Paper ID# 901028

SECURE COOPERATIVE AD HOC APPLICATIONS WITHIN UAV FLEETS1

- POSITION PAPER-

Serge Chaumettechaumett@labri.fr

Remi Laplacelaplace@labri.fr

Christophe Mazelcmazel@fly-n-sense.com

Aurelien Godinaurelien.godin@dga.defense.gouv.fr

LaBRI, Universite de Bordeaux351 Cours de la Liberation

33405 Talence Cedex, France

Fly-n-Sense, Bordeaux TechnoWest25 rue Marcel Issartier, BP 20005

33702 Merignac Cedex, France

DGA / ETAS / VTRRoute de Laval, BP 60036

49245 Avrille Cedex, France

ABSTRACT

A proper situation management system is one ofthe keysto success on the battlefield. It strongly relies on data collec­tion, fusion and interpretation. Unmanned Aerial Vehicles(UAVs) are becoming a major collection platform, but thereis almost no collaborative intelligence in these systems. Weclaim that a swarm of small UAVs connected together thatcould make cooperative decisions in an autonomous way(without requiring any intervention from the ground) wouldbe a major contributor to the information superiority. Inthis paper we present our vision of this approach and theresearch project that we are running to achieve an opera­tional system. It deals with levels 1, 2 and 4 ofthe DoD JDLdefinition ofinformation fusion. This is a position paper de­scribing a work in progress.

1 INTRODUCTION

The status of a military field of operation is very dynamic,therefore situation management is a major concern. This pro­cess is based on many mechanisms among which on the fielddata collection is central.

This collection process will be increasingly supported byembedded computers and sensors. Properly equipped sol­diers suits have become real-time information sources for thedecision corp. Among the significant systems are the FE­LIN 2 Infantry Combat Suit in France and the Future ForceWarrior (FFW) in the United States.

With the constant decrease in size and cost of embed­ded devices and their increasing communication capabilities,Mobile Ad hoc Networks (MANets) and Delay Tolerant Net­works (DTNs) have grown very important on battlefields.The considered devices need to collaborate even though theycannot rely on any stable communication infrastructure. Anad hoc network must thus be set up on the fly. It shouldbe noted that in the military context, the resulting configu­rations are strongly mobile and versatile. This is due to a

1This work is partly funded by a DGA PhD grant.2Fantassin it Equipements et Liaisons INtegres

number of factors: field conditions; interferences; presenceof the enemy. This kind of setup not only exists betweentroops, but also between vehicles (this is referred to as Ve­hicular Ad hoc Networks, VANets). Their extension to Un­manned Aerial Vehicles (UAVs) clearly makes sense. This isthe configuration that we are considering in this work, whichis partly funded by the french army (DGA3) and the Fly-n­Sense company [1], whose goal is to supply civil UAVs andassociated services. It should be noted that this work is alsosupported by the World Competitiveness Cluster AerospaceValley [2] through the SYMM4 project.

Using such a configuration raises a number of problemsamong which security and fleet control are prominent.

Security is a major concern. One of our contributionsconsists in embedding smart cards to support the appropri­ate features. This is based on our previous work on the nonplanned establishment of secured groups in a mobile ad hocnetwork. Our approach should reduce the impact of packetsniffing by enemies, of trapping a UAV, or of using an in­truder. Security will be discussed in section 8.

Fleet control is also important. In standard approaches,the applications have to adapt to the mobility of the system.They have absolutely no control over the flight pattern inthe case of regular MANets, and little control in the case ofground VANets (it is mandatory to follow the roads). Whenworking with a fleet ofUAVs, it becomes possible to controlthe mobility of the vehicles. Even though they are thrownwith an initial flight plan to achieve a given mission, theycan be diverted from this plan. The structure of the network(the interconnection graph) can thus be adapted by movingUAVs around so as to support a given application. Based onthese considerations, our goal is to redesign a number of al­gorithms and mechanisms that we have created for more reg­ular MANets, taking advantage of the possibility to modifythe flight plan in order, for example, to improve connectivitywithin the fleet. This will be discussed in section 5.

