CHAPTER 3 SAGE Routing
3.2 Leverage Algorithm
3.2.2 Geographic Layer
the result, S selects n nodes to become social allies. S calculates social level that connects any two nodes. S creates n+1 messages in message buffer for transmitting to social allies or D. If social level less than 6, we execute Geographic Messenger Forwarding Algorithm in geographic Messenger Forwarding Algorithm.
3.2.2 Geographic Layer
Figure 8: Geographic Messenger Forwarding Algorithm
In Geographic layer, we design a Geographic Messenger Forwarding Algorithm to select neighbors’ for forwarding messages. Without loss of generality, consider node A, which meets node B and decides to forward message, we calculate geographic probability (Pgeo) that can be defined as
Pgeo(B) = αCommunityActivity(B) * βDirection(B) * γVelocity(B)
Here, Direction (B) is the angle between Destination and B’s destination. As the Figure 9 shows below,
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Figure 9: Message Relay Policy
8 ： The direction from node A to its message m(a)’s destination.
9 ： The direction from node B to its destination.
θ ： The angle between
8 and
9
A compares the geographic probability that from A to D and the geographic probability that from B to D. If the probability that from B to D is bigger than that from A to D, it means that B is a good receiver for relay the message. A transmits message to node B, until encounter social ally or D.
Consider the social level, the algorithm is below:
In Geographic layer, when S meets Node A, S compares the geographic probability
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message. S transmits message to node A. When message transmits to social ally, then go to social layer to execute social ally selection algorithm.
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CHAPTER 4
Simulation and Results
In this section, we would like to evaluate our SAGE routing, and contrast its performance against Epidemic routing, PROPHET routing, and two social-based routing, SimBet and Bubble Rap. Additionally, we also want to see how the routing parameters, i.e., social ally and geographic parameters, impact the performance of our SAGE routing. We will first introduce the setup in our simulations.
The major object of DTNs routing protocol is to maximize delivery ratio on a rapidly changing topology. By considering energy, nodes’ lifetime is also an important issue to compare. We evaluate our design in four metrics:
l Messages delivery ratio
l Overhead ratio = :;<=>;? @=AB;CD *E;<FG;H;? @=AB;CD E;<FG;H;? @=AB;CD
l End-to-end delay
l Routing efficiency = I;DD=J; ?;<FG;H> H=CFK LG;HM;=? H=CFK ∗ OP?*CK*;P? ?;<=>
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4.1 Simulation Setup
We use a map-based model of a small part of Helsinki for The ONE simulator [24]
(version 1.4.1). All nodes are pedestrian and can only move on the roads of the map. When a node reaches its destination, it will randomly choose a new location of the map as its next destination and move to there by shortest path algorithm. Figure 10 is a snap shot of a simulation.
Figure 10: A snap shot of the ONE simulator
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4.1.1 General Settings of Simulator
Table 3: Parameters of simulation setting
The simulation time is 43200 seconds (12 hours). There is a warm up time for PROPHET set as 1000 seconds and all data will not count in evaluation during warm up period. The moving speed of nodes is 0.5 m/s to 1.5 m/s. The data transmission rate is 1 Mbps and the transmission range is 100m. The message size will randomly generate between 512 KB to 1 MB .Its generation interval is 5 seconds to 10 seconds and randomly chooses a node to own the message. The TTL is 5 hours. Each message randomly chooses a community of the
Map Size 4500m x 3400m
Simulation Time 43200 sec (12 hr) Number of Isolated Nodes 40, 80, 120
Number of Groups
4, 2, 1
Transmission Rate 1 Mbps
TX Radio Range 100 m
Message Size 512KB ~ 1MB (random) Interval of Msg. creation 5s ~ 10s (Total = 6190)
Buffer Size (Byte) 250 MB
Node Speed 0.5 m/s ~ 1.5 m/s
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map as its destination before generated. The buffer size is 250 MB in each node. Table 3 is the parameters we described above.
4.1.2 Parameters about SAGE Routing Protocol
We evaluate our design in Figure 11. Each node moves cross 3 communities. There are 6 communities, In this section, we would like to know the social ally parameter and geographic probability parameter that impact the performance of our SAGE routing protocol.
