Contact quality based forwarding strategy for delay tolerant network

1. Contact quality based forwarding strategy for delay tolerant network QaisarAyub, Sulma Rashid, M.SoperiMohdZahid, Abdul Hanan Abdullah Adviser:Frank.Yeong-Sung LinPresent by Li-Min Zheng

2. Introduction • The review of DTN routing protocols • System model • Simulations and results • Conclusion Agenda

3. Introduction

4. The ad hoc protocols maintain routing information about intermediate links from source to destination before the transmission of data. • These privileges are impossible in delay tolerant network, where frequency of disconnections is high due to network partitioning, node movement, dynamic topology change and periodic shutdown of low energy nodes. • The applications such as wildlife monitoring, military and urban areas network are possessed with such attributes. Introduction

5. In delay tolerant network, the message transmission is achieved via intermittent opportunistic connections by adopting store carry and forward paradigm wherein node stores an incoming message in its buffer, carries it while moving, and forwards when comes within the transmission range of other nodes. • The DTN routing protocols can be either single copy or multi-copy. • single copy • multi-copy. Introduction

6. Consider a city-based environment partitioned into multiple regions caused by the intersection of buildings. • Pedestrians • Bus • Trains • cars • cabs • DTN in city-based environment • good-quality nodes • message drops Introduction

7. We have proposed message forwarding strategy called as Contact Quality Based Forwarding Strategy(CQBFS) forcity based environments composed of heterogeneous network traffic, including pedestrians, cars, city buses, trains and cabs. • We have monitored the current activity of nodes by modifying the transmit factor and drop factor with detailed algorithmic descriptions. • We have proposed an efficient buffer management quality impact based buffer management policy to reduce the impact of message drop on the networkthroughput. • Wehaveanalyzedtheperformanceofexistingandproposed strategybytherealtimetraceSassyandHelsinkicityFinland with wellknownroutingprotocolssuchasPRoPHET,Epidemic, MaxpropandTimetolive(TTL)basedrouting. Introduction

8. The review of DTN routing protocols

9. The review of DTN routing protocols

10. The review of DTN routing protocols

11. System model

12. System Model

13. Model Name:Contact quality based forwarding strategy (CQBFS) • The existing probabilistic models compute quality value for a node in terms of delivery probability that is based on encountering frequency to meet message destination. • The proposed CQBFS modifies the operational architecture of transmit and drop factor(Ayub et al.,2013a) along with quality impact based buffer management policy and presents the comprehensive algorithmic descriptions of designed methodology by using following fourmodules: • Self-Statistical Update Module(SSUM) • Neighboring Statistical Update Module(NSUM) • Quality Update Module(QUM) • Contact Quality Scheduler(CQS) System Model

14. System Model

15. Self-Statistical Update Module(SSUM) System Model

16. System Model

17. Neighboring Statistical Update Module(NSUM) • The following four vectors are used to store the statistics about neighboring nodes: • Recent Encounter Vector (REV) • Trans- mit Count Vector (TCV) • Drop Count Vector (DCV) • Receive Count Vector (RCV). System Model

18. Neighboring Statistical Update Module(NSUM) • Recent Encounter Vector (REV) System Model

19. Neighboring Statistical Update Module(NSUM) • Transmit Count Vector (TCV) System Model

20. Neighboring Statistical Update Module(NSUM) • Drop Count Vector (DCV) System Model

21. Neighboring Statistical Update Module(NSUM) • Receive Count Vector (RCV). System Model

22. Neighboring Statistical Update Module(NSUM) System Model

23. System Model

24. Quality Update Module(QUM) • DF(Drop fator) • TF(Trasmit factor) • QV(Quality vector) System Model

25. Quality Update Module(QUM) System Model

26. Quality Update Module(QUM) System Model

27. Quality Update Module(QUM) System Model

28. System Model

29. Contact Quality Scheduler(CQS) • The CQB scheduler consists of • Message Forwarding Unit (MFU) • Buffer Management Unit (BMU) System Model

30. Contact Quality Scheduler(CQS) • Message Forwarding Unit (MFU) System Model

31. Contact Quality Scheduler(CQS) • Buffer Management Unit (BMU) • In the proposed buffer scheduling policy, we have used a vector titled as Quality Impact Vector (QIV). • The quality impact vector consists of Message Id and its Quality Impact (QI). • The QI has different meanings for source and relay. • source message • relay message System Model

32. Contact Quality Scheduler(CQS) • Buffer Management Unit (BMU) System Model

33. Contact Quality Scheduler(CQS) • Buffer Management Unit (BMU) System Model

34. Contact Quality Scheduler(CQS) System Model

35. Simulations and results

36. The assessment of routing protocols has been investigated under ONE (Keränen et al., 2009) simulator. ONE is a discrete event simulator written in JAVA and has been particularly used by a numerous researchers to analyze the disrupted store- carry-forward applications. • We have compared the CQBFS with PRoPHET, Epidemic MAXPROP and TTL based routing protocols in terms of minimizing the number of transmissions, message drop, overhead and increasing delivery ratio. • Two Scenario: • SCENARIO-01: real time trace sassy • SCENARIO-02: Helsinki city based environment Simulations and results

37. SCENARIO-01: real time trace sassy • The trace consists of 25 mobile devices carried by the pedestrians. • In scenario 1 the proposed CQBFS is compared with PRoPHET, Epidemic, Maxprop and TTL based routing protocols under the metric of • transmissions • message drop • delivery ratio Simulations and results

38. SCENARIO-01: real time trace sassy Simulations and results

39. SCENARIO-01: real time trace sassy Simulations and results

40. SCENARIO-01: real time trace sassy Simulations and results

41. SCENARIO-02: Helsinki city based environment • Consists of heterogeneous traffic : • Pedestrians (20 each community , 0.5 km/h to 1.5 km/h, 2MB) • Cars (20 each community ,3 km/h to 7 km/h ,2MB) • Cabs (20, 3 km/h to 14 km/h , 2MB) • Trams (3, 7 km/h to 10 km/ h ,50MB) • Buses (3, 7 km/h to 10 km/ h ,50MB) Simulations and results

42. SCENARIO-02: Helsinki city based environment • In scenario 2 we have compared the proposed CQFS with PRoPHET, Epidemic and TTL based routing protocols based on • delivery ratio • end-to-end delay • hop count average Simulations and results

43. SCENARIO-02: Helsinki city based environment Simulations and results

44. SCENARIO-02: Helsinki city based environment Simulations and results

45. SCENARIO-02: Helsinki city based environment Simulations and results

46. Conclusion

47. In this paper, we have proposed Contact Quality Based For- warding Strategy that observes the quality of current contact in terms of its ability to carry the received messages. The various heuristics were used to determine the accuracy of suitable inter- mediate node as a message carrier. Moreover, a buffer manage- ment policy was proposed to reduce the impact of message drop on network throughput. We have measured the accuracy of existing and proposed forwarding by using real time mobility traces such as Sassy and Helsinki city. The proposed CQBFS out- performs existing PRoPHET, Epidemic, Maxprop and TTL based routing protocols in terms of minimizing the transmissions, delivery delay, hop count average and raising the delivery ratio. Conclusion

48. Thanks for your listening