We believe that by extending our previous work on mo-

3Delegation Generale pour l' Armernent4SYsteme de gestion de Mission pour Microdrone

978-1-4244-5239-2/09/$26.00 ©2009 IEEE 10f7

bile ad hoc networks to UAV fleets we will contribute to abetter operation/understanding of collaboration in this spe­cific kind of mobile ad hoc network, and thus to better sup­port situation management. The collaboration that we haveset up (University of Bordeaux, DGA, Fly-n-Sense) and thatincludes a civil UAVs supplier makes it possible to achieveexperiments without the constraints imposed by the militarycontext. At the same time, this will lead to real products thatFly-n-Sense will be able to bring to the market. Once op­erational, it will be possible for the DGA to adapt them tomilitary UAVs.

The rest of this paper is organized as follows . In section 2we review related work. In section 3 we describe the militaryand civil contexts in which UAVs and UAVfleets are consid­ered. We introduce in section 4 the system we are workingon and the specific features of the hardware that we are con­sidering. In section 5, we give an overview of the formalmodel that we are designing and that will support the vali­dation of applications. Section 6 is dedicated to the descrip­tion of some of the applications that we plan to implementor adapt from our previous work on more regular MANets.Section 7 introduces the simulation issues. In section 8 wepresent our approach of hardware and software security forUAVfleets. We eventually conclude and give future researchdirections in section 9.

2 RELATED WORK

Some research projects on UAV fleets already exist. Nev­ertheless most of them focus on a specific problem , whereaswe plan to offer a global framework, from a formal modeldown to real implementations.

The RECUV project [3] deals with the radio leashingproblem, the goal of which is to create and maintain optimalcommunication chains involving UAVs [4,5] . The positionsof UAVs are adapted based on the analysis of the receivedsignal. This interesting approach could be an alternative tothe first GPS based localization method we intend to use.

The C3UV group [6] at Berkeley has developed a methodthat enables distributed collaboration within a UAV fleet. Itis based on the usage of the Mission State Estimate (MSE)[7-9]. The MSE is a local data structure that contains thecurrent distribution between UAVs of elementary tasks toachieve to complete a global mission. This data structure isupdated often enough so that the system converges towardsan optimal task allocation. It is one of the most advancedprojects of this kind but it does not offer a formal approachas we intend to.

It should also be noted that a number of experimental fa­cilities exist around the world, among which the AerospaceControls Laboratory at MIT [10,11] .

3 DAVFLEETS IN MILITARY AND CIVILDOMAINS

3.1 MILlTARYCONTEXT3.1.1 UAVs in the BOA

In modem conflicts, controlling the information is a key totaking advantage over the opponent. The BOAS Demonstra­tor, the French equivalent of the Network Centric Warfare,proposes new means to enable efficient communication. Ev­ery friendly actor connected to this infrastructure participatesin the global data collection and can retrieve commands fromother partners .

The efficiency of this system strongly relies on the capac­ity to gather pieces of information quickly, being given thatthey can be collected far away, possibly beyond the enemylines. Unmanned Aerial Vehicles are an appropriate solutionto achieve this task. Such systems actually take very littletime to join an interest site, even when it is located severalkilometres away from their take-off point. They are able tokeep flying over the area quite a long time, typically half anhour or more for the systems expected in the BOA demon­strator. Hence, UAVs provide friendly forces with real-timeinformation which, in tum, give them the capacity to antici­pate threats , such as movements of hostile opponents.

Apart from data collection, another application is line ofsight and non-line of sight data links setup, UAVs playingthe role of repeaters. It has been developed mainly to providehigh throughput and high availability to long range and highaltitude platforms.

Many companies all around the world produce and sellmicro and mini-UAVs that can provide the above mentionedservices. American (Micro Air Vehicle - MAV from Honey­well or Pointer, Puma and Raven from AeroVironment [12])and Israeli companies (Skylite from Rafael or Skylark fromElbit Systems) are probably the leaders on this segment. Eu­ropean companies (EMT [13] in Germany with Aladin, orEADS [14] in France with the DRAC used in BOA, Figure 1)also propose very competitive products.

