Phase Transitions for Controlled Mobility in

AIAA 2006-6464

AIAA Guidance, Navigation, and Control Conference and Exhibit 21 - 24 August 2006, Keystone, Colorado

Phase Transitions for Controlled Mobility in Wireless Ad hoc Networks Cory Dixon * , Daniel Henkel † , Eric W. Frew ‡ , and Timothy X. Brown § University of Colorado at Boulder, Boulder, CO., 80309 In this paper we investigate the phase transitions between different modes of controlled mobility in wireless ad hoc networks. The transmission of data between two nodes can be performed by one of three methods in a mobile ad hoc network: direct transmission between nodes; multi-hop relaying through intermediate nodes; and data ferrying through a node that physically moves between sources and destinations. Assuming the source and destination nodes are stationary, the best choice of transmission mode through the network is a function of several variables including the separation distance between nodes, the required average data throughput, the maximum tolerable delay, and the data ferry speed and buffer capacity. This paper presents the notion of a phase diagram relating separation distance to average data throughput. Contours of maximum packet delay are specified on this phase plot and are used to identify the optimal mode under various configurations. The creation and maintenance of communication channels of specified performance in a mobile ad hoc network can be viewed as a hybrid control problem. Calculation of the boundaries between phases corresponds to determining the transition conditions of the hybrid controller.

Nomenclature D d B P K ε R SNR C W τ T M V

C

= = = = = = = = = = = = = =

source and destination separation distance distance between any two communicating nodes buffer size packet size RF power coefficient RF power loss exponent long term average communication rate signal-to-noise ratio Shannon channel capacity (instantaneous communication rate) Shannon bandwidth coefficient time delay for delivery of a packet required throughput hybrid control mode ferry speed

I.

Introduction

OMMUNICATION networks between and through aerial vehicles are a mainstay of current battlefield communications1. Small low-cost Commercial Off-The-Shelf (COTS) radio equipment combined with powerful computer processing can be mounted on small (~10 kg) Unmanned Aircraft (UA) and has the potential to revolutionize battlefield communication and open up many scientific and commercial applications. One example COTS technology is the IEEE 802.11b wireless LANs (so called WiFi) which connect wireless mobile nodes to a fixed infrastructure and is being widely deployed, including in UAS applications (Fig. 1).2-6 More interesting

*

PhD Candidate, Aerospace Engineering, 429 UCB, AIAA Member PhD Candidate, Interdisciplinary Telecommunications Program, 530 UCB ‡ Assistant Professor, Aerospace Engineering, 429 UCB, AIAA Member § Associate Professor, Interdisciplinary Telecommunications Program, 530 UCB †

1 American Institute of Aeronautics and Astronautics Copyright © 2006 by Cory Dixon. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

(a)

(b)

(c)

(d)

Figure 1. Ad hoc UAS Ground Network (AUGNet) combining COTS networking technology with small UAS’s: (a) aerial view of the Table Mountain Field Site, (b) ladder-mounted ground node, (c) automobile mounted node, (d) airborne nodes mounted inside three Ares UA.

applications are possible when the different mobile nodes connect to each other in peer-to-peer ad hoc meshed wireless networks.7,8 As the applications of UAS expand, the principal issues of communication technologies are flexibility, adaptability, and controllability of the information/data flows. Future systems will be net-centric and rely on mobile ad hoc networking to provide real-time connectivity among cooperating agents and to provide backhaul of sensor and telemetry data from mobile nodes to a data repository or command center. Two motivating scenarios are shown in Fig. 2 . In scenario (a), an ad hoc network of ground nodes is initially disconnected because of distance and/or terrain. A UAS is then introduced, its altitude provides a superior “view” of the nodes and it maintains connectivity as an ad hoc relay. In scenario (b), the power and payload constraints of the small UA limit the communication range, which limits operational range. Relaying messages in an ad hoc network between multiple UA extends the communication range and thus the operational range. Recent experiments have demonstrated that the performance of such mobile ad-hoc networks (MANETs) of airborne nodes is highly sensitive to vehicle dynamics and local interference and that the network becomes stressed or fractured more easily than expected.6 These experiments indicate that stressed or fractured network behavior is fundamental to airborne MANETs and therefore new concepts such as the role of controlled mobility must be explored. (a)

UA

(b)

Group 2

Group 1 Radio Node

Operations Center

Figure 2. AUGNet deployment scenarios, (a) Scenario 1: UA used to increase ad hoc ground node connectivity and connects disconnected groups, (b) Scenario 2: ad hoc networking between UA increases operational range.

