On Effectiveness of Routing Algorithms for Satellite

On Effectiveness of Routing Algorithms for Satellite Communication Networks Wei Yu† , Sixiao Wei† , Guobin Xu† , Genshe Chen ‡ , Khanh Phamq , Erik P. Blasch∗ , and Chao Lu† † Computer

and Information Sciences Dept., Towson University, MD, 21252, USA ‡ Intelligent Fusion Technology, Inc, MD 20876, USA q Air Force Research Laboratory, Space Vehicles Directorate, Kirtland AFB, NM 87117 ∗ Air Force Research Laboratory, Information Directorate, Rome, NY 13441 ABSTRACT For worldwide, a satellite communication network is an integral component of the global networking infrastructure. In this paper, we focus on developing effective routing techniques that consider both user preferences and network dynamic conditions. In particular, we develop a weighted-based route selection scheme for the core satellite communication network. Unlike the shortest path routing scheme, our scheme chooses the route from multiple matched entries based on the assigned weights that reflect the dynamic condition of networks. We also discuss how to derive the optimal weights for route assignment. To further meet user’s preference, we implement the multiple path routing scheme to achieve the high rate of data transmission and the preemption based routing scheme to guarantee the data transmission for high priority users. Through extensive simulation studies, our data validates the effectiveness of our proposed routing schemes. Keywords: Satellite Communication Networks, Weighted-based Route Selection, Bandwidth Utilization, End-to-end Delay

1. INTRODUCTION Because of the global coverage, satellite communication networks dictate optimal performance for networking, message passing, and data routing. The satellite communication network needs to support various applications, including voice, video, and message data. However, the limited network resources in the satellite communication infrastructure becomes a major issue for supporting a large number of users and applications. Because of the unique characteristics of satellite communication links that are connected to heterogeneous satellite nodes and users, there are several challenges in designing the satellite communication network. First, satellites are located at low, medium, or high earth orbits. Satellites have different up/down link bandwidths and computing resources and the routing design should consider heterogeneous satellite nodes. Second, satellite nodes are mobile, especially satellites at low and medium orbits. It is important to develop schemes to reduce the impact of satellite mobility on network performance. Third, the users of satellite communication networks may demand different quality of service (QoS) in terms of the endto-end delay, throughput, error, and bandwidth to communicate with ground or airborne users.1 When the network does not have enough resources, it should accommodate new traffic flows for urgent use. To address the above issues, we develop effective routing schemes for satellite communication networks to achieve low end-to-end delay and high network utilization. To be specific, we develop a weighted-based route selection scheme for the core satellite communication network. Unlike the shortest path routing scheme, the weighted-based route selection scheme chooses the route from multiple matched next-hop entries based on weights, which reflect the dynamic network. The higher the weight, the better chance to be selected to route the traffic. In this way, we can effectively distribute the traffic through different routes so that the end-to-end delay and network throughput can be improved. To benefit the load sharing effect, we design the scheme to assign weights to the matched entries in the routing table and conduct the analysis to derive the optimal weight assignment. For further author information, Chen:[email protected]

please

send

correspondence

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Yu:

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and

Sensors and Systems for Space Applications VI, edited by Khanh D. Pham, Joseph L. Cox, Richard T. Howard, Genshe Chen, Proc. of SPIE Vol. 8739, 87390Q · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2015536

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Genshe

To further meet user’s QoS requirements, we implement the multiple path routing scheme to achieve the high data transmission rate and the preemption based routing scheme to provide QoS for high priority users. To validate the effectiveness of our proposed schemes, we conduct extensive simulations using ns-2. Our data validates that our proposed the weighted-based route selection and multiple paths based routing schemes can increase network throughput and reduce end-to-end delay and the preemption based routing scheme can effectively preserve QoS for high priority use. The remainder of the paper is organized as follows: In Section 2, we give an overview of satellite communication networks. In Section 3, we introduce our investigated schemes, including the weight-based route selection, the multiple path based routing, and the preemption based routing schemes. In Section 4, we show experimental results to validate the effectiveness of our proposed schemes. We conclude the paper in Section 6.

