Optimal UAV Relay Positions in Multi-RateNetworks

Erlend Larsen, Lars Landmark, and Øivind KureNorwegian Defence Research Establishment (FFI)

Email: {Erlend.Larsen, Lars.Landmark, Oivind.Kure}@ffi.no

Abstract—The performance of ad hoc networks dependsgreatly on the network topology. Thus, deploying relays or con-trolling the position of network nodes may impact the perfor-mance. Unmanned Aerial Vehicles (UAVs) and other elevatedplatforms may improve the ground network performance whenused as network relays, due to better Line-of-Sight (LoS) condi-tions. In addition, UAVs can easily move to better locations. Thispaper shows that positioning a UAV asymmetrically between twoground nodes could result in better communication services thana UAV placed at the center position between the ground nodes,given the use of stepwise adaptive modulation. The IEEE 802.11WLAN standard, a popular Mobile Ad Hoc Network (MANET)protocol with multi-rate capabilities, is employed for quantifi-cation of the effect. Simulations results show that the networkperformance can be lower with a UAV at the center between two(clusters of) ground nodes for certain geometries. For networksactively supported by UAV relays, this effect must be taken intoconsideration when choosing the location and when estimatingthe available capacity, e.g., for routing or call admission.

I. INTRODUCTION

Emergency and military networks are often utilized in en-vironments where traditional infrastructure is unavailable orunusable. In such scenarios, ad hoc networking may be thepreferred network type, due to their ability to establish andmaintain communication capabilities without infrastructure.However, the performance of ad hoc networks depends on thenetwork topology. Deploying relays or controlling the positionof network nodes may, therefore, impact the performance.Elevated relays, such as Unmanned Aerial Vehicles (UAVs),may improve the ground network performance when used asnetwork relays, due to better Line-of-Sight (LoS) conditions.In addition, UAVs can swiftly move to better locations.

The intent to use UAVs for communication is widespread.For instance, Google and Facebook in 2014 separately an-nounced their intentions to establish a network of balloons [1]and drones [2] to circle in the stratosphere over specific popu-lation centers to deliver broadband connectivity. These UAVswill be solar powered and can be on station for years with-out interruptions. At the same time, autonomously controlledUAVs will facilitate low-cost operation of UAVs as networkrelays. Thus, within the military, as well as within emergencyservices, UAVs can play a significant role in improving thenetwork performance.

The channel quality decreases with increasing distance.Thus, there is a tradeoff between the communication rangeand the link speed. Many standards for ad hoc communicationsupport multi-rate communication, such as the IEEE 802.11

Fig. 1. Off-center optimal position for a UAV relay in a multi-rate network.

WLAN standard [3]. Several algorithms to dynamically choosethe optimal communication rate have been developed [4]. Theoptimal rate depends on the distance. Therefore, repositioningthe nodes may affect the optimal rate. In a single-hop system,the maximum operating distance for a particular link rate canbe easily identified. If one or both of the network nodes aremovable, they can be positioned in order to optimize the linkrate as needed. In a two-hop system, i.e., a relay system, thecorrelation between the link distance and the optimal link ratemust be considered for both links. The combination of the linkrates on the two links may lead to off-center optimal positionsfor relays. In Fig. 1, the center position would provide onlythe lowest link rate between the UAV and each of the groundnodes. By moving the UAV towards one of the ground nodes,the link to the nearer ground node is increased, without reduc-ing the link to the other ground node. Thus the total relay capa-city is increased by moving the UAV to an off-center position.

In this paper, we explore the relationship between systemthroughput and placement of a UAV acting as a communica-tion relay. We present the theoretical foundation for why asym-metric placement of the UAV can be beneficial. The analysisis extended with simulations to capture packet level effects.

The rest of the paper is structured as follows: Related workis presented in Section II. Theory on important mechanismstriggering the observed effects is given in Section III, as wellas an estimation of multihop throughput for a flow relayed bya UAV. In Section IV, the simulations setup and results arepresented. Finally, in Section V, some conclusive remarks andthoughts on further work end the paper.

II. RELATED WORK

In [5], Orfanus et al. present a self-organizing method to po-sitioning UAVs to provide persistent communication between

978-1-5090-5856-3/17/$31.00 ©2017 IEEE 8

a ground-based Wireless Sensor Network (WSN) and back-end systems.

