach, only nodes that have the message in question will receive theacknowledgment and remove the message, i.e., be cured. This means that themessage may still be forwarded to other nodes until all nodes have received themessage and eventually the corresponding acknowledgment. A problem withpassive acknowledgements is that messages will stay in buffers unless nodestry to forward an acknowledged message to a cured node. To eliminate thisproblem, time-to-live can be associated with messages. If active acknowledge-ments are used all nodes that come in contact with a cured node will receivean acknowledgment and if the node is carrying the message remove it.

An advantage with acknowledgements is that the nodes are sure that themessage has been received by the destination before the message is removedfrom the buffer. A disadvantage is that it takes time for an acknowledgementto disseminate in the network. Additionally, the acknowledgements also needto have a time-to-live to avoid them from lingering in the network. The use ofacknowledgements has been proposed in [10, 55]. Thompson et al. [55] showthat in their scenario the use of acknowledgments only has a small impact ondelivery ratio and delay.

4.2 Buffer Size AdvertisementsTo avoid congestion collapse by replication nodes can share their buffer uti-lization statistics with neighboring nodes. With those statistics a node canestimate the level of congestion at neighboring nodes as well as the level ofcongestion in the network given the delay in the propagation of statistics. Byadvertising the free buffers, as proposed by [35, 38], the neighboring nodescan make decisions on what to transfer and how much to transfer. By adver-tising the available buffer size the neighboring nodes can avoid overloadingthe node and prioritize messages in order to use the buffer space as efficient aspossible. Ott et al. [35] use buffer size advertisements that are transmitted asbroadcasting beacons.

Thompson et al. [55] propose to both measure and share the drop rate andreplication rate of messages. By keeping track of how often messages arereplicated and dropped, the congestion level of the network can be estimated.Depending on the congestion estimate the replication limit is adjusted to avoidfurther congestion.

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4.3 Data-Centric Node Congestion AvoidanceIn data-centric networking messages are forwarded based on the interest in thedata. Using this principle in an opportunistic network we assume that a nodeis probably more likely to offer buffer space for data items that are of interestfor the node itself. We also assume that forwarding nodes keep data that theyare interested in, which leads to interested forwarding nodes becoming newsources.

As described earlier, nodes in the data-centric Haggle architecture [46] sub-scribe to data by sharing their interests. In Paper II, we investigate if interestsannounced by nodes could be used for eviction decision, e.g., data that has lit-tle collected interest are evicted first. We compare this eviction strategy withstrategies based on replication information. Interest based decisions take intoaccount how many nodes are interested in an item and possibly the level ofinterest. Nodes can then choose to evict data that is of little interest for thenodes in the network since few nodes will request that data. Nodes can alsochoose to evict data that is of high interest based on the assumption that othernodes probably already have the data, since it will frequently be requested andshared. Decisions can also be based on replication statistics, i.e., nodes keeptrack of how many times each data item has been forwarded. Replication-based eviction strategies instead look at how many times an item has beenlocally replicated, i.e., been forwarded by a node. Replication-based evictiondrops items that have been replicated the most times. That makes it likelythat a data item exists on another node. Because, if all nodes drop the mostreplicated data, a data item will most likely not be removed from a forwardingnode until a copy of the data item has been forwarded, thereby ensuring thatthe item lives on in the network.

All strategies suffer from unnecessarily storing data items that will neverbe forwarded or have already reached all nodes interested in the data. Eitherthey are data items that no node is interested in, or data items that have beenreplicated to all interested nodes but are still in the buffer because the dataitems are of high level of interest or have been replicated many times. Thesedata items become stale and take up space in the buffer that could have beenused to forward other data items.

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5. Transfer Ordering

Data is often transferred on first-in-first-out basis when two nodes establishcontact. That means, the data that should be delivered to, or forwarded througha node is ordered according to when it was received. This is not the most effi-cient method to disseminate data with respect to achieving high delivery ratioand short delay or any other dissemination objective. To achieve higher effi-ciency, the order could be based on properties, e.g., data size, priority, repli-cation status, overhead efficiency, or any other ordering scheme. Dependingon the information available for a node, data could be ordered differently bydifferent nodes.