2. BACKGROUND Generally speaking, satellites can be categorized according to the types of their orbits. Figure 1 shows the relative positions of satellites moving in different orbits. The satellite communication systems commonly use circular orbits. Note completely true as a LEO - the point is at the center of the Earth and GEO is some point on the Earth surface. The benefit of using the circular orbit is to ensure that satellites move at constant speeds. Satellites in circular orbits can be further classified as Geosynchronous Earth Orbit (GEO), Medium Earth Orbit (MEO), or Low Earth Orbit (LEO) satellites according to their altitudes. GEO satellites are located 35786km above the Equator.2 Because the angular velocity of a GEO satellite matches the angular rate of rotation of the Earth, it appears stationary when it is seen from the Earth. To provide the coverage for the Earth, a least three GEO satellites are required. The propagation delay between an Earth ground station and a GEO satellite varies with the difference in position in both longitude and latitude. MEO satellites are located at an altitude in [9000km, 11000km], which is between the inner and outer Van Allen radiation belts.3 A MEO satellite appears in motion when it is seen from the Earth and has the visibility period of tens of minutes. The average propagation for the communication through MEO satellites is around 110 − 130ms. LEO satellites have lower altitudes than the MEO satellites, typically in [500km, 2000km].4 To provide the global coverage, a large number of LEO satellites are needed and the number of satellites depends on the coverage and the minimum elevation angle used for communication. LEO satellites move rapidly relative to the surface of the Earth, with a speed of around 25000km/hour. The propagation delay between ground station and LEO satellite is often less than 15ms. Because of the low delay and power for ground stations, LEO satellites are attractive. In Figure 2, we show the satellite access network, which consists of hosts, ground station, and satellite nodes, which are connected to the satellite core network.

Figure 1: An Example of Core (Satellite) Network

Figure 2: An Example of Access Network

3. OUR SCHEMES With the increasing demands from various applications, the satellite communication network may suffer from congestion, leading to degraded QoS. To reduce end-to-end delay, avoid congestion, and improve the bandwidth utilization, we develop effective and efficient routing schemes. In the following, we first present our weighted-based route selection scheme and then present the multiple path based routing and the preemption based routing schemes.

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3.1 Weighted-Based Route Selection Scheme To effectively distribute the traffic over different routes and improve the end-to-end delay and network throughput in the satellite communication network, we develop a weighted-based route selection scheme. Unlike the shortest path routing scheme, our proposed scheme randomly selects the route from multiple matched next-hop entries based on assigned weights, which are determined to reflect dynamic network condition. Using the weighted-based route selection scheme, we can accommodate and extend performance over multiple conditions as they arise. The higher the weight, the better chance to be selected as the next hop. In this way, we can effectively distribute the traffic through different routes and improve the end-to-end delay and network throughput. To benefit the load sharing effect, we develop algorithms to carefully assign the weights to the matched entries in the routing table. We also conduct the brief analysis for deriving the optimal weight assignments. Without loss of generality, we design a simple and effective weight assignment scheme that covers a wide range of heuristics. Denote R as eligible entries in the routing table for a given destination. Denote these entries as 1, 2, . . . , R and their corresponding weights are W1 , W2 , . . . , WR , respectively. From the information provided in the entries of routing table, a rule of thumb is that the weight should be inversely proportional to the distance of the route and should be proportional to the available bandwidth. Hence, for i = 1, 2, . . . , R, we have Wi ∝ α

1 + βBi , Di

(1)

where Di is the distanceP to satellite i and Bi is the available bandwidth, α and β represent the coefficients of the expression. R To normalize Wi (e.g., (i=1) Wi = 1), we assign Wi as follows, Wi = P R

α D1i + βBi

j=1

(α D1j + βBj )

.

(2)

where i = 1, 2, . . . , R. However, we may further generalize Equation (2) by adding where m, n are non-negative real numbers. Then, for i = 1, 2, . . . , R, we have Wi = P R

α D1i

m

+ β(Bi )n

1 m j=1 [α( Dj )

+ β(Bj )n ]

.

