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Bachelor Informatica Disruption Tolerant Network- ing in Tactical Communication Networks Arjen Roodselaar July 7, 2011 Supervisor(s): dr. I. Bethke (UvA), dr. J.J. van der Ham (UvA), ir. G. Hoekstra (Thales), dr. ir. M. de Graaf (Thales) Signed: dr. I. Bethke (UvA), dr. J.J. van der Ham (UvA), ir. G. Hoekstra (Thales), dr. ir. M. de Graaf (Thales)

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Bachelor Informatica

Disruption Tolerant Network-ing in Tactical CommunicationNetworks

Arjen Roodselaar

July 7, 2011

Supervisor(s): dr. I. Bethke (UvA), dr. J.J. van der Ham (UvA),ir. G. Hoekstra (Thales), dr. ir. M. de Graaf (Thales)

Signed: dr. I. Bethke (UvA), dr. J.J. van der Ham (UvA),ir. G. Hoekstra (Thales), dr. ir. M. de Graaf (Thales)

Informatica—

Universiteit

vanAmst

erdam

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Abstract

With the adoption of Network Centric Warfare by military organizations, performance of com-munication infrastructure is becoming ever more important. Until now this performance has beenquantified in terms of technical capabilities instead of measures reflecting the need of end-users.

This research has derived six high level characteristics which need to be incorporated into thedesign of tactical communication networks in order to successfully support the task of Commandand Control (C2). Using a Blue Force Tracking application the quality of tactical networksis quantified in terms of C2 performance. Using this quantification the impact of Delay- andDisruptive Tolerant Networking on user-level performance has been evaluated in representativemilitary context.

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Contents

1 Introduction 51.1 Delay And Disruption-Tolerant Networking . . . . . . . . . . . . . . . . . . . . . 51.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Research Question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Command and Control 92.1 Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Characteristic Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 Application 133.1 Shared Situational Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Force Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.3 Network Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Experiment 154.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2 Blue Force Tracking Application . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.3 Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.4 Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.5 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.6 Link Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.7 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Results 215.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.2 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

6 Conclusion 276.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276.2 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

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CHAPTER 1

Introduction

In the late 1990s the United States Department of Defense pioneered a new theory of warcalled Network-Centric Warfare (NCW). With the adoption of this theory military organizationsare seeking to translate information advantage into competitive advantage. By networking theirforces allowing sharing of information these organizations aim for different forms of organizationalbehavior, leading to an increased force effectiveness [1, p.7].

Motivated by a need for interoperability and compatibility the Internet Protocol (IP) is con-sidered by many to be the technology of choice driving the communication networks facilitatingthis new doctrine. Unfortunately several assumptions regarding connectivity, delay and loss havebeen made in order for IP to merge multiple heterogeneous networks into one single network.The tactical environment operated by war fighters is challenging these assumptions because itis characterized by low bandwidth wireless links and frequent loss of connectivity due to terrainmasking, unpredictable node movement and adversaries disrupting communication. In order toprovide the reliable communication required to build a true networked military force, solutionsto these limitations must be found.

1.1 Delay And Disruption-Tolerant Networking

Delay-Tolerant Networking (DTN) started out as an attempt to expand the internet into spaceand solve technical difficulties such as delay and packet corruption inherent to deep space commu-nication. Inspired by the success of communication standardization illustrated by the TCP/IPprotocols of the Internet, the space communications research community started working on ageneralized, layered structure of protocols. However, long delay and packet corruption is quicklycausing network disruptions in TCP/IP protocols and better alternatives are needed in orderto successfully communicate in space. This led to the proposal of the Interplanetary Internet(IPN), a system solving these difficulties by persistently storing information in the network [5].

Recognizing the result of this research is useful in different context, techniques proposed forthe IPN were adapted as an architecture for challenged networks [6]. With the ideas underpinningthis architecture applied to a variety of networks, the DTN acronym gained widespread use andis nowadays used to describe the broad research topic dealing with network disruption as a resultof technical limitations and uncontrollable external factors. In this thesis the acronym DTNis used to indicate delay and disruption tolerant networking, that is networking in presence ofunavailable links, errors and failing network connectivity.

1.2 Related Work

In the past decade considerable research effort has been focused on routing data through net-works experiencing a varying degree of link availability. Solutions have been proposed for net-works dealing with scheduled- [8], predictable- [10] and unpredictable link availability [14]. Agood overview of these early DTN routing algorithms is found in [9], where the authors classify

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protocols using the properties of data replication and routing knowledge. The authors arguemost protocols achieve packet delivery by either using knowledge about the network to optimizeforwarding, or by flooding the network with multiple copies hoping one of them will reach theintended destination. Each of these approaches has a different impact on resource utilizationand performance.

