This document presents a delay constrained routing algorithm for wireless sensor networks with a mobile sink. It begins with an introduction to sensor nodes, wireless sensor networks, and the challenges of routing in WSNs. It then discusses prior work on stationary and mobile sink approaches. The proposed work formulates the problem and solves it using an optimal traveling salesman tour calculation and variable sojourn time computation. Simulation results show the proposed algorithm outperforms an existing MILP approach in terms of throughput, delay, energy consumption and network lifetime.

1. Presented by:
Md Arquam
Delay Constrained Routing Algorithm
for WSN with Mobile Sink

2. Outline
 Introduction
 Literature Survey
 Proposed Work
 Simulation and Result
 Conclusion
 References
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3. INTRODUCTION
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4. Sensor Node:Difinition
• A sensor node, is a node in a wireless sensor network that is capable
of performing some processing, gathering sensory information and
communicating with other connected nodes in the network.
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5. Sensor Characteristics
 Low computing capabilities.
 Low Memory.
 Radio.
Low-power.
Low data rate.
Limited range.
 Limited Energy.
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6. WSN: Definition
• Wireless sensor network (WSN) refers to a group of spatially dispersed
and dedicated sensors for monitoring and recording the physical
conditions of the environment and organizing the collected data at a
central location.
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7. WSN: Design Challenge
 Scalability
 Fault Tolerance
 Topology
 Hardware Constraint
 Transmission Media
 Power Consumption
 Environment
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8. General Network Architecture of
WSNS
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Network Architecture of WSNS

9. General Network Architecture of
WSNS
 The sensor nodes are usually scattered in a sensor field. Each of
these scattered sensor nodes has the capabilities to collect data and
route data back to the sink and the end users.
 Sink Node may be either stationary or mobile.
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10. WSN: Design Challenges in Routing
Protocol
 Unable to maintain global addressing schemes
 Redundant data
 Limited energy, processing and storage
 Application Specific
 Flow of sense data from multiple source
 Location Awareness
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11. With Stationary Sink
 The main aim is to design for energy efficient route setup and reliable
relaying of data from the sensor nodes to the sink so that the lifetime
of the network is maximized.
 Static Sink causes Energy Hole Problem.
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12. The Energy Hole Problem
 The sensor node transmits the data directly to the sink, or it relays the
data through neighbor nodes.
 The nodes near to the sink relay more data than other nodes. This
causes the nodes close to BS die out quicker than rest of the nodes.
 The phenomenon known as “energy hole problem” makes the network
unusable in spite of majority of nodes being still live.
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13. With Mobile Sink
 By using mobile sink every time route of data from gateway node
to sink is changed.
 Sensor close to mobile sink depletes less energy and remains alive
longer.
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14. Mobile Sink : Deployment Challenges
 Cost
 Data Collection Strategy
 Mobility Planning
 Random mobility
 fixed mobility,
 Controlled mobility
 Sojourn Locations and sojourn times
 Sojourn Tour
 Number of sinks
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15. Study Objectives
 Imposing delay constraint with mobile sink to increase network life
time.
 Computation of variable sojourn time so that sink stop at the
sojourn location till the data transmission.
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16. LITERATURE SURVEY
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17. Literature Survey
Sr.
No.
Yea
r
Author Description
1 2000 W.R.
Heinzelman
et.al
Proposed a Energy-Efficient Communication
Protocol for Wireless sensor Networks for cluster
Head selection called LEACH
2 2003 Perkins,
Belding Royer,
Das, S
Proposed routing protocol for route discovery
for ad-hoc networks called AODV. This is good
for instant route discovery
3 2003 S. R.
Gandham
et al.
Proposed a model to determine location of
Multiple mobile sink to minimize the energy
consumption.
4 2004 Fatma A.
Karkory,
Ali A.
Abudalmola
Proposed optimization technique to solve the
TSP using nearest neighbor problem
and minimum spanning tree.
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18. Literature Survey
Sr.
No.
Year Author Description
5 2005 J. Luo and
J-P Hubaux
Proposed sink movement path that should be
external perimeter of circle rather than sink
stationary position at centre.
6 2005 I.
Papadimitriou
and
L. Georgiadis
Proposed a model for movement of mobile sink
that can visit a small number of location that
increases the life time with optimizing routing
of traffic but did not consider the delay.
7 2006 J. Luo
et. al.
Proposed a method to calculate exact sojourn
time at each location and filtered out that
location where sojourn time is less than
threshold.
8 2008 S. Basagni et.
al.
Proposed a system to calculate maximum
distance between two consecutive movement
and sojourn time at each location
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19. Literature Survey
Sr.
No.
Year Author Description
9 2008 Y. Shi and
Y. T. Hou
Proposed the optimal sink movement , sojourn
location and schedule of visit Without delay.
10 2010 Young
Sang Yun
et al
Proposed the model to maximize life time by
using fixed delay that also called DTN.
11 2010 Weifa
Liang
et.al.
Proposed an MILP formulation to prolong
network life time by maximizing minimum
sojourn time. But the problem with model is
that sojourn time is always increasing
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20. Review Extract
 Research isn't made to impose delay constraint with sink mobility.
 Earlier work is carried out with fixed delay.
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21. Our Contribution
 Our routing algorithm uses the general wireless sensor network
architecture considering delay constraint with sink mobility.
 Routing decision is taken by the mobile sink and Cluster Head of
sensor node on the basis of the residual energy.
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22. PROPOSED WORK
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23. Problem formulation
• Sink is moving on predefined trajectory path L.
• It collects data from cluster head.
• Data Generation rate is ri bps
• Movement between two sojourn location should not be exceeded by R to
minimize loss of data , for this cluster head can buffer the data or sub flow
of all sensor node.
• Sojourn time on each stop is variable. It stops for a minimum duration tmin
and maximum duration tmax.
• Cluster head selection is based on LEACH protocol.
• Sojourn tour delay is D which includes sojourn time
• Δi is propagation delay
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24. 7/30/2015 MD ARQUAM 24
Problem formulation
Minimize
Subjected to
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25. Problem formulation(Continue…)
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26. Energy Model
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consumption
eele =energy expended in radio electronics
εfs =energy consumed in free space
Energy Model is given by TUDOSE