Another approach to positioning UAVs is presented by Frewet al. in [6], where the position and mobility of helper nodesare controlled based on local information about neighboringnodes and the communication flow through the network. Basedon a continuous variability of data rate with Signal-Noise Ratio(SNR), the authors position the helper nodes to improve theperformance using Delay Tolerant Networking (DTN).

In [7], Zhan et al. evaluate, using theoretical analysis andsimulation, where to position a UAV between two groundnodes prevented from communicating due to obstacles. Theypropose a method to deploy a UAV optimally to improve thequality of communications between two obstructed AccessPoints (APs). The method pursues the optimal positions of theUAV by resorting to a min-max optimization method.

Miranda et al. [8] address positioning for mobile relays, in-vestigating four different metrics as input, i.e., Received SignalStrength (RSS), SNR, Round-Trip-Time (RTT) and transmis-sion rate, by which to steer a relay. They conclude that RSSis the better metric as it brings the relay near the barycentricposition. Why the barycentric position should be the optimalone is not discussed beyond referring to general literature.

In [9], Han et al. optimize UAV movement and locationto improve the connectivity of Mobile Ad Hoc Networks(MANETs). They develop adaptive algorithms for identifyingthe optimal UAV position and for modeling the UAV move-ment. They provide a theoretical analysis for a simple two-node one-UAV case, and show great connectivity gains throughemploying a UAV, both in the simple case and for a moregeneral network setup. However, the authors do not take intoaccount modulation thresholds.

Frank Li et al. show in [10] that high data rate commun-ication is not always the preferred solution in a MANET.They find that the network carrying capacity is dependent onthe number of hops between the source and the destination.When the hop count exceeds 4 to 6 at 11 Mbps link rate(IEEE 802.11b), it is beneficial to reduce the link rate, therebylowering the hop count. A high link data rate results in reducedtransmission range, which leads to more hops. Given equallink break probability per hop, a path holding more hops ismore error prone. Hence, there is a trade-off between thelink throughput and the network connectivity. A high networkconnectivity requires selecting a lower link data rate, at thecost of a lower rate.

In [11], Heusse et al. study a single-cell network with linksat different rates. They show that fairness in capturing the me-dia for transmission penalizes the high link rate users, limitingthe throughput of the high link rate users to the level of thelower rate.

In conclusion, to the best of our knowledge, none of theworks on positioning relays take into consideration the multi-rate properties of modern MANET technologies, and noneof the works on multi-rate communications take into consid-eration how relays may be placed to optimize the networkthroughput.

Fig. 2. Communication link rate ranges for one IEEE 802.11b node.

III. THEORY AND ANALYSIS

A. Multi-rate links

The data between a sender and a receiver are transferredas bits encoded as radio signals, e.g., Binary Phase ShiftKeying (BPSK), Quadrature Phase Shift Keying (QPSK),Complementary Code Keying (CCK), and Quadrature Ampli-tude Modulation (QAM). Higher rate encoding schemes aremore vulnerable to channel conditions, and channel condi-tions degrade with distance. Thus, as a rule of thumb, theachieved link rate decreases by distance. In other words, asthe distance between sender and receiver increases, the senderneeds to change modulation if the receiver is to be able todecode. Therefore, most modern Medium Access Control layer(MAC)-implementations allow for multiple encoding choices,resulting in multiple rates. A multi-rate MAC may offer a flex-ible method to take advantage of the best available conditionsfor packet transfer over a link.

IEEE 802.11 is the dominant technology for ad-hoc net-working. The first version [12] supported 1 Mbps and 2 Mbpslink rates, encoded using Direct Sequence Spread Spectrum(DSSS) BPSK and QPSK. Later amendments (e.g., b, g, n,and ac) have introduced higher rate modulations. The IEEE802.11b, later consolidated with other amendments into [3],specifies two higher rates 5.5 Mbps and 11 Mbps encodedusing CCK. The implementation specifics on which rate touse, and how to switch between them under which circum-stances, are not specified. Hence, the dynamic rate controlleris left to be implemented by each vendor. Several rate adaptionalgorithms have been proposed [13], such as the Auto RateFallback (ARF) [14], Adaptive Auto Rate Fallback (AARF) [4]and Minstrel [15].