Even though most research work recognizes the usefulness of DTN in military context, to thebest of the authors knowledge little has been published on research in a tactical environment.An attempt to provide delay and disruption tolerance on a global scale is made by Krishnanet al. through the Survivable Policy-Influenced Networking (SPINDLE) system [11]. Developedunder the DARPA DTN program SPINDLE is a modular DTN framework build upon DTN2,the reference implementation of the DARPA DTN Research Group. The framework allows formodular implementation of routing protocols, a name-management architecture and contentaccess facilities such as distributed indexing, caching and retrieval methods. Along with thesystem the Anxiety-Prone Link State routing protocol is proposed, which looks capable of routingdata in sparsely populated environments by combining epidemic like spreading with path routing.

At the other end of the spectrum Holliday proposed an extension to the Optimized Link StateRouting (OLSR) protocol, one of the internet standard protocols used for ad-hoc mesh networks,called Native OLSR for Mobile Ad hoc and Disrupted networks (NOMAD) [7]. By temporarilystoring generic IP packets upon loss of connectivity, the extension is allowing for some disruptionin an ad hoc network. Simulator experiments using a realistic military movement scenario showan increase of 10% in data packets delivered using this extension [7]. However, due to delayrestrictions in the transport protocols above the networking layer this extension does not (yet)provide a solution for long term delay or partitioning of the network.

A promising routing protocol operating in a similar environment is LAROD, a geographicrouting protocol proposed by Kuiper [12]. This protocol uses positioning information to routedata and combined with DTN techniques allows for node movement to actually improve linkavailability. Due to tight integration of positioning data this protocol might provide interestingpossibilities in the area of multicasting and geocasting.

1.3 Research Question

With the exception of NOMAD and LAROD most of these protocols have only been evaluatedusing the random waypoint mobility model or small street plans. This raises the question whetherthese these models are applicable in the context of tactical operations. A key question that is leftunanswered is about the impact of DTN on the end-user experience. Until now most protocolshave only been evaluated on technical performance, measured in delivery rates and routing delay.These numbers, which give some indication on how different protocols perform on a technicallevel, do not reveal their impact on performance experienced by the end-user. Because militarycommunication networks are of such importance in the future of military organization this aspectmust not be overlooked. The main question of this research is therefore:

How can the performance of a tactical military network be improved by applyingdelay and disruption tolerant networking?

Answering this question requires understanding about what performance of communicationactually means in the context of military operations. Furthermore, appropriate metrics should bechosen to evaluate the performance of networks facilitating this communication. The followingquestions should be considered in order for the research question to be answered:

1. What are the requirements on communication in the context of tactical military operations?

2. How should the performance of the networks facilitating this communication be evaluated?

Answering the research questions will yield the following result supporting further researchon delay and disruption tolerance in the context of military communication:

1. A list of high level communication requirements in the context of tactical military opera-tions

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2. Metrics used to evaluate the performance of this communication reflecting these require-ments

3. A comparison of DTN to alternative networking approaches

4. Performance implications of DTN on end-user applications

In order to answer these questions three routing simulations models and a new group waypointmodel have been build. Using these models various simulations have been performed.

1.4 Overview

The remainder of this thesis is structured as follows: Chapters 2 and 3 answer the questions abouthigh level communication requirements and performance evaluation by studying publications onmilitary theory. The relation between two of these characteristics and network performance isthen studied using an experiment described in Chapter 4. In support of this experiment theGroup Random Waypoint mobility model is proposed, a model reflecting movement in militaryoperations. In Chapter 5 the results of this experiment are analyzed and interpreted. Chapter 6is written in conclusion to this research, summarizing results and recommending future research.

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CHAPTER 2

Command and Control

The process of managing military operations, Command and Control (C2), has received in-creasingly more attention in recent years as an academic research topic. Claiming that relevantthreats, operating environments, available technology and our understanding of human enter-prises are changing Alberts et al. argue research on new approaches of C2 is necessary in orderto successfully deal with changes [4, pp. 2]. In support of this research Alberts et al. suggested aconceptual framework modeling the essential elements. By studying this framework the require-ments on communication in military context will become clear, providing understanding of theimplications of DTN in tactical communication networks.

2.1 Conceptual Model

Command and Control is defined by Alberts et al. as a means of “focusing the efforts of anumber of entities (individuals and organizations) and resources, including information, towardthe achievement of some task, objective, or goal” [4, pp. 32]. This focussing efforts have beenrecognized by Alberts et al. as a feedback loop consisting of four essential processes: Command,Control, Sensemaking and Execution [4, ch. 5]. A diagram of this feedback loop is shown inFigure 2.1.

Chapter 5 63

Sensemaking

ceptual model needed to incorporate the concept of a C 2Approach, which affects the processes within the shaded area.

FIGURE 7. C2 CONCEPTUAL MODEL: C2 APPROACH

SENSEMAKING

Sensemaking consists of a set of activities or processes in thecognitive and social domains that begins on the edge of theinformation domain with the perception of available informa-tion and ends prior to taking action(s) that are meant to createeffects in any or all of the domains (for example: the employ-ment of kinetic weapons with direct effects in the physicaldomain and indirect effects in the other domains; the employ-ment of psychological or information operations designed tocreate direct effects in the cognitive and information domainswith indirect effects in the physical domain).