1 Di

and Bi with a power of m and n,

(3)

The detailed procedure of the weighted-based route selection scheme is shown in Algorithm 1. With different parameters, i.e., α, β, m, n, we can use the weighted-based route selection to deal with different scenarios, which require different treatment of distance and bandwidth. As an example, we show the selection of parameters in the following two scenarios. Scenario 1: When the network traffic is low, there is no congestion in wireless links connected to satellites. As we know, the propagation delay and queuing delay at the links are the two major factors for end-to-end delay. As the network is not congested, the queuing delay is very low and the propagation delay plays a key role in end-to-end delay. Hence, using the shortest path to route the traffic will incur the smallest propagation delay so that Di is the main factor for assigning Wa . For the weight assignment shown in Equation (3), we can see that in the low traffic rate scenario, m and α should be given much greater than n and β, respectively because of the dominant effect of distance Di . This indicates that when the traffic rate is low, n and β should be smaller in comparison with m and α. Scenario 2: When the network traffic is high, the queuing delay incurs a larger impact on end-to-end delay in comparison with prorogation delay. To reduce the impact from queuing delay, we shall distribute traffic to routes, which are idle or not fully loaded. Hence, the route selection should be mainly determined by the available bandwidth. In this scenario where the network traffic is high, as shown in Equation (3), we can see that m and β should be assigned much smaller than n and α in order to ensure that the major contribution to the weight should be based on bandwidth Bi . Hence, we conclude that when the network traffic is high, n and β should be larger than m and α. To improve network performance, our goal is to minimize the overall end-to-end delay for all traffic. We assume that packets arrival rate follows a Poisson process with a rate of λ. Furthermore, the transmission of packets through different

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satellites can be modeled as an M/M/1 system with the arrival rate of λi = λ ∗ Wi and the service rate of Si = α D1i + βBi . With the approximation assumptions, the average delay of all the packets is ϕ(W1 , W2 , . . . , WR ) = 1

R X j=1

Wj ·

1 Sj

1−

W λ Sjj

+ 2

R X

Wj

j=1

Wj λDj , C

(4)

where Dj is the distance between the satellite and the ground station for packet j, c is the speed of the light and PR W1 , W2 , . . . , WR are subjects to the constraint ξ = j=1 Wj = 1. Hence, to minimize ϕ(· · · ) in Equation (4), we need to derive the optimal assigned weight Wi . Based on two scenarios discussed above, we now discuss the selection of different parameters. In the first scenario where the traffic rate is low, the queuing delays can be ignored in comparison with the propagation delay. Hence, 1  2 PR Wj λDj . According to the Lagrange Multipliers method, to obtain a local optimum value of ϕ(x), and ϕ = j=1 Wj c PR W D ∂ϕ ∂ξ weights must satisfy ∂W = β ∂W (j = 1, . . . , R), in which β = λ W1CD1 = . . . = λ jc j . With ξ = j=1 Wj = 1, we j j 1 obtain Wj = D PR 1 . j

i=1 Di

In the same way, we can prove that when the network traffic is high in the second scenario, the queuing delay is larger r √ Si Si −

than the propagation delay. Hence, we have 1  2 and then the weight is Wi = PR weight expression, with Si = α D1i + βBi and λ ≈ i=1 Si , we obtain α( D1i )m + β(Bi )n

Wi = P R

1 m j=1 [α( Dj )

+ β(Bj )n ]

.

Si Sj + λ

Sj

Wj

. To simplify the

(5)

3.2 Multiple Paths Based Routing and Preemption Based Routing Schemes Many applications (e.g., audio, and video), demand a high data transmission rate. To improve the network throughput, we consider leveraging the routing scheme using multiple paths, which establishes multiple paths between a sourcedestination pair, and then distributes traffic load over multiple paths simultaneously. In comparison with the single path based routing scheme, the multiple path routing scheme can increase the bandwidth between a source-destination pair, improve the network throughput, and reduce the end-to-end delay. The multiple path based routing scheme has several benefits. First, it increases the reliability of data transmission. When communication link fails, multiple path based routing scheme can still transmit data. Second, when the link bandwidth is limited, to meet the user’s QoS requirements, the large amount of data can be separated into small pieces and those small pieces can be transmitted through multiple paths to the destination simultaneously. In this way, the congestion and bottlenecks can be mitigated in the satellite communication network and the network resources can be used effectively. We consider the preemption based routing scheme that provides the bandwidth guaranteeing for the data transmission of high priority use when the network does not have enough bandwidth to deliver the high priority data. To this end, we implement the preemption based routing scheme in both network and transport layers in satellite communication networks. When the network is congested and there are packets from high priority users to deliver, the router can automatically drop packets or reduce traffic flows associated with low priority. In this way, we can ensure data transmission for high priority use can be delivered. Furthermore, when we conduct the preemption, we shall determine packets or traffic flows with the lowest priority and drop them.