Figure 11: Community setting
4.1.2.1 Social Ally
We assume our geographic parameter: α=1, β=0, γ=0. We consider Social ally
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parameter from 0 to 6. In Figure 12, they show the more social allies that the higher delivery ratio. It is represented more social allies that mean the more message copies for the different social allies. Also, we found that n=3+1 had the trend of ascent in delivery ratio.
Figure 12: Delivery Ratio
We observed the social ally parameter that impacts the delay latency. In Figure 13 shows that there is a marked drop in delay latency when the social allies are more than 3 (n=3+1). Also, we found that the more the number of nodes have the lower delay latency, due to consider more nodes have more social allies have been chosen quickly.
Delivery Ratio
0.5
0.3
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Figure 13: End-to-end delay latency
In Figure 14, they show the overhead Ratio of SAGE routing protocol;. The more social allies that the higher overhead ratio. It is represented more social allies that mean the more message copies for the different social allies. Also, we found that n=4+1 had the trend of ascent in overhead ratio.
Average Delay
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3 (n=3+1), there is better routing efficiency. Since n=3+1 had the trend of ascent in delivery ratio and there is a marked drop in delay latency, finally we selected Social ally parameter is equal to 3.‧ 國
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Figure 15: Routing Efficiency
4.1.2.2 Geographic parameter
We Selected Social ally parameter is equal to 3 by section 4.1.2.1 result. In this section, we consider geographic probability parameter α, β and γ. We set number of nodes is equal to 40, 80 and 120. There are 5 sets in α, β and γ. We have set α, β and γ parameters like (α=1, β=0, γ=0), (α=0.8, β=0.1, γ=0.1), (α=0.6, β=0.2, γ=0.2), (α=0.4, β=0.3, γ=0.3) and (α=0.2, β=0.4, γ=0.4).
We observed the geographic probability parameter that impacts the delivery ratio. In Figure 16, they show geographic parameter: (α=0.6, β=0.2, γ=0.2) that the highest delivery ratio. It is represented node social community activity is more important than geographic information. But if we only consider social community activity parameter (α=1, β=0, γ=0), it is
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lower delivery ratio than (α=0.6, β=0.2, γ=0.2).
Figure 16: Delivery ratio of geographic parameter
In Figure 17, we observed the delay latency of SAGE Routing protocol. It shows that geographic parameter: (α=0.6, β=0.2, γ=0.2) that the lowest delay latency. It is represented node social community activity can help node to decide the better delivery strategy. If we only consider social community activity or geographic information, there is higher delay latency.
Since social properties are usually long-term characteristics and message transmissions rely on geographic position information, the geographic parameter: (α=0.6, β=0.2, γ=0.2) that the lowest delay latency.
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Figure 17: End-to-end delay of geographic parameter
We observed the geographic probability parameter that impacts the overhead ratio. In Figure 18, they show geographic parameter: (α=0.6, β=0.2, γ=0.2) that the lowest overhead ratio. Also, we found that the more the number of nodes have the higher overhead ratio, due to consider more nodes have more message copies.
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Figure 18: Overhead Ratio of geographic parameter
In result, Figure 16 and 17, they show geographic parameter: (α=0.6, β=0.2, γ=0.2) that the higher delivery ratio and lower delay latency. Figure 18 shows there are lower overhead ratio, due to social community activity increase to meet destination or social ally probability. In Figure 19 shows there are better routing efficiency in this geographic parameter.
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Figure 19: Routing Efficiency of geographic parameter
4.2 Simulation Results
Our social ally parameter is equal to 3 and our geographic probability is equal to (α=0.6, β=0.2, γ=0.2). Our SAGE routing protocol compares that Epidemic routing protocol with 4 copies, PROPHET routing protocol with 4 copies, SimBet routing protocol with 4 copies and Bubble Rap routing protocol with 4 copies. There are 3 scenarios are considered:
l Independent community l Multiple communities l Dependent community
In Independent community case (Figure 20), there are 4 communities. Each node moves within one community.
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Figure 20: Independent community case
In Figure 21 shows routing efficiency in independent community case. Due to the interaction between nodes is in an independent community, social behaviors are weak impact routing efficiency. Also, geographic information is weak impact routing efficiency. In this case, Epidemic routing protocol is better than other protocols, since there is no information to be considered.