Figure 1: A DRAC from EADS

5Bulle Operationnelle Aeroterrestre

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3.1.2 Rational and issues of military UAV fleets

One single vehicle in the air can only achieve the surveil­lance of a limited area. Extending the coverage to the wholebattlefield is compulsory and, in the future, the simultane­ous deployment of multiple aerial vehicles can be a plausi­ble solution to tackle this issue. Besides, if these systemscan cooperate with each-other, a huge gain of performanceis foreseen. From the navigation point of view, this coordi­nation will enable the computation of optimized trajectories,which is a factor of efficiency and safety. Another outcomeis that the loss of one or even several vehicles will not bedramatic as the remaining ones will be able to negotiate andre-plan new paths. Such re-planning process could also befired when a threat has been detected by a friendly actor sothat one of the flying systems is diverted from its originalroute to confirm the detection, or engage it. From an op­erational point of view, cooperating vehicles can be used toquickly build large accurate maps of a given area, thanks todata fusion techniques.

By using UAVfleets on the battlefield, it is also possible totake down the communication means of the enemy by jam­ming their transceivers locally and at a reasonable cost. Thiscan be achieved as follows. First, each UAV measures theenemy signal, then the fleet shares the collected informationin order to triangulate the position of the transceivers. Oncethe transceivers have been located, one or more UAVs canfly close to them and shoot them (since these UAVs are low­cost, one can afford to possibly loose some of them).

3.2 CIVIL CONTEXT

3.2.1 UAVs for civil usage

The two major advantages of a UAV, compared to a stan­dard aircraft, are: the possible miniaturization (which re­duces the costs and fosters adoption); the evolution in '3D'environment, 3D standing for Dull, Dirty and Dangerous, i.e.unsuited to human pilots. These advantages were recognizedvery early in the military field. The 3D concept can be trans­posed to civil monitoring missions that are likely to last forseveral hours, even several tens ofhours, in hostile industrialenvironments (e.g. nuclear power plant accident, chemicalindustry disaster, etc).

However, two major problems exist: (1) which technol­ogy to use? (2) how to deal with the aircraft traffic controlregulation? (1) The technology used for large UAVs (of sizesimilar to manned aircraft) is very similar to that of stan­dard aircraft and raises similar problems. Even though def­initely less developed, the technology of small UAVs alsoexists, and finds success by the aeromodelists. Nevertheless,professional applications have not widespread till now: ob­jectives, performances, level of reliability, costs, etc., haveto be considered specifically in this context. (2) The sizereduction ofUAVs facilitates their operation, which requiresneither installation on the ground, nor expert in piloting. The

regulation for small UAVs is less strict than for bigger ones.Nevertheless, ifpiloting from the ground, in the Visual Lineof Sight, is very well controlled by the model makers, it islargely insufficient for all other applications. A certain de­gree of autonomy of flight, as well as automatic navigationare thus required. This is the topic of our work.

Areas where UAVs are likely to have a major impactare surveillance and remote data collection. Examples ofsingle UAV applications include wildfire surveillance, cropand vegetation surveying, emergency data transfer, securityof people and protection of assets against terrorist relatedthreats.

3.2.2 Rational and issues of civil swarms of UAVsUsing micro-UAV swarms in a civil context makes sense

since peer-to-peer communication between UAVs equippedwith different types of sensors enables a better vision of theglobal state of the mission within the fleet. It can then helpsupport intelligent reaction. There are many potential ap­plications among which chemical sensors based monitoring(e.g. for chemical disasters), wild animals surveillance, mea­surement of buildings thermal loss in a city, etc.

Some of these civil applications will require to keep acertain number of UAVs constantly flying. For instance, itmakes sense to maintain a constant monitoring when dealingwith chemical disasters, or when monitoring wild animalsfor a long term survey. In such cases, the use ofUAV swarmswill make it possible to replace a UAV by a 'fresh' one, whilethe former is refueling. This can be achieved seamlessly ifthere is a sufficient number of UAVs available, and if theswarm can collaborate to manage the replacement of one ofits members.

3.2.3 Open problems and research directionsThe tactical use of small UAVs raises different questions.

Among the concerns are: short range but often non-lineof sight communication, multipath physical channels, directlink required with the user communication system (regula­tion). This implies the need to build different solutions (suchas relaying, ad hoc networking, MIMO air interfaces) and touse different techniques (light weight communication pay­load, etc.).