The four different encoding schemes used in IEEE 802.11bresult in a practical maximum relative distance for each of themodulations as shown in Fig. 2, where the range increasesas lower rates are used. The actual ranges depend on severalvariables, e.g., transmission power, antenna gain, multi-path.A mobile node that desires to communicate with the nodein Fig. 2 could position itself at the appropriate distance toachieve the desired communication rate.

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Fig. 3. Communication link rate ranges for two IEEE 802.11b nodes.

B. Multi-hop communciation

In a network where two nodes are interconnected througha relay, there are two individually range-dependent links. Thelink rates depend on the position of the three nodes. If the relaynode is movable, it could be beneficial to choose this positionto achieve the highest end-to-end throughput. Fig. 3 shows therate ranges and overlays for two nodes that are barely beyondeach other’s lowest link rate range. The line patterns and linkrate ranges are the same as in Fig. 2. Using this figure, wesee that a centered relay node (position A) would achieve alink rate of 2 Mbps on each link. However, if the relay nodemoves a little bit towards either of the nodes (positions B),the link rate would increase to 5.5 Mbps without reducing thelink rate to the other node.

The three-node topology can be considered as one cellsince all traffic transmissions interfere with the relay. I.e., themaximum capacity of the topology is limited to the maximumcapacity of the relay node. That means that the resulting end-to-end throughput can be estimated. The time expended for atwo-hop transmission can be defined as

B

cl1+

B

cl2, (1)

where B is the amount of data and cl1 and cl2 are thecapacity of each of the links. Throughput, i.e., data dividedby time, is calculated as

BBcl1

+ Bcl2

⇒ 11cl1

+ 1cl2

. (2)

Thus, while the center position would give a maximumsignaling end-to-end throughput of 1 Mbps, moving the relaytowards one of the end-nodes would increase the throughputto 1.5 Mbps. Performing the same calculation for positionsC (Fig. 3), where the link rate for each link is 11 Mbpsand 1 Mbps, gives a resulting maximum signaling end-to-endthroughput of 0.9 Mbps.

C. When to pursue an off-center relay position

Looking beyond the IEEE 802.11b link rate steps, wecan make some generic considerations on multi-rate MACs,throughput and whether an off-center position could improve

Fig. 4. Link capacity by distance.

Fig. 5. UAV relay position cases.

the end-to-end throughput. The packet modulation must adaptto the distance between the link sender and the receiver, andthe set of available modulations is limited. Thus, the link datarate will be stepwise as a function of distance. As the distancebetween the sender and the receiver increases, the link datarate decreases in steps, comparable to a ladder (Fig. 4).

The following analysis simplifies the 802.11b example to ageneric multi-rate link technology with three modulations, c1,c2 and c3, where c1 is the highest rate with the lowest range,and c3 is the lowest rate with the highest range.

Since the steps on the rate ladder are discrete, there arethree possible scenarios for the consequence of moving therelay from the symmetric links position in the center to anasymmetric links position closer to one of the end-nodes:

• The link rate to the farther end-node is reduced, withoutthe other link improving. In this case, the optimal positionis in the center.

• The link rate to the nearer end-node is improved withoutreducing the rate to the node further away (Case 1 inFig. 5). This always increases the end-to-end throughput.

• The rate to the nearest node is improved while the rateto the one further apart is decreased (Case 2 in Fig. 5).Whether this increases the end-to-end throughput dependson the rate difference between the link rates for the twolink changes.

Case 1 will always be beneficial, given that c1 > c2. In case2, it depends whether an asymmetrical position is beneficial.

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Let us consider a section of this ladder of achievablethroughput with three possible modulations with effectivebandwidths c1, c2 and c3 (Fig. 4) for the link. We can expressthe c1 and c3 as relative to c2.

(c1 = f · c2) > c2 > (c3 = g · c2) (3)

The c1 bandwidth is possible up to a distance of d1, c2 up tod2 and c3 up to d3. When the UAV is equidistant between thesender and the receiver (in the [d1 − d2] region), the effectivecapacity on each leg of the relay is set to c2. The effectiveend-to-end throughput, due to multihop interference, is

11c2

+ 1c2

⇔ c22. (4)

If we can position the UAV so that one link is in the [0−d1]region and the other leg remains in [d1 − d2], the throughputof the relay is

11c1

+ 1c2

(5)

This location is always better than the equidistance through-put of (4). If the position of the UAV is such that one legis in the [0 − d1] region, while the other is in the [d2 − d3]region, the effect of an off-center position will vary, comparedto a center position with capacity c2