Lindgren et al. [40] use delivery probabilities to order data before it is trans-ferred to the next hop. They show that ordering data according to how muchmore likely the neighboring node can deliver messages, not only increase thedelivery ratio, but also decrease the overhead per delivered message.

Burgess et al. [10] propose a protocol that transmits messages to encoun-tered nodes in the order of their probability for delivery, which is based onhistorical contact information. If the connection lasts long enough, all mes-sages will be transmitted, thus turning, into standard Epidemic forwarding.As a measure to avoid delivering the same message twice, new messages, i.e.,messages with a low hop count, are assigned higher priority to give them achance to propagate in the competition with messages that have a high likeli-hood of being delivered and therefore have traversed far in the network.

RAPID [8] was developed in order to be able to prioritize messages toachieve a certain property in the network. Nodes can be configured to or-der data to achieve properties like; a minimal maximum or average delay, orto deliver as many messages as possible before the time-to-live runs out. Inorder to make the decision on what to transfer and in what order, the nodesexchange metadata about messages. The metadata contains information aboutwhich nodes have a copy of a message and an estimated direct delivery timefor each node.

SSAR [38] uses properties: priority, willingness to forward, and availablebuffer space to choose what data to transfer and how to order the transfer. Theordering is adopted from RAPID [8]. When nodes share what they have intheir buffers, the probability to deliver messages and available buffer space isalso shared. This information is taken into account when messages are selectedand ordered before transfer. Data is deleted from the transmitting node oncethe data have been received by the neighboring node. In [38] the authorsshow that delivery ratio increases compared to SimBet [14] and Prophet [41]

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(including a Prophet version that takes social ties into consideration). Howmuch of the improvement that comes from the ordering of data is not describedin the evaluation.

In Paper III, we have studied how transfer ordering affects data delivery.Instead of only focusing on the standard metrics of delivery ratio and delay,we also look at a metric how well the level of interest can be met over allnodes. As an example, in the data-centric network architecture Haggle itemsare tagged with attributes. Nodes can set different levels of interests withrespect to these attributes. Depending on the attributes, two separate data itemsdo not have to have the same value for a node. This means that a data itemin one node can be ranked for the other node, according to the other node’slevel of interest. To measure the performance we used nDCG [25], a metricfrom information retrieval. With nDCG, all the data items in the network areranked according to the node’s interests and are then compared to received dataitems. How fast this metric increases over time gives an indication on how wellthe most interesting data items are delivered to the nodes. We discovered inPaper III that satisfying local interest, i.e., the interest of the node in contact,gives a high metric of satisfaction for highly interesting data items. However,ordering by local interest also creates interest segregation that may lead tosome data items not being disseminated to all interested nodes. If only localinterests are satisfied, all nodes on the path from a source to an interested nodemust have an interest in the data item otherwise it will not propagate over therelaying nodes. Our work in Paper III was the first on data-centric ordering ofdata transfers in opportunistic networks. It reveals basic understanding of howlocal and global ordering strategies affects the dissemination of data.

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6. Resilience in Opportunistic Networks

Since messages are disseminated using several copies, i.e., message replica-tion, opportunistic networks are resilient against message loss by default [11].An opportunistic network also has to be resilient against hardware and soft-ware failures, selfish node behavior such as free-riding, and jammers that dis-turb communication in a certain area. These attacks will all lead to loss offorwarding possibilities or direct message loss, which the opportunistic net-work protocols are designed to handle more or less well. Indeed, forwardingprotocols are affected differently. Properties of the network dictate how re-silient a protocol is to attacks. For example, node movement creates contactsbetween different nodes and results in a diversity of paths which increasesthe resilience. Message replication introduces redundancy to the system andhence resilience against jamming a particular node.