2 American Institute of Aeronautics and Astronautics

Controlled mobility in ad hoc networks allows nodes to reposition themselves in order to optimize data flow through the network.9,10 The establishment and maintenance of communication channels in a mobile ad hoc network then becomes a cooperative control problem where communication performance is the objective function. The interaction between networked communication and node mobility can be viewed as a closed loop system in which the status of the communication network determines the performance and stability of the multi-agent system whose motion in turn changes the performance of the communication network (Fig. 3). Both sides of this loop have received attention in different communities. The cooperative control field Mobility typically assumes an imposed network, models its Control performance, and studies the stability of the resulting system. Formal study of distributed cooperative control, swarm intelligence, networked control systems, and large interconnected systems rely on this approach. Likewise, the Delay-tolerant networks, Distributed cooperative Data ferrying, mobile ad hoc networking community has embraced node control, Mobile infostations, mobility within the concepts of delay-tolerant networking, Swarm intelligence, Helper nodes, Large interconnected data ferrying, mobile infostations, and fault tolerance. Fault tolerance. systems. The controlled mobility of network nodes is an essential component of a new class of networking referred Wireless to as delay tolerant networking (DTN). Node mobility Networked enables message passing in partially-connected networks Communication where standard ad- hoc networking (in which wireless nodes cooperate to relay packets over multiple relay hops Figure 3. Joint analysis and design of communication and from source to destination8) fails. Data is passed between control systems. nodes that come into close contact with one another and a data packet eventually reaches its destination. Such protocols are well suited to networks that are sparse, dynamic, and can tolerate end-to-end transmission delays in the data Mobility-based data packet delivery is known to be very effective when long delays can be tolerated11. Approaches that consider node movement is given typically assume nodes have similar capabilities, nodes move in random patterns relative to one another over time, and the communication is separated from the other activity of the node.12-15 The main advantages of these approaches are their ability to overcome network partitioning due to sparse numbers of nodes and reduced network congestion, since data is physically carried rather than transmitted multiple times through relays. Depending on the transmission protocols, mobility can also save overall power in the network. The main drawbacks of these approaches are their reliance on chance encounters to relay data between moving nodes and scalability issues due to their inherent flooding nature. A second class of protocols exploits known, periodic, or non-random mobility in subsets of nodes. The use of unmanned aircraft for ad hoc networking in battlefield networks exploits the coordinated, hierarchical organization of moving troop units.16-18 Coordinated flocking by the UAV relays improves the coverage and load balancing of these types of networks.19 The notion of general support nodes described in Ref. 20 where sub-protocols dictate the motion of dedicated relay nodes is shown to improve basic message passing, especially in highly dynamic environments. The concept of data ferrying also uses non-random movement to form efficient ferry routes that minimize delay and support bandwidth requirements for a group of nodes with known locations and known communication requirements.21 A handful of works address communication-reactive control strategies for ad hoc networks that go beyond simple position-based constraints. Reference 22 is one of the first works to address deliberate trajectory changes to make message passing between two communicating nodes possible. Movement control for fault tolerance was investigated in Ref. 23 by moving a subset of nodes to new locations in order to achieve biconnectivity in the network graph. Repositioning of a base station and trajectory design of mobile sinks and relays enhances the performance of energy-constrained sensor networks by extending their Figure 4. Three modes of maintaining a communication link lifetime.24-26 A geometric connectivity robustness metric has between two static nodes A and B. been developed that provides a means to consider motion 3 American Institute of Aeronautics and Astronautics