2 . The condition for apositive outcome for the off-center position is that the resultingcapacity is higher than that of the center position:

c22

<1

1fc2

+ 1gc2

. (6)

c2 can be eliminated, leaving f and g, thus

1

f+

1

g− 2 < 0. (7)

We let 1f = ∆ + g, allowing the following:

(∆ + g) +1

g− 2 < 0, (8)

which simplifies to

∆g + (1− g)2 < 0. (9)

For the expression (9) to be true, ∆ must be negative, giventhat g is positive. Thus, we can state that it is a requirement thatthe step between c1 and c2 is larger than the step between c2and c3 if an off-center position with one link with c1 capacityand the other at c3 capacity should be better than two linksat c2 capacity. I.e., the c2 step must be closer to the c3 stepthan the c1 step, using the ladder anology in Fig. 4, or moreformally

g >1

2− 1f

. (10)

In our examples based on IEEE 802.11b, the stepwisePhysical layer (PHY) rates are 11, 5.5, 2 and 1 Mbps, almost

TABLE ILINK RATE AND DISTANCE THRESHOLDS

Link rate (Mbps) dmin (m) dmax (m)

11.0 0 10005.5 1000 17002.0 1700 30001.0 3000 3800

TABLE IICALCULATED LINK COMBINATIONS AND THE RESULTING RATE

Rate l1 (Mbps) Rate l2 (Mbps) Effective throughput (kbps)

11.0 11.0 305011.0 5.5 237911.0 2.0 139011.0 1.0 7845.5 5.5 19505.5 2.0 12325.5 1.0 7312.0 2.0 9002.0 1.0 6001.0 1.0 450

a constant factor of 2. The exception is when one leg has abandwidth of 5.5 Mbps (c1), and the other leg has a bandwidthof 1 Mbps (c3). However, even with this link combination thereis a capacity reduction compared to using equidistant legs of2 Mbps (0.85 Mbps vs 1 Mbps respectively). We thereforelimit our focus to situations where the geometry implies atmost two different bandwidths when comparing the equidistantand off-center positions.

D. Estimating the end-to-end throughput

We can calculate the maximum signaling end-to-endthroughput using (2). Thus, if the link rates between the relayand each of the end-nodes in each relevant position can beestimated, the end-to-end throughput for all relay positions canalso be estimated.

The end-to-end throughput estimation depends on the posi-tions of the three nodes and the link-rate distance thresholds.There is very little literature investigating the ranges of thelink rates in 802.11b, but in [16], specific comparative rangesfor the four rates in an open indoor office environment withcubicle walls are determined to be 48, 67, 82, and 124 m forthe rates 11, 5.5, 2, and 1 Mbps respectively.

However, for outdoor communications with an elevatednode, there is little experience. Thus, we have chosen to useranges identified through simulations (Fig. 6) where the UAVmoves at an altitude of 2500 ft (762 m) and the Friis free spacepath loss model1 is used. The simulation setup is as describedin Section IV-A. The link-rate ranges used for estimating themaximum end-to-end throughput are listed in Table I.

1The two-ray-ground path-loss model is the configured model, but due tothe altitude difference between the nodes, the simulator actually employs theFriis free space path loss model between the UAV and the ground nodes.

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Fig. 6. Link throughput between the UAV and each ground node. UAV altitude2500 feet (762 m).

Fig. 7. Throughput by UAV X-axis and Z-axis position. Ground nodesseparation 4000 m.

From (2), we get the maximum signaling end-to-endthroughput. This throughput is not achievable for any end-system, as it includes overhead for multiple access and lowerlayer headers. However, using work by Jun et al.[17] wecan estimate the resulting effective throughput as 6100, 3900,1800, and 900 kbps for 11, 5.5, 2, and 1 Mbps, respec-tively. Thus, we get a resulting expected maximum end-to-endthroughput for each link-combination as shown in Table II.

The algorithm for estimating the expected performance foreach position is as follows:

1) Based on the node positions, calculate the distance ofthe two links L between the ground nodes and the UAV.

2) For each L, estimate the link-rate (Table I).3) Based on empirical link-rate combination results (Ta-

ble II), estimate the expected throughput.