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Figure 21: Routing Efficiency of independent community case
It is multiple communities’ case (Figure. 22). There are 6 communities. Each node moves cross communities. In Fig. 23 shows routing efficiency in multiple communities’ case.
Due to the interaction between nodes frequently, social-based routings are better than other protocols in routing efficiency. Our SAGE routing is not only social-based routing but also geographic information enhancement. SAGE routing is better than SimBet and Bubble Rap routing.
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Figure 22: Multiple community case
Figure 23: Routing Efficiency of multiple community case
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It is dependent communities’ case (Fig. 24). There are 4 communities. Each node moves cross 4 communities. In Fig. 25 shows routing efficiency in dependent communities’
case. SAGE routing is better than other routing protocols, Due to the interaction between people through social behaviors and message transmissions rely on geographic position information.
Figure 24: Dependent community case
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Figure 25: Routing Efficiency of dependent community case
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CHAPTER 5
Conclusions and Future Work
When a node has a forwarding message, all encountered nodes could be the candidates to relay it. Most of these DTN routing protocol, which attempt to make better relaying selection.
Consider both the interaction between people through social behaviors and that message transmissions rely on geographic position information. Social properties are usually long term characteristics and less volatile than node mobility. Forwarding message still need neighbors’
position information.
5.1 Conclusion
We proposed a SAGE routing protocol by dividing network topology into two sub layers:
social layer and geographic layer. In Social Layer, we design a Social Ally Select Algorithm to search a set of social allies. In Geographic Layer, we design a Geographic Messenger Forwarding Algorithm to decide forwarding messages. The simulation results show that if there are existing social behaviors, our routing protocol has higher delivery ratio and lower delay latency compared to other protocols.
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5.2 Future Work
In this thesis, our main idea is proposed a hybrid routing protocol by social behaviors and geographic information. Our results are based on simulation of social community. Our future work will try to collect social behaviors in real life to verification our algorithm.
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[1] Jian Shen, Sangman Moh, Ilyong Chung, “Routing Protocols in Delay Tolerant Networks: A Comparative Survey”, The 23rd International Technical Conference on Circuits/Systems,Computers and Communications, pp. 1577 - 1580, 8 July, 2008 [2] E.P.C. Jones and P.A.S ward, “Routing Strategies for Delay-Tolerant Networks”,
Submitted to Computer Communacation Review,2008
[3] A. Vahdat and D. Becker, “Epidemic routing for partially connected ad hoc networks,”
Duke University Technical Report CS-200006, April 2000.
[4] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray and wait: an efficient routing scheme for intermittently connected mobile networks,” in WDTN ’05: Proc. 2005 ACM SIGCOMM workshop on Delay-tolerant networking, 2005, pp. 252–259.
[5] J. Lebrun, C.-N. Chuah, D. Ghosal, and M. Zhang, “Knowledge-based opportunistic forwarding in vehicular wireless ad hoc networks”, in Proceedings of IEEE Vehicular Technology Conference (VTC), vol. 4, pp. 2289–2293, May 2005.
[6] A. Lindgren, A. Doria, and O. Schelen, “Probabilistic routing in intermittently connected networks”, in Proc. First International Workshop on Service Assurance with Partial and Intermittent Resources, pp. 239––254,2004.
[7] H. Dang and H. Wu, “Clustering and Cluster-Based Routing Protocol for Delay-Tolerant Mobile Networks”, IEEE Transactions on Wireless Communications, Vol. 9, No.6, pp.1874–1881, June 2010.
[8] P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot, “Pocket switched networks and human mobility in conference environments”, in Proc. ACM SIGCOMM Workshop on DTN and Related Topics, pp. 244––251, 2005.
[9] C. Liu and J. Wu, “Scalable routing in delay tolerant networks”, in Proc.ACM MobiHoc, 2007.
[10] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, “Impact of human mobility on the design of opportunistic forwarding algorithms”, in Proc. IEEE
‧
INFOCOM, pp. 1––13, 2006.
[11] M. Kim, D. Kotz, and S. Kim, “Extracting a mobility model from real user traces”, in Proc. IEEE INFOCOM, pp. 1––13, 2006.