constraints imposed by (range-based) connectivity requirements.27 Finally, Ref. 28 presents a self-adaptive distributed feedback control scheme to obtain desirable network properties such as connectivity and power efficiency. Although the goal is to optimize these network parameters, the performance metrics are transformed into position constraints based on a basic energy cost function. Given the presence of node mobility in an ad hoc network, transmission of data between a source and destination can take three forms (Fig. 4). Direct communication occurs when two nodes transmit data directly to one another. The Shannon-Hartley law describes the maximum data rate possible for a single direct transmission based on the signal to noise ratio of the signal. Relaying occurs when additional nodes are used to receive a transmission from source and retransmit it to a destination. Finally, data ferrying occurs when a mobile node physically stores and carries data from one location to another. These three methods form the modes of a hybrid controller for node mobility. The main contribution of this paper is the description of a phase diagram that describes the conditions for optimal performance by each transmission method. Calculation of the borders of the different regions in this phase diagram corresponds to the transitions in the hybrid controller. The remainder of this paper is organized as follows. Section II describes in more detail the three modes of the hybrid controller. Section III formulates the main problem addressed in this paper – calculation of the different phase regions. Section IV presents the main results of the paper and Section V concludes the paper.

II.

Hybrid Control Modes

The transmission of data between two static nodes can be performed by one of three methods in a mobile ad hoc network: direct transmission between nodes; multi-hop relaying through intermediate nodes; and data ferrying through a node that physically moves between sources and destinations (Fig. 5). For this paper, a single link is considered between nodes A and B with a third ferry/chaining node designated as C.

Relay

Direct

Ferry

Figure 5. Hybrid controller for node mobility in an ad-hoc A. Direct network. One mode of communication between two nodes A and B is to establish a direct wireless connection between the two nodes. In this approach when communication distance is long, the data rate is low; when communication distance is short, data rate is high. This is because of the inverse relationship of distance and signal power to noise power ratio (SNR), which governs wireless communication and is described by the Shannon- Hartley Law.29 For any two wireless nodes A and B, let d represent the distance between nodes A and B. Then the power received at node A from B’s transmission can be modeled by the standard RF exponential decay model given as

P (d ) =

Ko dε

(1)

where ε ≥ 2 is the signal decay exponent (ε = 2 is the ideal propagation model in free space) and Ko represents the gain of the link and is dependent upon the transmitter power, gain patterns of each antenna, the antenna orientations, and the quality of the radio electronics used by each node but is modeled as a constant in this paper. The signal-tonoise ratio is then defined as SNR =

P(d ) Ko K = = ε No Nod ε d

(2)

where No is the noise power level of the receiver and in general is a function of the environment and quality of the electronics, modeled as a constant for this paper and can be experimentally determined. The well-known Shannon-Hartley theorem states that a wireless communications channel has a maximum rate at which information can be cleanly transmitted and is given as C = W log 2 (1 + SNR )


(3)

where C is the Shannon capacity in bits per second, W is the bandwidth of the channel in hertz, and SNR is the signal-to-noise ratio of the communication signal expressed as a straight power ratio (not as decibels). Due to this relationship between capacity and SNR, the direct connection approach is feasible only when the separation distance between the nodes is relatively short. Depending on channel coding, actual data rates can be pushed very close to this theoretical maximum. The assumption of a wireless propagation model that links distance and SNR in the form of Eq. 2 leads to a relationship between data rate R and distance d. k ⎞ ⎛ R (d ) = W ⋅ log 2 ⎜1 + ε ⎟ d ⎝ ⎠

(4)

Finally, the delay in delivering a packet of size P from A to B is assumed to be only a function of the insertion time, i.e. propagation delay and channel access delay are assumed to be small as compared to insertion delay. For direct communication, the time delay is given as:

τ (d ) =

P R(d )

(5)

Thus in the direct mode of communication, the available data rate and time delays are driven explicitly by the SNR, which is taken to be a function of distance only for this paper.