Efforts have been put into studying selfish node behavior, both individ-ual selfishness [28, 29, 32] and social selfishness [38, 39] with respect to re-silience. Malicious selfish behavior includes to drop all messages that are ofno interest for the node or to not forward messages at all as well as to propa-gate misleading information of the node’s capability. These studies show howselfish behavior affects different opportunistic scenarios, including mobilitypatterns. What we consider in Paper IV is a range of attacks from worst tobest cases where a metric envelope is used for comparison how well a protocolcan cope with an attack under different network properties. An approximationof best- and worst-case is heuristically derived. As shown in Paper IV, themetric envelope gives a better scope of how predictable a protocol behaveswhen it is under attack. We also observe that heuristically choosing best- andworst-case for experiments where diverse contact traces are used, for examplerandom waypoint, is non-trivial. The envelope of the studied metric becomesnarrow and the random scenario is close to or sometimes overlaps with one ofthe extreme cases.

During the work with Paper IV, we performed experiments with limitedbuffer space. Shown in Figure 6.1 are measurements that show that individualselfishness leads to decreased node congestion since selfish nodes are not will-ing to replicate all messages. But it also leads to an increased delivery delaysince forwarding opportunities are lost. Li et al. [39] analytically show thatDTNs are robust to social selfishness. They observe that selfishness increasesmessage delay but reduces delivery cost since selfish nodes are assumed torefuse to replicate and forward messages from nodes in other social groups inthe network.

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Figure 6.1. In a scenario with the Shortest Path Map Based Mobility model [33] andEpidemic forwarding, we observe that the delivery ratio increases initially with thenumber of selfish nodes. At the same time the average delay increases. When thenumber of selfish nodes start to reach the total number of nodes in the network (126),the delivery ratio decreases while the delay continues to increase. The increase indelivery ratio is more prominent for small buffer sizes. The number in the legendrepresents the buffer size in number of messages.

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7. Research Methodology

The research method used in this thesis is mainly based on experimental re-search where a testbed and a simulator have been developed to test hypothe-ses. Part of the results in the thesis are based on experiments performed on atestbed built for the purpose of developing and testing a new network archi-tecture, Haggle. Other results were gained by using an existing simulator anddeveloping custom simulators. To be able to scale experiments we turned tosimulations. Different simulators were used depending on which architectureand property that was explored.

The variety of methods and tools used in this thesis is discussed in thefollowing sections.

7.1 Experimental TestbedsDieselNet [10], represents the type of testbeds that come closest to deploy-ments of delay tolerant and opportunistic networks. On the other hand, theDieselNet uses a specific scenario with 40 scheduled buses that have fixedroutes where only some aspects of opportunistic networks can be evaluated.The drawback of real world testbeds is that the scenario is often limited toone type of contact patterns. Hence the realism comes at the cost of gener-alization to other scenarios. Real world field experiments are also expensivewhen it comes to the people and devices needed to run them. Performingexperiments on opportunistic networks is not feasible using real equipmentand real users, if repeatability is a goal which is the case when two protocolsshould be compared. The large number of users and the long experiment du-ration makes it also impractical. Real world testbeds are best suited to testprotocols/architectures before deployment. Complex real world experimentsare not feasible in opportunistic networking when scenarios take place over alarge area and long time, in the range of hours to several days.

Emulators present a trade-off between real testbeds and simulators. Emula-tors can be connected to anything from simulated networks to virtual wirelessnetworks [49] at the lower layers. Yet, emulators allows unaltered code tobe run on the higher layers. Using emulators, contact traces collected fromdifferent real world experiments can be used to evaluate protocols. An advan-tage is that the same contact trace can be used several times to accommodaterepeatability.

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In our emulation we use virtual computers that are connected to a virtualEthernet segment. On the Ethernet segment, we filter traffic according to acontact trace, i.e., a list of pairwise contacts, that creates a change in the net-work topology that emulates movement of the nodes. The testbed is describedin Paper I. When we use virtual computers [9] running a standard operatingsystem, in this case Debian Linux [7], we can perform experiments with un-altered code, avoiding code discrepancies that can occur if code is convertedto simulators. Also, we can develop and test new functionality in the testbedbefore we deploy code on the mobile devices used in real-world experiments.