Figure 2.1: C2 Conceptual Model

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Functions

Focussing efforts using these four processes is done by executing several functions [4, ch. 4].While each of these functions is in some way related to communication, they are not equallydependent on communication infrastructure. Of the seven functions mentioned by Alberts et al.the following three functions are most dependent on communication:

Establishing Intent

Establishing intent involves specifying the objectives and the risks deemed acceptable pursuingthese objectives [4, pp. 36]. As such it is a function of Command. Once intent has beenestablished it becomes necessary to make sure this intent is “functionally aligned across thenetwork and with other participating organizations” [2, pp. 137-138] and becomes part of theSensemaking process.

This function is dependent on communication infrastructure to be executed and failure toproperly do so will make it hard for involved entities to coordinate efforts and successfully achievecommon goals. Because of this the integrity of information passing through the network shouldbe protected by preventing loss as well as modification by unauthorized entities.

Determining roles, responsibilities, and relationships

Determining roles, responsibilities, and relationships is another function of Command and “servesto enable, encourage, and constrain specific types of behavior.” The resulting behaviors createpatterns of interactions that will ultimately “determine the ability of the enterprise to accomplishits missions” [4, pp. 39-40].

Traditionally, the flow of information has been tightly coupled to the command relationships,constraining its patterns of interaction. This has grown out of necessity in order to cope with theamount of information, as well as a means to deal with sensitive information. This approach hasled to fragmented organizations, with each entity optimized for a specific task and informationonly traveling up and down the chain of command.

Present day military operation have shown war fighters face a much wider set of tasks,each of them involving different roles, responsibilities and information requirements. Theseroles, responsibilities and information requirements might change within a short amount of time,requiring entities to organize and exchange information on the spot. In order to allow self-organization of these roles, responsibilities and relationships and to encourage collaboratingbehavior the flow of information should be as free from hierarchical patterns of distribution aspossible [3, ch. 4].

When applied to a networked force this means the network should be as self-organizing aspossible, allowing entities from different organizations to establish communication as they seemfit. Consequently, access regulation of information should be fine grained and mission driven andnot be dictated by artificial hierarchy.

Monitoring and Assessing the Situation and Progress

Monitoring and assessing the situation and progress is part of the ongoing process of Sensemaking.Once objectives and a definition of roles, responsibilities, relationships, rules and constraintshave been given these initial conditions are subject to change. How these changes are recognizedand adjustments are made is defined by this function and is heavily influenced by the flow ofinformation and possibilities of collaboration [4, pp. 42-32].

Sensemaking

It becomes apparent from the definition of these functions that the use of communication net-works is of particular importance to the process of Sensemaking. Alberts et al. explain [2, pp.137-138] sensemaking as “understanding of individual and collective processes by which tacitknowledge (e.g., experience, expertise, and culture) is combined with real-time information toidentify, form and articulate appropriate points in an ongoing military operation.” Described

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in terms of four general capabilities “involved in the transformation of real-time battlespace in-formation into appropriate decision events and command intents”, of which the two capabilitiesmost relevant to communication are:

• Shared Situation Awareness, “the capability to extract meaningful activities and patternsfrom the battlespace picture and to share this awareness across the network with appro-priate participants”, and

• Clear and Consistent Command Intent, “the capability to articulate decisions in terms ofdesired goals/effects, constraints, and priorities that are functionally aligned across thenetwork and with other participating organizations.”

Within the definition of these capabilities lies the distinct difference between a military net-work deployed in the battlefield and networks, such as the internet and cell phone networks,found elsewhere. Even though all of these networks provide the means for an hierarchy-freeflow of information, the purpose of a military communication network is to provide its userswith the ability to increase their Shared Situational Awareness by sharing information and tocommunicate Command Intent. Because of this specific purpose the quality of tactical networksshould somehow be expressed in its ability to execute these tasks. Consequently, the performanceof tactical networks should be optimized towards better execution of these tasks. However, inorder to perform optimizations the parameters determining performance need to be known andunderstood.

2.2 Characteristic Requirements

The traditional definition of C2 allowed for only one way of assessing the quality of C2 bycorresponding it to mission accomplishment. Alberts et al. argue the use of missions successas a measure of quality of C2 to be problematic “because the very definition of the mission isa function of command. Hence, a failure to appropriately define the mission, that is, craftingmission objectives that are unattainable and result in mission failure, is in fact a failure ofcommand. (...) Rather, the quality of C2 should be directly measured by examining how wellthe functions of C2 have been performed.” [4, pp. 33] Therefore instead of using mission successas a measure of quality the concept of agility is proposed, making “the ability to recognize a needto change and the ability to adjust” the chosen measures of C2 quality [4, pp. 43].