Y-Position [m]

B. Static Relaying As the distance between nodes A and B UAV Location over Time 4000 increases, and the SNR declines, there eventually will be a point in which a higher link throughput can 3500 25 25 be achieved by introducing nodes into the communication link as static relays. In this mode, 3000 30 the goal of the mobility control algorithm is to move 40 2500 the relay node to a location in the environment that 40 maximizes the minimum link throughput. 2000 In Ref. 30, the authors form a linked chain of 35 1500 unmanned aircraft (UAs) to increase the communication range to a remote ground station by 35 1000 utilizing multi-hop network communications along 25 the chain. In the paper, a decentralized control 500 30 25 scheme for unmanned aircraft is presented that uses 0 the SNR of each link as input into the controller to 0 500 1000 1500 2000 2500 3000 3500 4000 X-Position [m] achieve a chaining affect by controlling an orbit center-point in the direction of the gradient of the Fig. 6. Results of running leashed chain controller from Ref. [21] for two static radio nodes and two UAs (orbit cent point is dashed SNR field for the link with the smallest SNR. line, actual UA path is blue line). Figure 6 shows results of running the controllers for two static nodes with no noise source present (for clarity of the contour lines). The dashed line represents the orbit center point and the solid blue line is the actual path of the UA. While the paper is applied to the specific application of UA, the control scheme presented can be applied to any vehicle that utilizes wireless communications and where the dynamics can be modeled by kinematic equations; such as bicycle, unicycle, or a point mass.30 The motivation for using the SNR as the controlled value is seen from Eq. 3 since,

max min Ci ⇔ max min SNRi . i∈N

i∈N

(6)

and the fact that the SNR (and its gradient) can be measured locally for each link. The most significant benefit is that the controller is able to adapt to localized noise sources as opposed to position-based controllers. When localized noise sources are present, a position-based controller will not be able to react to the noise source and thus will not maintain the performance of the communication link. 5 American Institute of Aeronautics and Astronautics

When the SNRs of the individual links of a two-node system with one relay is balanced by the controller as in Ref. 30, then the throughput can be given as RR ( D) =

R ( D / 2) 2

(7)

where D represents the separation distance of nodes A and B and the direct communication distance between A and the relay or B and the relay is d=D/2. The time delay to deliver a packet for the three-node chain is again only taken to be the sum of the insertion time of a packet of size P on each hop and is given as

τ R ( D) =

2P R( D / 2)

(8)

C. Ferrying Another mode of communication arises from the shortcomings of the “Direct” and the “Relay” models. Both only work well when separation distances are relatively small so a high data rate can be achieved. It can be shown that physically transporting data from one node to the other can dramatically improve data rates while at the same time improve robustness against interference.10 The agents carrying the data are called helper nodes or ferries and the mode is termed “Ferrying.”31 In research for the AUGNet project at the University of Colorado6 ferries are small, fast-moving airplanes. Controlling the movement of these airplanes such that their physical carrying of application data improves overall network performance is the pivotal area that is addressed in this research. The problem with Ferrying is that communication delays are negatively affected by the long transit delay of ferries along a connecting path between the two communicators. Two main ferrying models are distinguished: the Chain-Relay Model and the Conveyor Belt Model. In the chainrelay model (Fig. 7) it is assumed that all participating ferries are distributed along a connecting path from source to destination, forming a chain. The source passes available data on to the first ferry, which physically transports it some way along the path, hands it off to the next ferry, and eventually returns to the source. This hand-off procedure is repeated until the last ferry hands the data to the destination. The interference range is assumed equal to the communication range. In the conveyor belt model (Fig. 8), it is assumed that every ferry travels the complete distance from source to destination, then returns to the source and repeats the movements in a circuit. The two movements do not have to occur along the same path. Multiple ferries are sharing the same path, which increases throughput and robustness of the system. Each of these models has advantages depending on parameters such as number of task nodes and ferries, ferry speed, buffer size, and transmission rates. Fully characterizing these parameters is one of the main tasks of our research. l

S

F1

l F2

movement

movement

F3

D

S

movement

D

movement movement

Figure 7. Chain relay model where a helper node passes data to the next helper node closest to the destination.

F2

F1 F3

F4 l

Figure 8. Conveyor belt model where each helper makes the trip from source to destination node.