4. PERFORMANCE EVALUATION Using ns-2, we simulate our proposed routing schemes. In our simulation, we consider an integrated network that consists of one GEO satellite, 12 MEO satellites, and 66 LEO satellites. All the simulation parameters for satellites in different layers are listed in Table 1. We also configure a number of ground stations on the Earth to emulate sources and destinations that use the satellite core network to transmit data. In our simulation, all the parameters associated with satellites remain the same and all satellites move on defined orbits. The handoff time of each satellite is set as 10 time

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Algorithm 1: Weighted-Based Route Selection : Total Route Entries R; Distance of Route Entries D = [D1 , D2 , . . . , DR ]; Available Bandwidth of Route Entries B = [B1 , B2 , . . . , BR ]; Output : Associated Weights on Different Routes W = [W1 , W2 , . . . , WR ] Associated Weight Assignment: Obtain the distance between ground station and satellite D; Extract the available bandwidth of the different routes B; for i = 1 : R do Input

1 2 3 4 5

Wi =

α D1

i

m

+β(Bi )n

1 m n j=1 [α( D ) +β(Bj ) ]

PR

;

j

6 7 8 9 10 11 12 13 14 15 16

17 18 19 20 21 22 23 24

Calculate the assigned weights for different satellite route entries; end Route Selection: Collect associated weights for all the route entries W = [W1 , W2 , W3 , · · · , WR ]; We have constraint: Σ( i = 1)R Wi = 1; Initialize N = 0 and an array M that store next-hop ID with weight consideration; for i = 1 : R do for j = 1 : 100W1 do Mj = 1; N ++; end .. . for j = N + 1 : N + 100WR do Mj = R; N ++; end end r = ran.nextInt(M ); Randomly generate a number r from array M ; Choose route entry r to transmit the packet;

Table 1: Satellite Simulation Parameters Altitude Planes Satellite per plane Inclination (degree) Inter-plane separation (degree) Seam separation (degree) Elevation mask (degree) Intra-plane phasing Inter-plane phasing ISLs per satellite ISL bandwidth Up/downlink bandwidth

LEO (Iridium) 780 km 6 11 86.4 31.6 22 8.2 Yes Yes 4 25 Mb/s 1.5 Mb/s

MEO (Odyssey) 10000 km 3 4 55 15 15 40 Yes No 8 25 Mb/s 1.5 Mb/s

GEO 36000 km 1 2 15 N/A N/A 180 No No 1 25 Mb/s 10 Mb/s

units. When the simulation starts, the source nodes (ground stations) begin to transmit packets to the destination nodes. We increase the packet size and intervals when the time progresses, which represent the increased demand from users. We use the following metrics to evaluate the effectiveness of our proposed algorithms: (i) Throughput is defined as the successful data delivery rate over the network; (ii) Packet successful transmission ratio is defined as the ratio of the total number of received packets vs. the total number of transmitted packets and is used to measure the congestion status of the network; (iii) End-to-end delay is defined as the time taken for packets transmitted over the network from the source to the destination in average.

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18

-Weight-Based Selection -Shortest Path Routing

16

° 08

14 12

E 06

r 08

m 04

06 04

-Shortest Path Routing -Weight-Based Selection

02 20

30 40 50 Number of Users

60

70

80

°40

45

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60 Number of USers Users

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Time Slot's

Figure 3: Number of Users vs. Time

Figure 4: Throughput vs. Number of Figure 5: Packet Successful TransmisUsers sion Ratio vs. Number of Users

03

-Single Path

Shortest Path Routing Weight -Based Selection

-Mulfiple Paths

0.25

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°15

é `m 0.15

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0.1

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20 30 Number of Users

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Figure 6: End-to-end Delay vs. Num- Figure 7: Throughput vs. Number of Figure 8: End-to-end Delay vs. Number of Users Users ber of Users

In Figure 3, we show the relationship between the number of users and time. As we can see, as the time progresses, more users are connected to the network. With the fixed packet size (e.g., 1000 bytes), the decline of packet arrival intervals can achieve a similar effect as the increase of the number of users. Figure 4 shows the throughput vs. the number of users for both our the weighted-based route selection scheme and the shortest path route selection scheme. As we can see, our developed scheme has much better throughput when more than 45 users are included into the network. This is expected as our developed scheme can choose the best routes to transmit packets when the number of users increases. Figure 5 shows the relationship between the packet successful transmission ratio and the number of users. For the shortest path routing scheme, the packet successful transmission ratio is around one initially when the network is not congested. When the number of users increases, the network becomes congested, posing the decline of the packet retransmission ratio. 810s