This shift in assessing the quality of C2 has consequences for the means used in the executionof its functions. Because tactical networks are used to support C2, performance should bemeasured by the ability to serve their purpose in face of these agility demands. Mission success,the ability to serve purpose and technical performance is, among other things, the result of theability to adjust to change. Agility should therefore be taken into account when designing themeans aimed at supporting C2 in accomplishing its task. Applying the six key elements of agilityproposed by Alberts and Hayes to tactical communication networks, the following of list of highlevel characteristics emerges:

• Robustness is explained as “a necessary response to the need to operate across the missionspectrum” [4, pp. 190]. This means specific optimizations in the realization of a net-work should be carefully examined in order to avoid the inability to operate under certainconditions.

• With change continuously present in the dynamic environment of military operations andwindows of opportunity closing fast, seizing the initiative requires on-time delivery of rele-vant information. With the evermore increasing pace of change the need for Responsivenessin the flow of information is growing.

• In order to facilitate self-organization and collaboration Flexibility of the composition ofthe network is required, meaning the network should allow for ad-hoc connectivity.

• The value of information is increased by collaboration. Innovation is how information isused will be necessary to keep an information advantage.

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• The importance of Resilience is growing due to the increasing dependency on informationfor effective mission execution. Operating equipment in the battlespace environment ishard and the network itself will be a target for adversaries. As a result network nodes willfail and disruptions will be the norm rather then the exception. Care must be devoted atall levels of the network to keep these events from leading to failure and loss of information.

• With the diversity of entities involved in military operations networking capabilities willbe equally mixed. This requires Adaptation of the network to the operating environmentin order to maximize its performance.

2.3 Summary

With the help of a conceptual model for C2 the application domain of a tactical communicationnetwork has been defined. Based on the idea that the purpose of such a network is to supportC2 in the process of sensemaking the quality of its implementation is determined by its ability to(1) enable and encourage collaboration trough sharing of information among end-users, allowingthem to increase their shared situational awareness, and to (2) provide the means necessary tocommunicate command intent as clear and consistent as possible across all entities present inthe battlespace.

By examining the measures used to assess the quality of the C2 the following list of high levelcharacteristics was derived:

• Robustness,

• Responsiveness,

• Flexibility,

• Innovation,

• Resilience, and

• Adaption

Further study on the relationship between these characteristics and the parameters definingthem is necessary in order to determine how DTN can be used to increase network performance.

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CHAPTER 3

Application

In Chapter 2 two main capabilities most relevant to a tactical communication network are de-fined as (1) facilitating its users to increase their shared situational awareness (SSA) throughinformation sharing and collaboration, and (2) to communicate command as clear as possible.The quality of such a network is defined by degree in which these capabilities are performed.In order to compare the quality that is delivered in respect to these capabilities a performancemeasure must be defined. In this way the performance of various networks can be comparedquantitatively

3.1 Shared Situational Awareness

Shared situational awareness, as introduced in Chapter 2, is the result of a continuous process ofcollaboration among entities in the battlespace to develop an accurate, shared picture of currentstate of affairs [2, pp. 137-138]. Extracting data from the battlespace is the first step in thisprocess depending on communication networks. After this raw data has been analyzed, the newinformation that is relevant needs to be communicated to the entities involved.

How well the communication tasks in this process are executed is determined by the abilityto communicate, the amount of information successfully communicated and the time needed tocommunicate this information. Expressed in terms of the high level characteristics derived inChapter 2, the performance of a network is determined by its resilience and responsiveness. Inorder to learn the impact of DTN on these parameters consider the following example.

3.2 Force Tracking

One of the first steps in increasing SSA is the accurate plotting of positioning information onentities in the battlespace, in order to make informed decisions on movement, asset deploymentand reduction of accidents such as friendly fire. In case of tracking one’s own force, so calledBlue Force Tracking (BFT), this elementary but important task can be automated by equippingall blue entities with positioning sensors each broadcasting its position. Red Force Tracking(RFT) of non-friendly forces is a more difficult process involving fusion of multiple and probablydifferent sources of information in order to get an accurate image.

Movement of entities is a continuous function in time represented by an infinite number oflocation coordinates. Automating the tracking of forces will require sampling of this data atdiscrete intervals, hence introducing a loss of accuracy during the interval between samples.When expressed in terms of SSA, this loss in accuracy will result in a less accurate pictureof reality and can therefore be interpreted as loss in SSA. Upon communicating these locationcoordinates using a tactical network, the performance of this network will have an impact onthe loss in SSA. If no coordinates are received due to a lack of communication opportunities orloss of information the loss of SSA will increase. Consequently, because entities are moving, the

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time required to communicate location coordinates will affect their accuracy. Delay introducedby the network will result in additional loss of SSA.