Making the helper assignments can be addressed through a number of mechanisms. If the communication requirements are well defined and known into the future, then they can be assigned using linear programming. For instance, given two task nodes both communicating over a long range to a third node; the helper needs to divide its time relaying or ferrying traffic between the two traffic streams. This can be framed as a simple linear program where the average delay is minimized subject to meeting traffic flow-rate requirements. The linear program specifies to the control algorithm the broad task of the helper, but in turn, depends on how long the control algorithm requires to relay traffic and to switch between traffic streams. 6 American Institute of Aeronautics and Astronautics

In a simple two-node system with only one ferry, the two ferrying models reduce to only one mode of operation. In this mode, the long-term throughput is given as10

R F ( D, V , B ) =

BV 2D

1 −

1

= R R ( D) +

⎛ B(ε − 1)V ⎞ ε −1 ⎟⎟ 1 − ⎜⎜1 + ⎝ 2 R R ( D) D ⎠

BV 2D

(9)

where the second equality holds when ε = 2. The cycle time, which is taken to be the packet delay, follows as 1 ⎞ ⎛ − B (ε − 1)V ⎞ ε −1 ⎟ 2D ⎜ ⎛ B ⎟ ⎜1 − ⎜1 + ⎟= τ ( D, V , B ) = V ⎜ ⎜⎝ 2 R R ( D) D ⎟⎠ ⎟⎟ R ( D) + BV ⎜ R ⎝ ⎠ 2D

(10)

where the second equality holds when ε = 2. Equation 9 show that the increase in link capacity is only bounded by the limitations of the vehicle in terms of its speed and buffer size, but does come at the cost of increasing delay.

III.

Mode Switching & Boundary Conditions

Assuming the source and destination nodes are stationary, the best choice of a communication link mode through the network is a function of several variables including the separation distance between nodes, the required average data throughput, the maximum tolerable delay, the data ferry speed and buffer capacity, and finally on the radio performance capabilities. For a given required data rate and link distance there may be multiple modes that work, thus another criterion is required. As an example, the use of the resources of the relay/ferry node is minimized leading to the use of the direct link when possible, then the relay and finally ferrying since ferrying requires fuel energy to continuously move the vehicle. Another criterion is to minimize the time delay for the delivery of an atomic packet. In this case, the mode selection is not as obvious and boundary conditions that are functions of distance and throughput are required. For this paper, two communication link objectives are discussed: long-term throughput and the time delay for the delivery of a single packet. The problem addressed by this paper is the selection of a control mode that minimizes the packet delay while achieving a desired throughput at some distance; given the speed and buffer size of the ferry, and radio parameters. A. Switching Between Direct and Relay Modes The boundary condition that dictates the transition from direct communication to static relay, when considering throughput only, is when the direct link has half the link capacity of the single hop from A to C (which is at half the distance as suggested Fig. 4). This condition can be written as RD ≤ RR 2 R ( D) ≤ R ( D / 2)

(11)

ε 2 Log 2 ⎡⎢1 + K ε ⎤⎥ ≤ Log 2 ⎡⎢1 + K 2 ε ⎤⎥ D ⎦ D ⎦ ⎣ ⎣

where the boundary point is found to be 1/ ε

⎛ K ⎞ D=⎜ ε ⎟ ⎝2 −2⎠

(12)

Equation 12 is a function of the radio parameters K and ε only. Thus, only at separation distances greater than that given in Eq. 12 should relaying be considered to increase throughput.


When considering packet time delay for chaining as compared to direct communication, the switching condition is given as

τR ≤τD 2P P ≤ R ( D / 2) R ( D )

(13)

This corresponds to the same condition as in (12). Therefore, Eq. 11 and 13 define the mode switching condition between the relay and direct modes which is at the distance defined in (12). It should be noted that both conditions are a function of the separation distance for a given throughput. B. Switching Between Direct and Ferrying Modes In the case of switching from the direct communication mode to the ferrying mode, the switching condition is based on required link capacity as a function of distance. At short distances, if the direct communication mode can sustain the required capacity, then this mode offers the smallest time delay (by a significant amount). However, once the demand exceeds the link capacity for a given distance for either the direct relay modes then ferrying is the only option that can be used to increase the link capacity, however it comes at the cost of a significant increase in the time delay. From Eq. 9, the increase in link capacity is only bounded the limitations of the vehicle in terms of its speed and buffer size. Thus, the condition for switching between direct and ferrying is simply when the direct method can no longer sustain the required communication rate T. T > Rd