A limiting factor with our emulation over Ethernet is that the links betweenthe nodes are binary, meaning either they are running at full capacity or notat all. We neither emulate properties of a wireless channel, nor contention.Also, since we are using contact traces, it is already difficult to know if thereis contention in the ether. When it comes to data rate, it is possible to throttlethe outgoing data rate on virtual nodes, but for the ingoing traffic it is notpossible, which makes it feasible for several nodes to transfer data to a singledestination.

Another testbed based on virtual nodes is Hydra [42], a testbed developedfor delay tolerant networks. Virtual nodes are distributed on several host ma-chines controlled by a master node. The master node is controlling connec-tivity according to contact traces. VirtualBox [2] is used as a virtualizationsoftware with OpenWRT [1] running on the virtual nodes to avoid using toomuch CPU and memory. Compared to the virtualization software used in ourtestbed, VirtualBox introduces more overhead on the host machines.

7.2 SimulationsSimulators are practical when experiments must be scaled up in the numberof nodes and run-time, where real world experiments and real time emulatedtestbeds no longer make sense. A drawback with moving to a simulated en-vironment is that the code has to be re-written or altered to work with thesimulator.

The simulators used in this thesis are event-driven either by messages, con-tacts, or by both. Our contact- and message-driven simulations suffer from thelack of simulated network contention. The positions of the nodes are not in-cluded and therefore it is not possible to figure out when nodes are interferingwith each other.

7.2.1 Message-Driven SimulationsMerkourious et al. [30] has developed an "inflation and filtering" technique toevaluate opportunistic networks using contact traces and message traces. Thebenefit of this method is a gain in simulation time, which is several magnitudes

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faster. Three processing phases constitute the technique: (i) the input contact-trace is filtered on a per-message basis to single out those encounters thatrepresent possible forwarding opportunities according to the specific protocolrules; (ii) filtered traces are then converted with graph-expansion techniquesinto a graph construct with the message source node at its root; and (iii) laststandard shortest-path algorithms are used to determine performance metrics,such as message delivery probability, delay, and hop count. A drawback ofthe technique, revealed during the work with Paper IV, is that it is hard tosimulate nodes with limited buffer space. Messages are simulated one-by-onewhich means that the nodes do not have a state. Therefore, it is not possibleto know how many messages a node is carrying at a certain point in time.However, state-full forwarding protocols can be used if pre-processing of thestates for the forwarding protocol is done. This simulation method was usedin Paper IV.

The technique gives us an efficient approximation to evaluate the perfor-mance of forwarding protocols, based on contact traces. Since no contentionand bandwidth as well as message size are simulated we have chosen to imple-ment the functionality in another, simple stateful simulator, described below.

7.2.2 Contact-Driven SimulationsIn contact-driven simulators, in contrast to the message-driven simulator, nodesdo have states. Nodes are simulated with states for forwarding protocols,buffered messages, interests (if data-centric nodes are simulated), and othermiscellaneous settings. The state approach enables simulations of limitedbuffer space, transfer ordering, and node congestion.

A contact trace drives the simulation. Each entry in the contact trace is con-sidered as an opportunity to exchange messages. The contact trace is executedaccording to the order of the time at which a contact is discovered. Nodesexchange messages until the contact ends or no more messages are availablefor exchange. If no more messages are available for forwarding and contacttime still remains, new contact is created that is re-inserted in the contact trace.This will enable a message to be forwarded through a node that is in contactwith two or several nodes at the same time. The drawback with this method,as with any contact trace based simulation, is that network contention cannotbe simulated.

Overlapping contacts, i.e., a node with two or more contacts at the sametime, are serialized. If nodes are transferring messages simultaneously, a mes-sage will first be transferred from one node to the other, depending on the starttime of the contact entry. This could, depending on the eviction strategy, leadto that a message that should have been relayed on arrival instead is evictedfrom the buffer before it can be forwarded.