In this force tracking example, loss of SSA at some point t in time is directly related to theachieved accuracy when plotting the location coordinates of other entities in the battlespace.Considering the location c(t) of an node (entity) as a function of time, this location will beperceived by others as some approximation c′(t) due to sampling and communication. The forcetracking error eij(t) of some node i as perceived by another node j in the battlespace is equal tothe distance between these functions and expressed by:

eij(t) = |c′ij(t)− ci(t)|

Given this error for all nodes in the battlespace, the force tracking error Ej(t) perceived anode j is expressed by the following sum:

Ej(t) =

n∑i=1

eij(t)

Finally, the total force tracking error E(t) perceived at any point in time is given by:

E(t) =

n∑j=1

Ej(t) =

n∑j=1

n∑i=1

eij(t)

With this total error E being a measure of SSA loss, achieving an error closer to 0 translatesinto less SSA loss and because of that better C2 performance.

3.3 Network Implementation

The example of force tracking has illustrated that SSA will be affected by the implementationand performance of a tactical network. An assumption made by IP regarding connectivity is thatan end-to-end path must exist between two communicating nodes. If such a path can not befound information, then sent into the network will be lost. By storing information in the networkDTN is able to loosen this requirement, allowing the path between nodes to be disrupted for aperiod of time. As soon as connectivity is restored DTN will attempt a delayed delivery of theinformation.

By better utilization of communication opportunities DTN improves network resilience, ef-fectively reducing SSA loss. However, because of the effort required to store information in thenetwork and monitor communication opportunities DTN will introduce additional delay, affect-ing network responsiveness and increasing the SSA loss. Because both these parameters are theresult of many more, often implementation specific variables their combined impact on SSA lossis difficult to predict.

3.4 Summary

SSA is affected by the network used to communicate the information necessary to build a sharedpicture of the battlespace. Loss or delay of information will result in loss of SSA. Alterationsin the parameters behind the requirement characteristics derived in Chapter 2 will increaseor reduce this loss, affecting the ability of entities in the battlespace to develop their sharedsituational awareness. An example of how this SSA loss can be quantified by considering a forcetracking application. Because of the complex behavior of the parameters involved in networkimplementations study of their effects is necessary. The next chapter describes an experimentperformed to study the effect of resilience and responsiveness parameters on SSA loss.

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CHAPTER 4

Experiment

In Chapter 3 SSA loss is quantified using a force tracking application. This chapter describes anexperiment using this force tracking. The goal of this experiment is to study the effects of thecharacteristic requirements of resilience and responsiveness on SSA loss.

4.1 Overview

The experiment consists of four simulated nodes moving across terrain in a group formation.Each node is running a Blue Force Tracking (BFT) application which transmits the locationof the node using a communication network, implemented as a network model. The networkmodel uses a radio model to transmit and receive information sent by the BFT application.Node movement and the characteristics of the radio model will produce a certain amount oflink availability. The combination of link availability, network model and BFT application willresult in delay and loss of information. Finally, this delay and loss of information will producean amount of error in SSA. A schematic representation of the experiment is shown in Figure 4.1.The experiment itself will be performed using the OPNET Modeller network simulator [13].

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4.2 Blue Force Tracking Application

All nodes run a blue force tracking application periodically broadcasting the node’s position witha fixed interval ∆t. Each node maintains a table containing the last known location of all other

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nodes. Every second each node uses the last known location of its neighbors and the expressionderived in Chapter 3 to record the error Ei.

Being a broadcasting application communicating over a wireless ad-hoc network, nodes lo-cated far apart will not overhear each others location updates. In order to distribute updatesthroughout the network intermediate nodes will need to rebroadcast updates when necessary.This rebroadcasting of traffic should be done carefully in order to not waste the limited net-working resources available. Because nodes might overhear the retransmission of updates thenetwork keeps track of these duplicates in order to avoid the same information being deliveredto the application more than once. The network models evaluated in this experiment implementduplicate checking where appropriate.

4.3 Network Models

Three network models have been implemented, each with different resilience and responsivenesscharacteristics.

Perfect Network

The perfect network is is a reference implementation representing the perfect network. All trafficsent through this network is instantly delivered to its destination without loss or delay. Executingthe experiment using this perfect network will reveal the loss in SSA due to communicating BFTupdates using a fixed interval, illustrating the theoretical limitations of running BFT applicationson top of a tactical network. Figure 4.2 is plot of the SSA error over time for ∆t is 10, 30 and60 seconds.

Figure 4.2: The mean SSA error for all nodes over time using a Perfect network for ∆t is 10, 30and 60 seconds.

Basic Multicast Forwarding Network

A network using IP protocols is implemented using the Open Link-State Protocol. OLSR is arouting algorithm specifically designed for wireless ad-hoc networks, and as such is consideredto be one of the protocols suitable for tactical communication networks [7]. Being a proactiverouting protocol OLSR, maintains a topology database used to calculate shortest paths throughthe network. Synchronization of this database is required in order to keep the IP routing tableup to date and this is done by periodically flooding the network with link state updates. In orderto control this flooding the protocol elects a set of Multipoint Relays, or MPRs. These MPRs actas local hubs in the network distributing the topology updates to neighbors in close proximity.Using the same MPR selector set, the Basic Multicast Forwarding router is able to rebroadcast

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traffic in a controlled manner, spreading data throughout the network without wasting too muchresources.