1

-N -MLPP - MLPP

End ro end delay of High Pnonry Packet's

8

6 5

3

-1' 2

°O

20

40

60

80

100

120

140

160

Time Slotls

Figure 9: High Priority Packet Throughput vs. Time

Figure 10: End-to-end Delay of High Priority Packets vs. Time

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Differently, the packet successful transmission ratio of our developed routing scheme remains high consistently. As shown in Figure 6, the trend of end-to-end delay of our developed scheme is smaller than that of the shortest path routing scheme because our scheme can avoid the congested links and distribute the traffic to other links with available bandwidth. As shown in Figure 7, when we apply multiple path routing scheme, the network throughput is more than that of the single path routing scheme, because the single path routing scheme is easier to reach the bandwidth limit. Simultaneously, Figure 8 illustrates the performance of the end-to-end delay between single path and multiple paths. As we can see, the endto-end delay of multiple path routing scheme is smaller than that of the single path scheme because the because essentially more bandwidth can be utilized to transmit data. We also evaluate the effectiveness of preemption based routing scheme. In our simulation, we assign users to high, medium, or low priority levels. Figure 9 shows the throughput of high priority users when all the users increase their data transmission rate to the network. When using the preemption based routing scheme, the high priority users can acquire more bandwidth than the one in the system without using the preemption based routing scheme. In Figure 10, we show the end-to-end delay of a high priority user and show that the preemption based routing scheme achieves smaller delay for higher priority users.

5. RELATED WORK In the past, there are a number of research efforts, which have been made on studying routing algorithm in the satellite communication networks.5–23 For example, Chen et al.5 studied the architectures and routing protocols for satellite and space communication networks to support applications with different traffic types and heterogeneous QoS requirements. Hogie et al.6 called for using the Internet protocols to support spacecraft communications and discussed issues in physical, data link, and network layers. NASA recently proposed the research on Disruption Tolerant Networking (DTN) for Space Operations, which is used to establish better communication with spacecraft and enable the exploration of the cosmic space.23, 24 This research was to develop and operate the communications protocols, which enable Internet-like communications between spacer components (such as satellite, earth station, vehicles).23 Chen et al.8 proposed a Satellite Grouping and Routing Protocol (SGRP) based on the delay reports from LEO and MEO satellites. Zhou et al.9 proposed a clustering based scheme for satellite communication networks to increase the stability and reduce the overhead of storage using a five-dimensional vector model. Akyildiz et al.10 proposed a new congestion control scheme called TCP (Transmission Control Protocol)-Peach for satellite communication networks, which consists of the sudden start and rapid recovery algorithms, along with the standard TCP congestion avoidance and fast retransmit schemes. Durresi et al.11 proposed a new Diffserv-based scheme for the bandwidth allocation during the network congestion. In their proposed scheme, when congestion occurs, all flows obtain a fair share of available bandwidth, which is in proportion to the subscribed information rate. Wang et al.12 proposed a congestion avoidance routing scheme to deal with congestion in satellite communication networks. Kota et al.14 proposed a QoS framework for satellite IP networks, including requirements, objectives, and mechanisms. Ercetin et al.15 proposed a routing algorithm called Predictive Routing Protocol (PRP), which exploits the predictive nature of the LEO satellite topology to maximize the total number of users supported by the network, while meeting QoS requirements from end users.

6. CONCLUSION In this paper, we developed a weighted-based route selection scheme for the satellite communication network. Unlike the shortest path routing scheme, our develop routing scheme chose the route from multiple matched routes using carefully assigned weights, which reflect the dynamic network condition. We discussed how to derive optimal weight assignments. To further meet user’s QoS requirements, we implemented the multiple path routing scheme to achieve the high data transmission rate and the preemption based routing scheme to preserve the network resources for high priority users. Through extensive simulations, our data showed the effectiveness of our proposed routing schemes.