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7.2.3 Event-Driven SimulationsIn event-driven simulators, all events that are generated by nodes or messagesare, together with mobility events and based on the time the event should beexecuted fed into a queue. The queue is then executed by the simulation en-gine. The granularity can be high at the cost of slow simulations. Two pub-licly available event simulators are DTNsim2 [5] and the ONE simulator [33],where the latter is the most widely used simulator in the opportunistic networkresearch community. An advantage with the ONE simulator is that the code ispublicly available and that several forwarding protocols and mobility modelshave been implemented.

The ONE simulator was used in the simulations described in Paper IV. Inthese simulations we use the coordinates of nodes to capture mobility models.Node coordinates are commonly not available in contact traces. The coordi-nates made it possible for us to place jammers at certain locations to disruptcommunication in a specific area. Another advantage with mobility models isthat it is possible to simulate network contention. This is an important featureif the network is dense and the traffic load is high.

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8. Summary of Papers

This thesis consists of the following papers:

8.1 Paper IHaggle Testbed: a Testbed for Opportunistic NetworksFredrik Bjurefors, Per Gunningberg, and Christina RohnerIn Proceedings of the 7th Swedish National Computer Networking Workshop,Linköping, June 2011

SummaryWe describe the structure of the Haggle testbed and evaluate the timing ac-curacy and scaling properties. The testbed was implemented using virtualmachines. The host computer acts as the controller of experiments. We evalu-ated timing accuracy at three different levels: interface (Ethernet), Haggle andapplication. The evaluation shows that the timing accuracy of the testbed iswithin a few milliseconds, which is reasonable when the probing interval forneighbor discovery in Haggle is five seconds. We also looked at how well thetestbed scales with the number of nodes. The limiting factor was shown to bedisk access. During our evaluation we switched from a standard SATA diskto using a SSD disk. After the switch we were able to run more than twice asmany nodes, due to the increased read and write speed of the disk.

ReflectionsNetwork contention is a problem when contact traces are used, that we did notconsider. Another problem that we had was the data rate between nodes. Out-going data rate is easy to configure, but the ingoing is complicated, especiallysince the number of incoming links vary.

My ContributionI designed the structure of the testbed and implemented it using Java and Bashscripts that control the experiments. I performed the experiments to show thetiming properties and the limitations of the testbed.

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8.2 Paper IICongestion Avoidance in a Data-Centric Opportunistic NetworkFredrik Bjurefors, Per Gunningberg, Christian Rohner, and Sam TavakoliIn Proceedings of ACM SIGCOMM Workshop on Information-Centric Net-working (ICN 2011), Toronto, Canada, August 2011DOI:http://dx.doi.org/10.1145/2018584.2018594

SummaryCongestion in opportunistic networks occur when nodes’ buffers becomes full,which is commonly caused by replication of messages. In this paper, we ex-plore how to avoid congestion in data-centric opportunistic networks. Con-gestion, in this context, means that a node can no longer receive new data toforward. Data has to be dropped to free up space in the buffer to enable newdata to be received. In opportunistic networks, the lack of an end-to-end path,i.e., disconnection, leads to disruption in node communication and the lackof a feedback loop. Therefore, decisions on what to drop must be taken onlocal information. We proposed a set of basic dropping strategies that use datareplication statistics or the interests of other nodes to make a decision on whatto drop. To evaluate the proposed dropping strategies we use a set of artificialnetwork topologies where a central node connects clusters of nodes, thus lead-ing to node congestion. We show that dropping data based on data replicationstatistics performs better than using the interests of the nodes in the network.

ReflectionsWe should have used an aging time on data items to see how well that com-pares to the other dropping strategies. Also, global knowledge of what data hasbeen replicated should have been used as a best case for comparison. Addi-tionally, to solve the problem with stale data in buffers, we should have addedrandom evictions to the evaluated strategies to allow new data coming into thebuffers.

My ContributionI proposed the dropping strategies that the master thesis student, Sam Tavakoli,whom I supervised, implemented into the Haggle code [4]. I set up and per-formed the experiments as well as evaluated the dropping strategies. The re-sults were analyzed together with my supervisor, Christian Rohner.