Disruption Tolerant Network

The disruption tolerant network is implemented using the Epidemic protocol, one of the firstDTN routing algorithms proposed [14]. Each router stores the data it receives in a buffer, ideallybacked by persistent storage. Upon coming into contact with other nodes they periodically syn-chronize their buffers by exchanging a summary of the data present in each buffer and requestingthe transmission of data not present in both buffers. Given enough contacts data will spreadthroughout the network, eventually reaching its destination.

Detection of nodes within range is done by periodically broadcasting small beacon messages.Each node keeps a small lookup table with timestamps on the last time a beacon has been receivedfrom its neighbors and when their buffers have been synchronized. When synchronizing bufferseach node sends a summary of its buffer by hashing the header of stored data packets. In orderto keep the state each node maintains as low as possible no information is stored about whichdata packets have been exchanged in the past. This keeps the implementation fairly simple, buthas the downside of sending a full summary of the buffer each time two nodes synchronize. Tokeep these summaries small enough to fit in a single transmission only the 500 most recent bufferentries are taken into account. This approach will be sufficient for this experiment, but morecomplex networks will require a better solutions to the question which data packets to includein the summary.

4.4 Radio Model

The radio model used is an implementation of the 802.11b WIFI standard. The transmissionrange of this model can be fixed at a certain distance r. Nodes beyond this range r will not beable to communicate.

4.5 Mobility Model

Most DTN protocols have been evaluated using the Random Waypoint mobility model [10]. Theexperiments performed often randomly distribute a given amount of nodes over a confined area.Nodes then travel to a random location inside this area using a random speed. Upon arrivalnodes wait for some amount of time before repeating the same procedure.

Because military operations are never carried out as random movements by single units a newGroup Random Waypoint mobility model has been implemented. An illustration of the modelis shown in Figure 4.3. The model implements four nodes traveling traveling as a group in a twoby two formation over parallel paths. While crossing terrain the two leading nodes are separatedby a distance y. Nodes traveling along the same path maintain between them a distance x. Bothdistances x and y are a result of military procedures and influenced by factors such the typeof the operating unit, terrain conditions, hostile threat etc. While on the move the nodes willbe subject to terrain features causing deviations from their intended path causing the distancebetween nodes to vary over time.

The implementation of this mobility model is done by executing the Random Waypoint modelon group level as well as individual node level. The group of nodes is considered a single unittraveling along a path of random waypoints. Inside the group each node is assigned a quadrantwith dimensions α and β. While traveling as a unit each node moves to random waypoints insideits quadrant with a chosen speed relative to that of the group. When the motion of the group andthe relative motion of each node is considered the result is that of four nodes traveling towardsthe same waypoints maintaining distances x and y between them with small deviations up to αand β. An example movement pattern of the Group Random Waypoint model is show in Figure4.4.

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The mobility model is implemented outside of OPNET as a Python script. Using a pseudorandom generator the movement patterns are generated and saved in so called trajectory files.The trajectory files are then loaded into OPNET.

Figure 4.4: Example of the movement pattern of the nodes as group in Group Random Waypointmobility model. The inset illustrates a detailed part of the individual node paths.

4.6 Link Availability

With the distance between nodes changing over time due the mobility model and the transmissionrange of the radio model fixed at a certain distance r, nodes will be in and out of contact witheach other. When the distance between two nodes is smaller then range r a link between thesetwo nodes will be available, allowing the nodes to communicate. The links between nodes areshown in red in Figure 4.3. Using the trajectory files generated by the mobility model script,the number of available links is determined with a one second interval. Figure 4.5 illustrateshow link availability between nodes varies over time for different values of r. With four nodes a

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maximum of 6 links will be available.

Figure 4.5: Link Availability

4.7 Parameters

The parameters for the actual experiment have been chosen as follows. The nodes have beenmodeled after small military vehicles patrolling a 10km by 10km area. Random waypoints arechosen using a uniform distribution inside the patrol area, with the speed chosen uniform between5 and 10 m/s. The relative speed of each node is randomly chosen between 1 and 3 m/s with awaiting time between 0 and 5 seconds. While moving the vehicles keep a distance x of 30 metersand a distance y of 20 meters. Deviations α and β are set at 10 and 5 meters.

Using these movement parameters an average link availability of around 25%, 50% and 75%was achieved by fixing the transmission range r to 24, 28 and 34 meters.

The blue force tracking application is sending updates with 10, 30 and 60 second intervals.The SSA error as experienced by each of the nodes is measured every second. The beacon intervalof the DTN routing protocol is set at 5 seconds and nodes will synchronize their buffers as oftenas possible but no more then once every 30 seconds. Parameters of the OLSR protocol are equalto those used in the OLSR example network provided with the OPNET simulator.

The simulation time was set at 8 hours, loosely reflecting the duration of military patrols.