Fig. 7 shows the estimated end-to-end throughput for a flowbetween two ground nodes with a separation of 4000 m, whereone ground node is at position 0 on the X-axis, and the otheris at position 4000. The Z-axis is the altitude of the relay. Ifwe were to position an elevated relay based on this estimation,there are two off-center ”hot-spots” around 1400 and 2700 mon the X-axis up to an altitude of nearly 4000 ft (1219 m). Ataltitudes over 4000 ft (1219 m), the center position is the bestposition, although the UAV can move significantly towards oneof the sides without causing lower end-to-end throughput. As

TABLE IIISIMULATION PARAMETER SETTINGS

Parameter Setting

Propagation model Two-ray groundCommunication frequency 2.4 GHzGround nodes antenna altitude 2 mPHY data rates 11, 5.5, 2 and 1 Mbps

MAC protocol IEEE 802.11 with AARF Rate controlRTS/CTS threshold 2347 and 1

Traffic type UDP, Constant Bit RateData packet size 1500 bytes

Simulation time 300 sTraffic start 50 sTraffic stop 250 s

Number of runs per data point 10Confidence interval 95%

the altitude increases beyond 4000 ft (1219 m), the optimalarea narrows down, and at altitudes above 7300 ft (2228 m),the off-center areas are again performing better than the centerposition.

The information presented in Fig. 7 shows that the altitudeof the relay, as well as the horizontal axis position, affects theresulting performance. If we were to estimate the throughputover the x–y plane, the results should resemble the Fig. 3, withA yielding lower throughput than B, and C marginally lowerthan A.

IV. RESULTS

A. Simulations settings

Simulations have been performed using the network simula-tor ns-3.25 [18] to investigate the capacity impact of the UAVposition. The participating nodes use an adaptive-rate MAC todynamically adjust packet modulation, given the propagationconditions. The propagation environment is modeled using theTwo-ray ground [19] path-loss model, but for communicationbetween the elevated node and the ground nodes, the sim-ulator resorts to employing the Friis free space propagationloss model [20], due to limitations with regards to the two-ray-ground path-loss model. Preliminary simulations usingother propagation models available in ns-3, e.g., the ITU-R1411 [21], showed the same effecs as presented in this paper,but to avoid unnecessary focus on the propagation model,the ns-3 simulator was configured to use the Two-ray groundmodel. The scenario used in the simulations consists of twoground nodes and one UAV.

The default settings of ns-3.25 have been used in the sim-ulations if nothing else is specified. Simulation parametersare also listed in Table III. All results are presented with a95% confidence interval based on 10 runs per data point. Thesimulations have been run with 10 different run numbers (ns-3 parameter). However, due to the static topology and trafficflows, the confidence intervals are very small. Static routingwas set up between the three nodes, to ensure that the routeswould prevail during congestion. The routing tables forced the

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ground nodes to use the UAV as a relay to communicate witheach other, and the UAV had direct routes to the two groundnodes.

The simulations were run on a scenario using the sameparameters as the theoretical throughput estimation in Sec-tion III-D. The scenario thus consisted of two ground nodesand one UAV. The two ground nodes were placed out of com-munication range of each other, with a separation of 4000 me-ters. The UAV was placed in various positions on the X-axisand the Z-axis to evaluate the effect of the UAV’s position onthe network performance. The nodes were stationary duringeach simulation. The traffic was generated by Node 0 andreceived by Node 1 after retransmission by the UAV.

B. Link performance

The performance of each of the links between the UAV andthe ground nodes is essential to understanding the resultingflow throughput from Node 0 to Node 1 over the UAV relay.Therefore, we examine the link behavior between the groundnodes and the UAV flying at 2500 ft (762 m) with varyingUAV X-axis positions. The links are individually loaded tocongestion, to examine how the link throughput varies withthe X-axis position of the UAV. The direction of the traffic isthe same as for the end-to-end flow from Node 0 to Node 1.

As the UAV moves along the X-axis, the throughput (Fig. 6)for the Node0-UAV link is reduced stepwise from just above6 Mbps, via 3.8 Mbps and 1.8 Mbps to 600 kbps. The AARFregulates the link rate as the UAV moves away from the groundnode. The increased distance deteriorates the link between theUAV and the ground node, causing a stepwise reduction of thethroughput. The UAV–Node 1 link performance (Fig. 6, UAV-Node1) is a mirrored version of the link Node0-UAV.