Once these low level problems have been dealt with, en­suring cooperative use of heterogeneous unmanned systems,supervising the complex resulting overall architecture, com­puting a merged model, are still open and very difficult sci­entific issues.

3.3 CONCLUSIONIn this section, we have seen that, in both military and civil

contexts, UAVs and UAV fleets are promising technologies.It is also quite clear that a network built with ground wire­less sensors and small collaborative UAVs could be a majorcontributor to the information superiority.

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The military and civil domains share many similar prob­lems even though their final goals are not the same. For in­stance, connectivity among the fleet has to be dealt with inmost applications (the maintenance of overlay networks isone of our major algorithmic concerns) .

4 TARGET UAVS & SUPPORTINGTECHNOLOGIES

In this section we describe the target and experimental en­vironments that we are using.

4.1 THE PAPARAZZI AUTOPILOTPaparazzi [15] is an open-source project started in 2003

whose goal is to design and build a cheap autopilot for fixedwing autonomous UAVs. The autopilot component has al­ready been used on more than 15 airframes by several teamsaround the world. Hundreds of hours of autonomous flighthave successfully been achieved with it.

The Paparazzi system includes the airborne hardware, theairborne autopilot software, a ground control station, thecommunication protocols linking the different componentsand a simulation environment.

Safety has been one of the main concerns during all thephases of its development : the airborne code has been madeas simple and short as possible; software and hardware seg­regation ofthe critical code ensures a good level of reliabilityof the system.

Peperezr i EquipedModel A/reraN

GroundS/a/ion

Figure 2: Paparazzi System Components[Source : http : / /paparazzi.enac .fr/ reproduced withcourtesy of ENAC]

The flight plan language (XML-based) allows the user todefine complex autonomous missions while taking externalevents into account. The ground control station is based ona client/server architecture which enables to control one orseveral aircraft from one or several locations. The groundstation operator can control the aircraft with high level com­mands (e.g. area coverage patterns).

4.2 FLY-N-SENSE UAVSThe goal of Fly-n-Sense is to supply micro-UAVs (MAVs)

equipped with sensors (limited to a few hundred grams),and compliant with the Air Traffic Regulation (flying at Vi­sual Line of Sight/VLOS or Below/BLOS with specific au­thorizations). The embedded hardware will allow obser­vation and detection of specific characteristics in the closeenvironment : photographs or videos at low altitude withelectro-optical sensors; atmospheric measurements (collect­ing and/or analyzing chemical samples with biological­chemical sensors or bio-sensors); radio sampling (radiotracking, transmission, measurement) with electromagneticsensors.

There are not many available micro-UAVs dedicated tocivil applications, because the underlying technologies re­quire a high level of expertise and the air regulation impliesa strong miniaturization of the platforms so that they can beoperated safely.

Figure 3: MicroUAV FNS900-Seeker class (Fly-n-Sense)

Nevertheless, for a company such as Fly-n-Sense that hasthe proper expertise, the technology of Micro Aerial Vehi­cles is now ready to be deployed for real applications. Thesmall size (less than 1 m, Figure 3) and light weight ofMAVsmake them easier to transport to the flight spot and to oper­ate than the now extensively used UAVs of larger wingspan.Despite its small bulk, a MAV is able to fly a lightweight pay­load which can fulfill arbitrary complex missions in a fullyautonomous way, with a satisfactory endurance (30 mn toI hour). The small cost of a MAV makes it very attractivefor multipurpose missions and even makes it disposable forsome applications (it can be broken or even shot down). So,MAVs are now good candidates for observation and surveil­lance missions.

4.3 EXTENDED FLIGHT MANAGEMENT SYSTEMThe main innovation that makes the Fly-n-Sense differ­

ent brands of UAVs good platforms to set up a fleet is itsExtended-FMS. The integration of an autonomous missionmanagement module or Flight Management System (FMS)

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based on microcomputers or micro CPUs guarantees thatreal-time processing of the data collected from the airbornesensors will not impact the autopilot which is a critical re­source for the execution of the mission and the safety ofboththe equipment and the people on the ground.