(14)

At longer distances that are greater than the distance in Eq. (12), the relay mode dominates the direct mode. C. Switching Between Relay and Ferrying Modes When considering using ferrying over relaying, the throughput boundary condition for moving from relaying to ferrying is stated as RF ≤ RR

(15)

where RF is given in Eq. 9 above and it is pointed out that this region is, at minimum also, bounded by the minimum separation distance given in Eq. 12. This is because there is a minimum range in which static relaying can improve communication performance, and the region above this must be ferrying. Thus there will be a “triple-point” of the three modes. Since the direct method will have the shortest time delay at the triple point, the direct mode will be chosen in this instance. The time delay condition are similar to that above and is

τF ≤τ R

(16)

which due to the dependence upon the radio environment and ferry capabilities does not lend itself to an analytic solution. However, the conditions can be readily solved with numerical methods.

IV.

Hybrid Control Mode Phase Diagram

This paper presents the notion of a phase diagram, relating separation distance to average data throughput, as the method of determining the switching conditions for the hybrid mode controller where calculation of the boundaries between phases corresponds to determining the transition conditions of the hybrid controller. This is because the equations above do not lead to set points within the distance-throughput space but lead to functions of throughput and distance that define the mode switching conditions and thus lead to a phase diagram. Since some of the boundary conditions do not lend themselves to analytic expressions, a phase plot allows for the numerical calculation of the boundary conditions to determine the phase shapes. It should be noted by the reader that the shape of the phase plot is significantly affected by our choice to minimize delay for some throughput and distance, which 8 American Institute of Aeronautics and Astronautics

is effectively a slice in the phase space. If instead the goal was to find the available throughputs at a given time delay and distance, then a different orthogonal slice of the phase space would be generated.

8

10

Unachievable 6

data rate [bps]

10

4

10

Ferry

Direct

2

10

Relay 0

10 1 10

2

10

3

10

4

10 distance[m]

5

10

6

10

Figure 9: Hybrid control mode phase regions and transition boundaries.

As an example, consider a radio link that has a rate of 1 Mbps at 1 km separation, 100 Mbps at 1 m separation, and the path loss exponent is 3 (i.e., W = 3.6 MHz, k = 2.1x109, and ε = 3). The bottom curve in Fig. 9 shows the obtainable rates as a function of distance. The allowable rate decreases from 1 Mbps at 1 km to 1 bps at 100 km. If a relay node is added at the midpoint, then the obtainable rates are given by the second curve. Below about 300m, relaying does not add to the obtainable rate since the rate achieved by relaying through two shorter links is less than twice the direct link rate. Introducing a ferry node that can move at 50 m/s and has a 50 MB buffer, the achievable data rates are further improved as shown in the upper curve in Fig. 9. Decreasing either buffer size or ferry velocity decreases the obtainable ferry rates. For very small values the ferry throughput curve follows the relay throughput curve until large distances are reached. Rates above the ferry curve are unachievable without increasing the buffer or velocity. Assuming unbounded buffer size or velocity, any rate can be achieved. Examining 25km, the link can sustain a data rate of 70 bps using a direct link, 280 bps using a relay link and 400,000 bps using a ferry link. The ferry in this example has much greater throughput suggesting that it should always be used. However, ferrying might not be able to meet the delay constraint of a given application. Consider a packet of length P in bits. For the direct link, the time to send this packet is dominated by the insertion time, P/R(D). For the relay link, the packet must be sent twice and so the delay is two insertion times at the smaller distance, 2P/R(D/2). For the ferry link, the delay is the time it takes the ferry to complete its ferry circuit. For the example above, this time is very close to the time required to fly from source to sender and back, 2D/V. Using the boundary conditions derived in Section III, a packet of size P = 1000 bits and a given distance D = 25km, these result in 14sec for direct, 3.5sec for relay and 1000sec for ferrying. Relaying yields lower delay according to Eq. 12., whereas the ferry delay is greater than both of the other delays. However, in extreme distances (above 400km) the ferry has lower delay. Therefore, given the above criterion we would divide the distance data rate space into the control mode regions as labeled on the graph. 9 American Institute of Aeronautics and Astronautics

V.