The steps in Fig. 6 correlate directly with the PHY rate. Thehighest step at a throughput of 6 Mbps correlates to 11 MbpsPHY rate, at a ratio of 0.55 between the PHY bitrate andthe measured throughput rate for UDP payload. Next follows5.5 Mbps, 2 Mbps, and 1 Mbps. The link rates between theUAV and each of the two ground nodes are the same at thecenter, but the rates differ considerably when moving just300 m to either side.

In a multihop network, the capacity is reduced proportion-ally with the number of active participants as only one nodecan access the shared medium at any time [22]. In a line ofthree nodes, only one can transmit at any time. A packet flowbetween the two end-nodes requires two transmissions perpacket. Thus, two links at 2 Mbps will only provide an end-to-end throughput comparable to a one-hop flow transferredover a 1 Mbps link.

From Fig. 6 we see that the throughput per link when theUAV is in the center between the two ground nodes is 2 Mbps.Using these two links the end-to-end throughput would behalf of this, due to the interfering retransmission by the UAV.When accounting for overhead, it would be around 800 kbps.However, there are two areas to the left and right of the center,where one link is 2 Mbps, and the other is 5.5 Mbps. In the

Fig. 8. End-to-end throughput by UAV X-axis position at various UAValtitudes.

Fig. 9. Two-way end-to-end throughput by UAV X-axis position with RTS-CTS enabled. UAV altitude 2500 ft, ground nodes separation 4000 m.

next subsection, the potential effect of this on the end-to-endthroughput is addressed.

C. End-to-end throughput

The end-to-end throughput (Fig. 8) for a UAV relay atdifferent altitudes depends on the altitude of the UAV and theposition. At 2500 ft, the center is a sub-optimal position forrelaying a traffic flow from Node 0 to Node 1. Instead, thereis a throughput increase by positioning the UAV off-center,where the links between the UAV and each of the groundnodes have different rate performance. There are two peakswhere the throughput is around 1150 kbps, at the positions1400 and 2600 m. It is considerably lower at the center pointbetween the two ground nodes (2000 m), at about 850 kbps.This corresponds well with the expected throughput reductiondue to multihop. Thus, moving the relay away from the centerposition may give a 35% throughput increase. For the UAValtitudes up to 4000 ft, the off-center optimums are present,but at higher altitudes, the effect fades (4500 ft) and disappearsat 5000 ft (1524 m).

D. Two-way transmissions

The effect is still present when the UAV relays one trafficflow in each direction between the two ground nodes. Fig. 9shows the total throughput when there are two flows in thenetwork, one from Node 0 to Node 1, and one the other way

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from Node 1 to Node 0. Note that the Request-To-Send (RTS)-Clear-To-Send (CTS) mechanism had to be enabled to avoiddetrimental collisions due to a hidden node effect between thetwo ground nodes, creating collisions at the relay node. Sincethe ground nodes are beyond each other’s sensing range, theycannot detect the other ground node’s ongoing transmission.Thus, they transmit with a much higher probability of colli-sions than if they had been inside each other’s sensing range.

V. CONCLUSIONS AND FUTURE WORK

In this paper, we have investigated the optimal relay positionin a multi-rate communication system, both through theoreticalanalysis and simulations. The theoretical analysis showed that,depending on the distance between the ground nodes and thealtitude of the relay, there could be better positions than acenter position to achieve maximum throughput. As aspectssuch as interference and collisions are hard to study in suchan analysis, simulations were performed to investigate thesubject more thoroughly. The simulation results confirmed ouranalysis, showing a situation where positioning a relay closerto one of the ground nodes gave 300 kbps (35%) higher end-to-end throughput, compared to the center position.

The altitude of the relay and the horizontal separation be-tween the ground nodes were shown to have great influenceon the variations in end-to-end throughput with varying relayposition.

Future work includes addressing the case of multiple (> 2)ground nodes. The complexity of positioning a relay increasesgreatly with a higher number of ground nodes than two. Trafficpatterns and traffic amount, as well as flow priority and therequirement for fairness, could have a great impact on the op-timal position. The simulation results should also be validatedon real hardware, to show that the effect can be obtained andexploited in real life.

ACKNOWLEDGMENT

This work is part of the project Coalition Networks forSecure Information Sharing (CoNSIS) II. The authors wouldlike to thank Dr. Svein Haavik and Dr. Bjørnar Libæk for theirvaluable feedback during the work with this paper.

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