REFERENCES [1] E. Blasch, G. Eusebio, and E. Huling, “Investigating effects of communications modulation technique on targeting performance,,” in Proc. of SPIE, Vol. 6229, 2006. [2] Geostationary Orbit, http://en.wikipedia.org/wiki/Geostationary_orbit. [3] Medium Earth Orbit, http://en.wikipedia.org/wiki/Medium_Earth_orbit. [4] Low Earth Orbit, http://en.wikipedia.org/wiki/Low_Earth_orbit.

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[5] C. Chen, Advanced Routing Protocols for Satellite and Space Networks. PhD thesis, Electrical and Computer Engineering Department, Georgia Institute of Technology, 2005. [6] K. Hogie, E. Criscuolo, and R. Parise, “Link and routing issues for internet protocols in space,” Proceedings of Aerospace Conference , 2001. [7] A. Iera and A. Molinaro, “Designing the interworking of terrestrial and satellite ip-based networks,” IEEE Communications Magazine , 2002. [8] C. Chen, E. Ekici, and I. F. Akyildiz, “Satellite grouping and routing protocol for leo/meo satellite ip networks,” Proceedings of IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WOWMOM) , 2002. [9] Z. Mu, G. Qing, and W. Zhenyong, “A novel stable clustering design method for hierarchical satellite network,” Chinese Journal of Aeronautics , 2010. [10] I. F. Akyildiz, G. Morabito, and S. Palazzo, “Tcp-peach: a new congestion control scheme for satellite ip networks,” IEEE/ACM Transaction on Networking (ToN) , 2010. [11] A. Durresi, P. K. Jagannathan, and R. Jain, “Scalable proportional allocation of bandwidth in ip satellite networks,” Proceedings of Aerospace Conference , 2003. [12] Z.-Y. Wang, X.-M. Gu, and Q. Guo, “A congestion avoidance routing algorithms based on ant-algorithm in satellite ip network,” Proceedings of Machine Learning and Cybernetics , 2006. [13] J. Cao and M. Stefanovic, “New results in stable switching congestion control for satellite tcp/aqm networks,” Proceedings of IEEE Conference on Decision and Control (CDC) , 2010. [14] S. Kota and M. Marchese, “Quality of service for satellite ip networks: a survey,” International Journal of Satellite Communications and Networking Special Issue: Satellite IP Quality of Service , 2003. [15] O. Ercetin, S. Krishnamurthy, D. Son, and L. Tassiulas, “A predictive qos routing scheme for broadband low earth orbit satellite networks,” Proceedings of The 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications , 2000. [16] A. Molinaro, F. D. Rango, S. Marano, and M. Tropea, “A scalable framework for in ip-oriented terrestrial-geo satellite networks,” Communications Magazine , 2005. [17] F. Chiti, R. Fantacci, D. Tarchi, S. Kota, and T. Pecorella, “Qos provisioning in geo satellite with onboard processing using predictor algorithms,” IEEE Wireless Communications , 2005. [18] Z. Yin, L. Zhang, X. Zhou, P. Xu, and T. Zhang, “Qos-aware multicast routing protocol for triple-layered leo/heo/geo satellite ip networks,” In Proceedings of 2010 Mobile Congress (GMC) Conference , 2010. [19] E. Ekici, I. F. Akyildiz, and M. D. Bender, “A multicast routing algorithm for leo satellite ip networks,” IEEE/ACM Transactions on Networking , 2002. [20] K. Liu, L. Cheng, and J. Zhang, “Efficient multicast routing for leo satellite ip networks,” Proceedings of IEEE Vehicular Technology , 2009. [21] H. Du, X. Huang, J. Liang, J. Liu, B. G. Evans, and I. Chlamtac, “Cross-layer quality-driven adaptation for scheduling heterogeneous multimedia over 3g satellite networks,” Journal of Wireless Networks , 2010. [22] M. Ali, L. Liang, Z. Sun, H. Cruickshank, P. Thompson, L. M. Audah, T. Bouquentar, and N. Alagha, “End-toend qos measurement over a dvb-rcs satellite network,” Personal Satellite Services Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , 2010. [23] K. Gifford, “Disruption tolerant networking for space operations (dtn),” April 2013. http://www.nasa.gov/ mission_pages/station/research/experiments/730.html#publications. [24] A. Jenkins, S. Kuzminsky, K. Gifford, R. L. Pitts, and K. Nichols, “Delay/disruption-tolerant networking: flight test results from the international space station,” in Proceedings of 2010 IEEE Aerospace Conference, 2010.

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