The FMS, linked to the autopilot, may autonomouslyachieve any complex operation/computation to monitor, de­tect and locate any event considered significant for the cur­rent mission. The computing power provided by the newgenerations of embeddable CPUs offers great functional andoperational possibilities. It is now feasible to analyse im­ages or videos in real-time, to detect obstacles, to computeoptimal trajectories or to cypher multipoint communicationson-board.

5 A FORMAL MODEL TO SUPPORT UAVFLEETS

UAV fleets definitely need to operate in a perfectly safemanner: they must not cause any harm; they must not destroyeach other because of improper operation; they must achievetheir mission as expected. A proper model must thus be usedand defined if not available. This is the topic of this section.

In the near future our model will be supported by simula­tion tools that will make it possible to check both the algo­rithms that we will design and to test various graph configu­rations (i.e. flight plans).

5.1 A MODEL FOR DYNAMIC MOBILE AD HOC NET­WORKS

The aspects that a proper model needs to cope with arethe following: the collaborative algorithms embedded in thefleet ofUAVs; the initial flight plans and the FMS controlledmodifications; the inter UAVs communication.

A good candidate model is the graph relabeling approach[16]. In this model, the nodes of the graph can be used torepresent the UAVs and their status (by using a label) andan edge between two nodes a possible communication. Thestructure of the graph changes over time based on the move­ments of the fleet and on the status of the radio links. Anumber ofrewriting rules describe the algorithm executed byeach node, a rule consisting in relabeling a node dependingon the status of its neighbour nodes.

In [17,18] we have extended this model to introduce Dy­namicity Aware Graph Relabeling Systems (DAGRS). Theevolution of the system is still based on local rules, but anode is now aware of the evolution of its neighbor nodes, notonly through the messages that they exchange together butalso through the evolution of the local connectivity. By de­tecting disappearing/appearing edges it is possible to adaptthe system to the structural evolutions of the graph, i.e. tothe movements of the fleet, and not only to the status of theneighborhood.

5.2 INTEGRATING SWARMINTELLIGENCE AND CON­TROL IN THE MODEL

As described above, the model that we have designed doesnot take into account the fact that the movements of nodescan be controlled by the algorithm itself (the application canmove a UAV to a new location if required). It must thus beadapted, because flight plans are definitely an important pa­rameter in a fleet ofUAVs; they drive the mobility patterns.Two cases are to be considered:

• When the flight plan is static, the mobility pattern ofthe fleet is perfectly known. It is then possible to workout a series of graphs that describes the evolution of thesystem.

• When the FMS (see section 4) modifies the flight planin response to some external event or to a global col­laborative decision of the fleet, the situation is muchmore complex. The changes in the flight pattern impactthe application that is run by the swarm. The algorithmmust thus be adapted to the possibly new mobility pat­tern.

This last point needs further investigation. We are cur­rently working on these relationships between the flight plan,the mobility pattern and the application being run. The goalis to manage their interactions in both the model and the ap­plications. This is something new and as far as we know ithas never been modeled till now. The availability of such amodel will make it possible to guarantee that some propertiesare satisfied by the fleet when running a given mission.

6 APPLICATIONSAmong the algorithms and applications that we intend

to design/adapt to UAV fleets (from our previous work onMANets), is the Shaadhoc application [19]. It was devel­oped within the context of a former project run in collabora­tion between the LaBRI and the DGA. The goal ofthis appli­cation is to enable concurrent modifications of independentparts of a document by several users (e.g. a tactical map ina military context) equipped with mobile devices. The docu­ment is first split in parts that are distributed to the users. Inorder to be able to modify a part of the global document, auser then needs to hold the lock associated with this specificpart. The underlying lock system guarantees that a part ofthe document can only be modified by one person at a time.Locks might be passed from one user to another when theymeet on the field. At this occasion, mobile devices automat­ically synchronise their respective copies of the document.This guarantees that each device holds the most up-to-dateversion of the document according to the other devices thathave been met. The application operates in a purely peer-to­peer manner and is thus resistant to the disappearance of oneor more of its participating users (even though some parts of

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the global document might remain locked). We believe thatthis application is a good candidate to be adapted to a fleetofUAVs.