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8.3 Paper III“Making the Most of Your Contacts: Transfer Ordering in Data-CentricOpportunistic Networks”Christian Rohner, Fredrik Bjurefors, Per Gunningberg, Liam McNamara, andErik NordströmIn Proceedings of the Third International Workshop on Mobile OpportunisticNetworking (MobiOpp 2012), Zurich, Switzerland, March 2012DOI:http://dx.doi.org/10.1145/2159576.2159589

SummaryContacts in opportunistic networks are short and unpredictable. What is actu-ally forwarded during a contact has a profound effect on the global dissemina-tion objectives. We evaluated several ordering strategies that order data uponnode contact before data is transferred. The evaluated strategies select databased on interests of the node in direct contact and order the data; last-in-first-out, random, or the level of interest (calculated using the recipients specificinterest per attribute). Another strategy uses the accumulated knowledge ofwhat nodes are interested in, and attributes describing data and order accord-ing to the level of interest. We also compare the strategies to a random orderingstrategy that is agnostic of the interests of nodes and the content of data. Weuse a quality metric (nDCG) to evaluate if we achieve the desired effect froma pairwise transfer ordering scheme. We show that satisfying the directly con-nected node’s interests has the lowest delay when it comes to delivering themost interesting data to all nodes in the network. However, this means thatdata can only disseminate if the next hop is interested in the data. Globallysatisfying interests will overcome this problem and yield a better result whenit comes to delivery of all data items of interest, but on average with a longerdelay for the most interesting data items.

ReflectionsIt would have been interesting to compare an ordering strategy with globalknowledge of the interest of all other nodes in the network with the real strate-gies that operate with local and partial global knowledge. Also, it would havebeen interesting to evaluate larger networks, which was not possible at thetime the paper was written.

My ContributionI carried out the experiments and, together with my supervisor Christian Rohner,I analyzed the data. I participated in writing the paper.

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8.4 Paper IV“Resilience and Opportunistic Forwarding: Beyond Average Value”Fredrik Bjurefors, Merkourios Karaliopoulos, Christian Rohner, Paul Smith,George Theodoropoulos, Per GunningbergSpecial Issue on Opportunistic Networks. Computer Communications, 2014

SummaryOpportunistic networks are inherently resilient to disruption and delay in com-munication. However, if the network is attacked, valuable forwarding oppor-tunities are lost. How this affects the dissemination of messages depends onthe forwarding protocol. We proposed a new method to evaluate forwardingprotocols under attack. It is not feasible to evaluate all possible permutationsof an attack to find the best and worst outcomes. Instead we used heuristicsto approximate worst- and best-case under the assumption that lost contactopportunities decrease the performance of the network. These cases give usan envelope with a lower and upper bound on the performance under the in-fluence of an attack. We use three different attack scenarios: jammer, soft-ware/hardware failure, and free-riding.

My ContributionI implemented the message driven simulator for Epidemic and Source Sprayand Wait forwarding for the jammer experiments and ran all experiments forthe named protocols. I performed all experiments for software/hardware fail-ure scenarios, as well as the experiments for free-riding based on the mobilitymodels RWP and SPMBM. I analyzed the data together with the other authorsof the paper. I am the main author of the paper.

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9. Conclusions and Future Work

In this thesis, we have studied how to handle the limited resources available inopportunistic networks. The network is limited in communication opportuni-ties and storage at participating nodes. The communication opportunities arealso unpredictable when the mobility of the nodes is not deterministic. Duringthe work on this thesis we have developed evaluation tools for understandingand measuring the performance of protocols and storage handling for oppor-tunistic networks. Paper I describes a testbed we developed based on virtualnodes. It gives us the possibility to evaluate real implementations of protocols.The other tool is a simulator, used in Paper IV, used to study large networksbased on contact traces.

Paper II, deals with buffer eviction strategies in order to handle congestion.We find that eviction decisions based on data replication statistics perform bet-ter than eviction schemes based on the interests of nodes. Evicting data thathas already been forwarded to other nodes will give new data a chance to beforwarded in the network. In a replication based system it is also likely that analready forwarded message has several copies and therefore it is a strong can-didate for eviction. However, randomly dropping data from the buffer yieldsthe second best results among all strategies. This shows that replacing data inthe buffer makes the most significant improvement.