Conclusion

In this paper the a phase diagram that relates separation distance to average data throughput is presented as the method of determining the switching conditions for a communications based hybrid mode controller. The three modes of a communication link that have been presented in this paper are direct, static relays, and ferrying. With in each communication mode there is a different sub-controller that achieves the goals of the mode. A phase plot is generated for an example radio highlighting the different boundary regions generated from the mode switching conditions presented in Section III and can be used to determine the switching conditions of the hybrid mode controller.

References 1

Office of the Secretary of Defense, Unmanned Aircraft Systems Roadmap, 2005-2030., 2005. 2 Schulze, K., Buescher, J., “A Scalable, Economic Autonomous Flight Control and Guidance Package for UAVs,” 2nd AIAA "Unmanned Unlimited" Systems, Technologies, and Operations—Aerospace, 15-18 September 2003, San Diego, California. 3 D. Henkel, C. Dixon, J. Elston, T. X Brown, “A Reliable Sensor Data Collection Network Using Unmanned Aircraft.” in Proc. Second International Workshop on Multi-hop Ad Hoc Networks (REALMAN), Florence, May 26, 2006. 4 T. X Brown, S. Doshi, S. Jadhav, D. Henkel, R.-G. Thekkekunnel, “A Full-Scale Wireless Ad Hoc Network Test Bed.” in Proc. of International Symposium on Advanced Radio Technologies, Boulder, CO, March 1-3, 2005. 5 T. X Brown, S. Doshi, S. Jadhav, J. Himmelstein, “Test Bed for a Wireless Network on Small UAVs.” in Proc. AIAA 3rd ``Unmanned Unlimited'' Technical Conference, Chicago, IL, 20-23 Sep 2004. 6 T. X Brown, B. Argrow, S. Doshi, R.-G. Thekkekunnel, D. Henkel, “Ad Hoc UAV Ground Network (AUGNet).” in Proc. AIAA 3rd ``Unmanned Unlimited'' Technical Conference, Chicago, IL, 20-23 Sep 2004. 7 Toh, C.-K., Ad Hoc Mobile Wireless Networks: Protocols and Systems, Prentice Hall, 336p., 2001. 8 C. Elliott and B. Heile, "Self-Organizing, Self-Healing Wireless Networks," Proceedings of the 2000 IEEE International Conference on Personal Wireless Communications, Dec 17-Dec 20 2000, Hyderabad, India, 2000. 9 E. W. Frew, T. X. Brown, C. Dixon, and D. Henkel. “Establishment and Maintenance of a Delay Tolerant Network through Decentralized Mobility Control.” IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, April 2006. 10 T. X Brown, D. Henkel, “On Controlled Node Mobility in Delay-Tolerant Networks of Unmanned Aerial Vehicles.” in Proc. of International Symposium on Advanced Radio Technologies, Boulder, CO, March 7-9, 2006. 11 Z. D. Chen, D. Vlah, and H. T. Kung, "Ad Hoc Relay Wireless Networks over Moving Vehicles on Highways," Proceedings of the 2001 ACM International Symposium on Mobile Ad Hoc Networking and Computing: MobiHoc 2001, Oct 4-5 2001, Long Beach, CA, United States, 2001. 12 D. Cavalcanti, D. Sadok, and J. Kelner, "Mobile Infostations: A Paradigm for Wireless Data Communications," Proceedings of IASTED International Conference on Wireless and Optical Communications (WOC 2002), 17-19 July 2002, Banff, Alta., Canada, 2002. 13 M. Grossglauser and D. N. C. Tse, "Mobility Increases the Capacity of Ad Hoc Wireless Networks," IEEE/ACM Transactions on Networking, vol. 10, pp. 477, 2002. 14 A. F. Winfield, L. E. Parker, G. W. Bekey, and J. Barhen, "Distributed Sensing and Data Collection Via Broken Ad Hoc Wireless Connected Networks of Mobile Robots," in Distributed Autonomous Robotic Systems, vol. 4, Ed. Berlin: Springer, 2000, pp. 273. 15 T. Small and Z. J. Haas, "The Shared Wireless Infostation Model - a New Ad Hoc Networking Paradigm (or Where There Is a Whale, There Is a Way)," MOBIHOC 2003: Proc. of The Fourth ACM International Symposium on Mobile Ad Hoc Networking and Computing, Jun 1-3 2003, Annapolis, MD, United States, 2003. 16 D. L. Gu, G. Pei, H. Ly, M. Gerla, B. Zhang, and X. Hong, "UAV Aided Intelligent Routing for Ad-Hoc Wireless Network in Single-Area Theater," Proceedings of the 2000 IEEE Wireless Communications and Networking Conference, Sep 23-28 2000, Chicago, IL, United States, 2000. 17 K. Xu, X. Hong, M. Gerla, H. Ly, and D. L. Gu, "Landmark Routing in Large Wireless Battlefield Networks Using Uavs," Proceedings of the Milcom 2001: Communications for Network-Centric Operations: Creating the Information Force, Oct 28-31 2001, McLean, VA, 2001. 18 M. Gerla, X. Kaixin, and H. Xiaoyan, "Exploiting Mobility in Large Scale Ad Hoc Wireless Networks," Proceedings of the 2003 IEEE 18th Annual Workshop on Computer Communications, 20-21 Oct. 2003, Dana Point, CA, USA, 2003. 19 P. Basu, J. Redi, and V. Shurbanov, "Coordinated Flocking of UAVs for Improved Connectivity of Mobile Ground Nodes," Proceedings of the MILCOM2004, 2004. 20 I. Chatzigiannakis and S. Nikoletseas, "Design and Analysis of an Efficient Communication Strategy for Hierarchical and Highly Changing Ad-Hoc Mobile Networks," Mobile Networks and Applications, vol. 9, pp. 319, 2004. 21 R. C. Shah, S. Roy, S. Jain, and W. Brunette, "Data Mules: Modeling and Analysis of a Three-Tier Architecture for Sparse Sensor Networks," Ad Hoc Networks, vol. 1, pp. 215, 2003. 22 Q. Li and D. Rus, "Sending Messages to Mobile Users in Disconnected Ad-Hoc Wireless Networks," Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MOBICOM 2000), Aug 6-Aug 11 2000, Boston, MA, USA, 2000.