4.8 Summary

In order to study the effects of the characteristic requirements of resilience and responsiveness intactical networks on SSA loss an experiment has been developed using the force tracking examplepresented in Chapter 3. In support of this experiment a new mobility model called the GroupRandom Waypoint model has been developed. Three networks with different resilience andresponsiveness characteristics have been implemented; a perfect network, an IP ad-hoc network

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and an Epidemic disruptive tolerant network. The results of this experiment are presented anddiscussed in Chapter 5.

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CHAPTER 5

Results

In Chapter 4 an experiment has been described which was used to study effects of the charac-teristic requirements of resilience and responsiveness on SSA loss. In this chapter the results ofthe experiment are analyzed and discussed.

5.1 Summary

In order to reduce the influence of individual random values 50 different trajectory files havebeen generated, simulating 50 different patrols. For each of these patrols the mean values of

the mean values of information loss and SSA error Of these 50 patrols mean values are takenfor information loss and SSA error, and a summary of these results are listed below. Table 5.1shows the mean values and 99% confidence intervals of the fraction of tracking updates lost dueto a link availability. The mean SSA error in meters, a result of delay and loss of updates,together with the 99% confidence intervals is shown in Table 5.2.

∆t link avail. info loss 99% CI

Perfect10 100% 0.000 0.00%30 100% 0.000 0.00%60 100% 0.000 0.00%

BMF

1025% 0.713 0.00%50% 0.150 0.01%75% 0.074 0.01%

3025% 0.713 0.00%50% 0.152 0.01%75% 0.074 0.01%

6025% 0.712 0.00%50% 0.152 0.01%75% 0.075 0.02%

DTN

1025% 0.012 0.78%50% 0.001 0.09%75% 0.001 0.09%

3025% 0.002 0.11%50% 0.001 0.11%75% 0.000 0.20%

6025% 0.002 0.12%50% 0.001 0.18%75% 0.000 0.20%

Table 5.1: Summary of Information Loss.

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∆t link avail. error 99% CI

Perfect10 100% 36.24 2.62%30 100% 109.53 2.64%60 100% 215.12 2.73%

BMF

1025% 805.72 6.02%50% 49.90 3.54%75% 42.18 2.85%

3025% 1394.32 4.62%50% 150.47 3.71%75% 128.72 3.32%

6025% 1766.80 4.44%50% 293.98 4.20%75% 251.80 3.56%

DTN

1025% 475.58 8.48%50% 236.02 5.73%75% 195.10 3.09%

3025% 444.04 3.64%50% 283.75 3.02%75% 128.72 3.23%

6025% 547.37 3.54%50% 385.34 3.13%75% 342.96 3.11%

Table 5.2: Summary of SSA Error.

5.2 Interpretation

In order to illustrate the delay and loss of information and their effect on SSA error Figures 5.1,5.2 and 5.3 show the delay and amount of information over time for the same track, ∆t is 60seconds and link availability of 25%.

Information Loss

When implementing the experiment it was presumed the loss of information would not be subjectto a uniform distribution, due to the non uniform behavior of the nodes moving as a group. TheBMF implementation seems to support this presumption as down to a link availability of 50% onaverage only 15% of the tracking updates are lost. Upon further decrease of link availability theloss of updates raises quickly, losing on average another 54% of the updates despite only 25% lossof link availability. At this point the true strength of delay and disruption tolerant networkingbecomes apparent as the DTN implementation suffers an information loss of little over 1% onaverage, despite the low link availability.

Despite storing the tracking updates in a buffer DTN does seem to suffer a little loss ofinformation, most notably for ∆t = 10s and low link availability. This is probably due to thebuffer synchronization summaries being limited to a fixed number of packets, causing some ofthe old packets not being synchronized after long delay. The fraction of loss seen for ∆t = 30and 60s is probably due to a small number of tracking updates not delivered before simulationhas ended.

SSA Error

Figures 5.4, 5.5 and 5.6 illustrate the relation between link availability and SSA error ∆t is 10,30 and 60s seconds. When looking at the mean SSA error it becomes apparent the use of DTNresults in a higher error when link availability is over 50%. This can be explained by the buffersof nodes only being synchronized at best once every 30 seconds. The additional transmissiondelay introduced raises the SSA error considerably. Upon sending updates with little loss of

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Figure 5.1: Information Delay

Figure 5.2: Information Received

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Figure 5.3: SSA Error

information due to link unavailability, the mean SSA error induced by the BMF implementationis only a quarter of the error derived from the DTN implementation.

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With link availability dropping below 50% the mean SSA error of the BMF implementationquickly increases, which is due to the rapid increase of lost information. Because only a smallfraction of the information is lost in the DTN implementation the mean error appears to be muchmore stable despite this decline in link availability. The resilience to link failure of the DTNimplementation is most clearly demonstrated when the importance of update delivery is raisedby sending tracking updates only once every 60 seconds. Despite delay the DTN implementationsuffers relatively little additional SSA error when link availability drops to 25%, resulting in amean SSA error which is only a third compared to the BMF implementation.