In Paper III, we have shown that transfer ordering can impact both whichdata and how data is disseminated in the network. By ordering data beforeit is transferred over opportunistic contacts, different dissemination objectivescan be achieved. An example could be to disseminate data to all interestednodes in shortest possible time or to selectively distribute the data with thehighest interest to as many nodes as possible. We discovered that the networkruns a risk of being segregated when data dissemination is based on level ofinterest only. With this strategy, data with low interest runs the risk to stayin buffers and to possibly be evicted. That means, data cannot traverse thenetwork unless the data is of interest for all intermediate nodes.

The metric envelope approach presented in Paper IV shows that the impactof an attack manifest itself differently depending on how well informed theattacker is. The evaluation method with heuristically chosen lower and up-per bounds for an attack yields a better view of how resilient a protocol is,compared to an average value experiment performed over several runs. Themethod requires a smaller set of experiments to give the better understandingof how a protocol is affected by an attack scenario.

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Future WorkAs the amount of data in the network increases, transfer ordering will becomemore important. In general the ordering should be derived from the dissem-ination objectives as well as priorities when a contact time is limited. Forexample, in a disaster scenario, it is conceivable to assume that the infras-tructure is down and that communication is instead done over opportunisticnetworks. Then, it is necessary to prioritize in which order data is transferredsince it is not possible to know when the next contact occurs, and some data ismore important to deliver in time than other.

We speculate that transfer ordering is going to become increasingly impor-tant when the amount of generated data in sensor networks increase. Somedata may be collected over time with no hard deadline while other data in thenetwork is of interest as soon as possible. If the Internet uses a data-centricarchitecture transfer ordering may also be used to prioritize data to satisfyseveral requests for the same data item at the same time. Since most pro-posed data-centric networks replicate data, it may be possible to use similarcongestion avoidance mechanisms to the ones we propose for data-centric op-portunistic networks in the regular Internet.

The resilience against attacks in opportunistic networks will become impor-tant in new scenarios. These scenarios include military networks that collectcombat information through soldiers and vehicles in the field. It is then im-portant to understand how severely a protocol may be affected by an attack.Most important is perhaps to get an understanding of the worst case scenarioand how likely it is. This understanding may include how much data still canbe forwarded and the impact on the delay characteristics. With this informa-tion messages can be prioritized and transfer ordering can be applied to get themost critical data through the network.

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10. Summary in Swedish

Opportunistiska nätverk är uppbyggda av noder som mobiltelefoner och da-torer vilka bärs av personer eller är monterade på fordon. Nätverkets uppgift äratt sprida information eller vidarebefordra information inom områden som intehar radiotäckning. Dessa nätverk skapar egna kommunikationsmöjligheter därinfrastruktur, som kablar och antenner, saknas. Däremot kan noderna kommu-nicera med varandra över kortare avstånd, t.ex. via blåtand och WiFi. Kom-munikationen består av att noder vidarebefordrar meddelanden åt andra noder.Meddelanden kan även flyttas i nätverket genom att noder är mobila och rörsig i den fysiska miljön. Detta gör att nätverk som har blivit delade, genomatt noder har tappat kontakt med varandra, kopplas samman genom de mo-bila noderna. Detta leder till att dessa nätverk måste klara av både långa för-dröjningar och avbrott. Detta hanteras genom att noder lagrar meddelandenoch skickar dem vidare när tillfälle uppstår.

I arbetet med överlast av noder och överföringsstrategier använder vi oss avopportunistiska nätverk som adresserar data, med det menas att noder efter-frågar data från nätverket. Vi använder den data-centriska nätverksarkitek-turen Haggle som är framtagen specifikt för opportunistiska nätverk. I Hag-gle prenumererar noder på data genom att uttrycka intresse i metadata sombeskriver data. Var data kommer ifrån som matchar intressena har ingen be-tydelse. Varje meddelande är associerat med metadata som beskriver medde-landet.