23 P. Basu and J. Redi, "Movement Control Algorithms for Realization of Fault-Tolerant Ad Hoc Robot Networks," IEEE Network, vol. 18, pp. 36, 2004. 24 "Proceedings of SPIE: Unmanned Ground Vehicle Technology Iii," Proceedings of the Unmanned Ground Vehicle Technology, Apr 16-17 2001, Orlando, FL, United States, 2001. 25 M. Younis, M. Bangad, and K. Akkaya, "Base-Station Repositioning for Optimized Performance of Sensor Networks," Proceedings of the 2003 IEEE 58th Vehicular Technology Conference, VTC2003-Fall, Oct 6-9 2003, Orlando, FL, United States, 2004. 26 W. Wang, V. Srinivasan, and K. C. Chua, "Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks," Proceedings of the International Conference on Mobile Computing and Networking, 2005. 27 D. P. Spanos and R. M. Murray, "Robust Connectivity of Networked Vehicles," 43rd IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, 14-17 December, 2004. 28 D. Goldenberg, J. Lin, A. S. Morse, B. E. Rosen, and Y. R. yang, "Towards Mobility as a Network Control Primitive," Mobihoc '04, Roppongi, Japan, 24-26 May, 2004. 29 B. Taub and D. L. Schilling, Principles of Communication Systems. New York: McGraw-Hill, 1986. 30 C. Dixon and E. W. Frew. "Maintaining a Linked Network Chain Utilizing Decentralized Mobility Control," AIAA's Guidance, Navigation and Control Conference and Exhibit, Keystone, CO, 21 - 24 Aug 2006, 2006. 31 W. Zhao, M. Ammar, and E. Zegura, "A Message Ferrying Approach for Data Delivery in Sparse Mobile Ad Hoc Networks," Proceedings of the Fifth ACM International Symposium on Mobile Ad Hoc Networking and Computing, MoBiHoc 2004, May 24-26 2004, Tokyo, Japan, 2004.