Another algorithm that we want to adapt to a fleet ofUAVsis the 'covering forest' algorithm that we have developed andvalidated using the DAGRS model [17, 18] described in sec­tion 5. Its goal is to maintain an overlay network on top ofa mobile ad hoc network, whatever the evolution of its con­nectivity. It is clear that this will be really useful in a swarmofUAVs that need to collaborate.

7 SIMULATIONFor classical and obvious safety reasons, any algorithm

and application will have to be run and tested using a simu­lator before being executed on real UAV fleets. This is com­plementary to the formal validation ofthe algorithm (see sec­tion 5).

Classical DTN simulators mainly use random mobilitymodels (such as Random Waypoint) to simulate the mo­bility of real world nodes. The situation here is different,since a UAV fleet needs to have a consistent group mobility(as stated by Claus Christmann in [20]). Indeed, most de­ployed algorithms and applications will have particular goalswhich will imply structured movements of individual UAVswithin the fleet. We thus will have to implement a simulatorthat takes flight plans into consideration. These flight plansshould be as close as possible to the real flight plans of Pa­parazzi enhanced by new directives (such as the possibilityfor the on-board decision module - FMS - to override theinitial flight plan).

To be as accurate as possible we would like to use bothhardware and software in the loop simulations. HardwareIn The Loop (HITL/HWTL) simulation is used at Boulder[21,22], at MIT [11] and at ENAC with Paparazzi. It makesit possible to test the system based on real inputs and outputs.Software In The Loop (SITL) simulation will be useful dur­ing the first test phases. This would enable to get rid ofhard­ware and to have control over simulated communications.

This simulator will also need to support inter UAVs XBee[23] ad hoc connections, XBee being the technology that wecurrently use to communicate between UAVs.

8 SECURITYIn both military and civil contexts, some if not all applica­

tions will have security requirements because the final userswill most likely need their data to remain confidential. Thisis especially true in the military context because of enemythreats. Civil applications might also need security sincethey can be used for commercial purpose and/or collect datathat should be protected for privacy reasons. In all cases anunfriendly UAV should not be able to join the fleet if it isnot allowed to. The user must know which aircraft are in hisfleet and where they are coming from.

In the considered configurations, two levels ofsecurity canbe dealt with. First, is the low level radio security. This workwhich is based on the use ofdedicated hardware componentsand a proper choice of radio frequencies is carried out incollaboration with the SigFox company [24] .

Second, is the software level security. At this level, wewill elaborate on the previous work that we have carried outat the LaBRI on the non planned establishment of securedgroups [25,26]. This makes it possible to deal with dynamiccoalitions created from individual entities that join a groupon the fly. From a technical point of view, we embed smartcards in the equipments that we want to secure. By doing so,we can have data, such as encryption keys, protected insidethe card. The card can also carry some pieces of code thatwill achieve a number ofoperations (cyphering, managementof identities, signature of messages) which, being executedinside the card, remain protected from an external observer.

It is nevertheless quite clear that smart cards have a verysmall footprint. These limitations in terms of memory andCPU make it impossible to achieve computation intensiveoperations. For instance, it will not be possible to cyphera video stream to transfer it from the UAV to the ground.To address this limitation we have begun working with themicro-electronics laboratory IMS [27] on the possibility toembed a FPGA that will be tuned to provide the proper se­curity functions depending on the application and the targetenvironment.

9 CONCLUSIONThe work presented in this paper is supported by the Uni­

versity of Bordeaux, the french army (DGA) and the Fly-n­Sense company.

We have presented our view of why fleets of UAVs area key to information superiority, in both the military andthe civil domains. We have described the platform that weare working on and its specific features among which theFlight Management System (FMS) that provides effectivecomputation and thus decision capabilities to the UAVs.This makes it possible to develop collaborative applicationsthat provide global intelligence to the swarm, withoutrequiring any ground intervention. We have introduced anew formalism to support the creation and validation ofalgorithms in this specific context. We have described anumber of applications originally developed and effectivelyrun in our previous work on MANets and we have explainedwhy we believe that it makes sens to adapt them to a fleetof UAVs. We have eventually introduced the associatedsimulation and security issues.

This work is currently in progress and we expect to runour first application on a real fleet by Q3 2009.

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