5.3 Discussion

The experiment has demonstrated DTN will increase the performance of a tactical network byincreasing the resilience to the disruption of communication links. However, this gain comes

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at the price of an increase in delay, which leads to an overall decrease in responsiveness of thenetwork.

In the case of a force tracking application this loss of responsiveness has a strong influenceon the SSA accuracy, causing BMF to perform much better then DTN when loss of informationis low. Once link availability starts dropping below a certain threshold information loss quicklyoutweighs information delay, and network resilience will become the dominant factor determiningC2 performance.

The results of this experiment have shown the relation between the resilience and responsive-ness characteristics of a tactical network and C2 performance using a force tracking application.Even through force tracking is an important application in military context other applications,mobility models and DTN algorithms should be considered before general conclusions can bedrawn.

This experiment has been performed using only four nodes. Even though this number isfairly common in the context of military operations the scale is limited. Consequently, the BFTapplication was the only application running on the network, never utilizing the full capacity ofthe network. With more applications running other factors will influence performance and otherhigh level network characteristics will become important.

5.4 Summary

The performance of tactical military communication networks is influenced by many parameters.DTN can improve this performance by increasing the resilience to disruption of communicationlinks. However, this increase in resilience comes at a price of a decrease in responsiveness, due tothe network persistently storing the information. Whether DTN will improve the performanceof a tactical network is dependent on the delay tolerance and the influence of responsiveness ofthe network on SSA loss. Given enough disruption the increase in resilience provided by DTNwill outweigh the influence of delayed information on C2 performance.

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CHAPTER 6

Conclusion

6.1 Conclusion

By studying the work written by Alberts et al. on the organization of military organizations, thepurpose of tactical communication was defined as twofold; (1) enable and encourage collaborationtrough sharing of information among entities in the battlespace, allowing them to increase theirshared situational awareness, and to (2) provide the means necessary to communicate commandintent as clear and consistent as possible across the network.

Given this strong relation between tactical communication networks and C2, their perfor-mance should be measured using the same agility quality measures. From these agility qualitymeasure the following high level characteristics was derived. Tactical communication networksshould be:

• Robust,

• Responsive,

• Flexible,

• Innovative,

• Resilient, and

• Adapting

By considering a force tracking application the, C2 performance was quantified by introducingthe concept of loss of SSA. Using the same force tracking example this loss of SSA was shown tobe determined by the characteristics of resilience and responsiveness.

In order to study the relation between these characteristics and loss in SSA an experimentwas developed. In support of this experiment, models of a perfect network, an IP ad-hoc networkand a disruptive tolerant network, as well as a new Group Random Waypoint mobility modelwhere implemented.

By executing the experiment using the OPNET network simulator it was illustrated DTNwill improve the resilience of a tactical communication network to disruption of its communi-cation links. However, this increase in resilience comes at the price of a decrease in networkresponsiveness, due to delay introduced by persistent storing of information. In the case of theblue force tracking application used in the experiment DTN will improve C2 performance whenon average more then 50% of the communication links become unavailable.

6.2 Future Research

This research has only shown the relation between the characteristics of resilience and respon-siveness and C2 performance. Study of the other characteristics is necessary to gain betterunderstanding of their relationship and their impact on C2 performance.

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Furthermore, additional applications, dealing for example with voice information, shouldbe considered to gain better understanding about the delay tolerance of information. Otherperformance parameters such as bandwidth limitations, should be studied as Quality of Servicewill be important topics in the context of tactical communication.

The experiment could be improved by adding sequence numbers to streams of information,allowing the DTN middleware to ignore old copies of the same data. In case of this experimentthis will greatly reduce the buffer size of the nodes as only the last known location needs to bestored.

Finally, further research should be on DTN algorithms with better responsiveness allowingbetter use of the improvement in network resilience.

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[4] David S. Alberts and Richard E. Hayes. Understanding command and control, 2006.

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[7] Peter Holliday. Nomad. Distributed Computing Systems Workshops, International Confer-ence on, 0:488–492, 2009.

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[10] Evan P. C. Jones. Practical routing in delay-tolerant networks. In in Proc. WDTN, pages237–243. ACM Press, 2005.

[11] Rajesh Krishnan, Prithwish Basu, Joanne M. Mikkelson, Christopher Small, Ram Ra-manathan, Daniel W. Brown, John R. Burgess, Armando L. Caro, Matthew Condell,Nicholas C. Goffee, Regina Rosales Hain, Richard E. Hansen, Christine E. Jones, VikasKawadia, David P. Mankins, Beverly I. Schwartz, William T. Strayer, Jeffrey W. Ward,David P. Wiggins, and Stephen H. Polit. The spindle disruption-tolerant networking sys-tem. In Military Communications Conference, 2007. MILCOM 2007. IEEE, pages 1 –7,oct. 2007.

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