[12] T. Spyropoulos, K. Psounis, and C. Raghavendra, “Performance analysis of
mobility-assisted routing”, in Proc. MobiHoc’06: 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing,pp. 49––60, 2006.
[13] J. Leguay, T. Friedman, and V. Conan, “DTN routing in a mobility pattern space”, in Proc. WDTN‘05: 2005 ACM SIGCOMM Workshop on Delay-tolerant Networking, pp.
276––283, 2005.
[14] Ari Keränen, Jörg Ott, Teemu Kärkkäinen, “The ONE Simulator for DTN Protocol Evaluation”, in SIMUTools’09: 2nd International Conference on Simulation Tools ans Techniques, Rome, March 2-6,2009
[15] H. Zhu, X. Lin, R. Lu, and X. Shen, “A secure incentive scheme for delay tolerant networks,” in Proc. 3rd International Conference on Communications and Networking in China (ChinaCom), Aug. 2008.
[16] H. Zhu, X. Lin, R. Lu, Y. Fan, and X. Shen, “Smart: A secure multilayer credit-based incentive scheme for delay-tolerant networks,” IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 4628–4639, Oct. 2009.
[17] P. Hui and J. Crowcroft, “How small labels create big improvements,” in International Workshop on Intermittently Connected Mobile Ad hoc Networks in conjunction with IEEE PerCom 2007, March 19-23, 2007.
[18] E. M. Daly and M. Haahr, “Social network analysis for routing in disconnected delay-tolerant manets,” in MobiHoc ’07 Proc. 8th ACM international symposium on Mobile ad hoc networking and computing, 2007.
[19] P. Hui, J. Crowcroft, and E. Yonek, “Bubble rap: Social-based for- warding in delay tolerant networks,” in Proc. 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May, 2008.
[20] W. Zhao, M. Ammar, and E. Zegura, “A message ferrying approach for data delivery in sparse mobile ad hoc networks,” in MobiHoc ’04: Proc. 5th ACM international
‧
symposium on Mobile ad hoc networking and computing, pp. 187–198, 2004.
[21] W. Zhao, M. Ammar, and E. Zegura, “Controlling the mobility of multiple data transport ferries in a delay-tolerant network,” in Proc. 24th IEEE International Conference on Computer Communications (INFOCOM), 2005.
[22] P. Juang, H. Oki, Y. Wang, M. Martonosi, L. S. Peh, and D. Rubenstein,
“Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with zebranet,” SIGOPS Oper. Syst. Rev., vol. 36, no. 5, pp. 96–107, 2002.
[23] T. Hossmann, F. Legendre, and T. Spyropoulos, “From contacts to graphs: pitfalls in using complex network analysis for DTN routing,” in INFOCOM’09: Proc. 28th IEEE International conference on Computer Communications Workshops, pp. 260–265, 2009.
[24] T. Hossmann, T. Spyropoulos, and F. Legendre, “Know thy neighbor: Towards optimal mapping of contacts to social graphs for DTN routing,” in INFOCOM’10: Proc. 29th IEEE International conference on Computer Communications, 2010.
[25] P. Hui, E. Yoneki, S.Y. Chan, and J. Crowcroft, “Distributed community detection in delay tolerant networks,” in Proc. ACM SIGCOMM Workshop, MobiArch’07, 2007.
[26] E. Yoneki, P. Hui, and J. Crowcroft, “Visualizing community detection in opportunistic networks,” in Proc. ACM MobiCom Workshop on Challenged Networks (CHANTS), September, 2007.
[27] P. Hui, E. Yoneki, and J. Crowcroft, “Identifying social communities in complex communications for network efficiency,” in Proc. 1st In- ternational Conference on Complex Sciences: Theory and Applications (Complex 2009), February, 2009.
[28] S. Y. Chan, P. Hui, and K. Xu, “Community detection of time-varying mobile social networks,” in Proc. 1st International Conference on Complex Sciences: Theory and Applications (Complex 2009), February, 2009.
[29] W. Wang, S. Eidenbenz, Y. Wang, and X.-Y. Li, “OURS: Optimal unicast routing system in non-cooperative wireless networks,” in Proc. 12th ACM Annual International
Conference on Mobile Computing and Networking (MobiCom 2006), 2006.