Vi antar att när noder är tillräckligt nära varandra för kommunikation kopierasmeddelanden mellan noder, d.v.s., ett meddelanden tas inte bort från den sän-dande noden. När meddelanden kopieras istället för att ett original skickasvidare blir nätverket mer robust men det skapar också mer trafik i nätverketoch fler meddelanden i buffertar. Detta kan leda till att buffertar blir över-lastade med meddelanden, mer än den kan ta emot vilket innebär att medde-landen måste kastas eller vägras tillträde. D.v.s., för att noden fortfarande skallkunna ta emot meddelanden måste noden kasta bort meddelanden ur buffer-ten. Eftersom man i dessa nätverk inte kan stoppa meddelanden vid källansom i TCP-protokollet i Internet måste överlasten hanteras lokalt i noden. Viföreslår strategier för hur en nod ska välja vilka meddelanden som skall kastasbort vid överlast. Vi jämför strategier som kastar bort meddelanden baseratpå antal kopior som har gjorts på noden med strategier som tar innehållet imeddelandet i beaktande.

Kontakter, det vill säga möjligheter till kommunikation mellan två noder,kan i dessa nätverk vara relativt sällsynta när det är långt mellan noderna och

41

när de är mobila. Under dessa förutsättningar är det därför viktigt hur kontak-ter hanteras. När kontakterna är korta och kan bli avbrutna när som helst blirdet viktigt i vilken ordning meddelanden överförs. Vi föreslår strategier föratt ordna meddelanden innan de skickas för att uppnå olika spridningsmål. Vivisar på att ordningen som meddelanden skickas mellan två noder gör skillnadför hur meddelanden sprids globalt i nätverket.

För att utvärdera överlast och ordning av data har en testbed utvecklats.Testbädden bygger på virtuella datorer som kopplas samman av ett virtuellEthernet-brygga. Kommunikationen mellan de virtuella datorerna filtreras föratt emulera noders rörelse och kommunikationsmöjligheter. För att utvärderastörre nätverk använder vi simuleringar. Två simulatorer har utvecklats för än-damålet. En baseras på en metod där den kortaste möjliga vägen för ett med-delande, med avseende på en specifik forwarding protokoll, beräknas. Denandra simulatorn kan simulera både värd- och data-centriska nätverk. I denkan man simulera överlast och överföringsordning kan simuleras. Dessa sim-ulatorer har använt vid utvärderingen av en ny metod som vi utvecklat för attundersöka hur motståndskraftiga opportunistiska nätverk är mot attacker.

Opportunistiska nätverk är designade för att klara avbrutna förbindelser ochlånga meddelandefördröjningar. Detta gör att nätverken är relativt motstånd-skraftiga mot attacker jämfört med Internet. Nätverken fortsätter att fungerafast med nedsatt prestanda. En attack kan gå ut på att störa ut radiokommu-nikation inom ett område eller själviskt beteende hos deltagare i nätverket.Men kommunikations möjligheter kan också försvinna på grund av effekternaav kompatibilitets problem i hårdvara och mjukvara. För att en självisk nodinte ska bli upptäckt tar den emot alla meddelanden som överförs till den, menden skickar endast vidare meddelanden som den själv har genererat. Nodenkastar helt enkelt bort kopian den har mottagit. Alla dessa attacker leder tillförlorade kommunikationstillfällen. Beroende på hur väl attacken är genom-förd har den olika effekter på prestandan i nätverket. Det är inte möjligt atttesta alla möjliga permutationer av attacker. Vi föreslår en metod där vi ap-proximerar det bästa och värsta utfallet med hjälp av en heuristisk metod.

Vi visar att differensen mellan resultaten för bästa och värsta utfallet vari-erar mellan protokoll. Variation finns även beroende på vilken attack nätver-ket utsätts för. Denna information kan användas för att förstå hur nätverketkan påverkas av en attack och därmed vilket protokoll som är lämpligast förolika attackscenarios och acceptabel prestandaförlust. Oftast måste lasten pånätverket öka för att tolerera förlorade meddelanden, d.v.s., mängden kopiorsom skapas av varje meddelande, vägas mot en säkrare leverans.

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