Contact quality based forwarding
strategy for delay tolerant network
Qaisar Ayub, Sulma Rashid, M.Soperi Mohd Zahid,
Abdul Hanan Abdullah
Adviser:Frank.Yeong-Sung Lin
Present by Li-Min Zheng
 Introduction
 The review of DTN routing protocols
Agenda
 System model
 Simulations and results
 Conclusion
Introduction
 The ad hoc protocols maintain routing information
about intermediate links from source to destination
before the transmission of data.
Introduction
 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
 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.
 Consider a city-based environment partitioned into
multiple regions caused by the intersection of buildings.
Introduction





Pedestrians
Bus
Trains
cars
cabs
 DTN in city-based environment
 good-quality nodes
 message drops
 We have proposed message forwarding strategy called as Contact
Quality Based Forwarding Strategy(CQBFS) for city based
environments composed of heterogeneous network traffic,
including pedestrians, cars, city buses, trains and cabs.
Introduction
 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 network throughput.
 We have analyzed the performance of existing and proposed
strategy by the real time trace Sassy and Helsinki city Finland with
well known routing protocols such as PRoPHET,Epidemic,
Maxprop and Time to live(TTL)basedrouting.
The review of DTN routing protocols
The review of
DTN routing
protocols
The review of
DTN routing
protocols
System model
System Model
 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.
System Model
 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
 Self-Statistical Update Module(SSUM)
System Model
System Model
 Neighboring Statistical Update
Module(NSUM)
System Model
 The following four vectors are used to store the
statistics about neighboring nodes:
(i)
(ii)
(iii)
(iv)
Recent Encounter Vector (REV)
Trans- mit Count Vector (TCV)
Drop Count Vector (DCV)
Receive Count Vector (RCV).
 Neighboring Statistical Update
Module(NSUM)
 Recent Encounter Vector (REV)
System Model
 Neighboring Statistical Update
Module(NSUM)
 Transmit Count Vector (TCV)
System Model
 Neighboring Statistical Update
Module(NSUM)
 Drop Count Vector (DCV)
System Model
 Neighboring Statistical Update
Module(NSUM)
 Receive Count Vector (RCV).
System Model
System Model
 Neighboring Statistical Update
Module(NSUM)
System Model
 Quality Update Module(QUM)
System Model
(i)
(ii)
(iii)
DF(Drop fator)
TF(Trasmit factor)
QV(Quality vector)
 Quality Update Module(QUM)
System Model
 Quality Update Module(QUM)
System Model
 Quality Update Module(QUM)
System Model
System Model
 Contact Quality Scheduler(CQS)
 The CQB scheduler consists of
System Model
(i)
(ii)
Message Forwarding Unit (MFU)
Buffer Management Unit (BMU)
 Contact Quality Scheduler(CQS)
 Message Forwarding Unit (MFU)
System Model
 Contact Quality Scheduler(CQS)
 Buffer Management Unit (BMU)
System Model
 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
 Contact Quality Scheduler(CQS)
 Buffer Management Unit (BMU)
System Model
 Contact Quality Scheduler(CQS)
 Buffer Management Unit (BMU)
System Model
System Model
 Contact Quality Scheduler(CQS)
Simulations and results
 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- carryforward applications.
Simulations
and results
 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
 SCENARIO-01: real time trace sassy
 The trace consists of 25 mobile devices carried by the pedestrians.
Simulations
and results
 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
 SCENARIO-01: real time trace sassy
Simulations
and results
 SCENARIO-01: real time trace sassy
Simulations
and results
 SCENARIO-01: real time trace sassy
Simulations
and results
 SCENARIO-02: Helsinki city based environment
 Consists of heterogeneous traffic :
Simulations
and results
(i)
Pedestrians (20 each community , 0.5 km/h to 1.5 km/h, 2MB)
(ii)
(iii)
(iv)
(v)
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)
 SCENARIO-02: Helsinki city based environment
Simulations
and results
 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
 SCENARIO-02: Helsinki city based environment
Simulations
and results
 SCENARIO-02: Helsinki city based environment
Simulations
and results
 SCENARIO-02: Helsinki city based environment
Simulations
and results
Conclusion
Conclusion
 In this paper, we have proposed Contact Quality Based Forwarding 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 intermediate node as a message carrier. Moreover, a buffer management 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 outperforms 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.
Thanks for your listening