27. Solution of Model
 Calculating Optimal Sojourn tour of mobile sink :-Find an optimal
sojourn tour based on Travelling Salesman problem.
Optimal sojourn tour is computed by solving TSP using Nearest
Neighbor problem.
 Computation of Sojourn Time: Mobiroute modified so that data is
collected from all the locations.
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28. Nearest Neighbor problem
It is perhaps the simplest and straightforward TSP heuristic, which is
normally close to the optimal route, and it does not take too much
time to execute. The key to this algorithm is to always visit the
nearest node. Select a starting point, as long as there are nodes
that have not been visited yet, visit the nearest node that still has
not appeared in the tour, and finally return to the starting node.
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29. Nearest Insertion Algorithm
The basics idea of this algorithm is to select the shortest route, and
make a sub tour of it, then select a node that is not in the sub tour,
having the shortest distance to any one of the node in the sub tour.
Find a shortest tour in the sub tour such that the cost of inserting the
selected node between the tour’s nodes will minimal. Repeat the
selection of the node until no more nodes exist
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30. Tour Improvement Algorithms
• The tour construction is a greedy approach based heuristic. The part
of the tour, which is already constructed, remains unchanged during
the tour construction process. No any attempt is made to change
the tour that has been constructed. This is in contrast to the tour
improvement heuristic which changes the configuration of the tour
during the iterative improvement process until a short tour is found
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31. TSP solution
• TSP optimal solution by using approximation algorithm by Nearest
Neighbor Algorithm.
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Graph showing TSP

32. TSP solution
• Pick the start vertex in the tour, which is for example N1.
• Mark it as visited ,store it in the Linked List L.
• Iterating over N1’s neighbors (N2,40),(N3, 70), (N4,30) ,(N5,10)
• To get the edge that has the minimum weight ,which is (N5,10)
• Store the node N5 in the collection and mark it as visited.
• During the next iteration on the N5’s neighbors that has not been in the
collection < N2, 60>, <N3, 110>, <N4, 20>the node N4 will be stored in
the collection.
• During the next iteration over N4’s neighbors (N2, 100), (N3, 30), the
node N3 will be stored on the collection.
• Finally on the iteration over N2’s neighbors, which is, the only one left on
the graph that has not been Visited, Node N2 will be stored on the
collection. Going back to the start vertex N1 we complete the tour of the
TSP.
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33. Calculation of Sojourn Time
• In general packet delivery time is calculated by below given formula
Packet delivery time =Transmission time + Propagation delay
Therefore calculate the sojourn time at each sojourn point
according to the power consumption profile
• Luo and et.al. suggest the model to compute exact sojourn time at
each location by following MILP
Where T is total sojourn time
Subjected to
Where P is energy consumed at time Ti
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itT

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i iiTP

34. Algorithm
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1. Initially sink move with predefined path and stop each sojourn
location. It stops for the tmin time to broadcast its ID, its sojourn
location, its energy level and broadcast address.
2. Initially CH is in sleeping condition, when received sink ID it activates
and send its CH_ID, residual energy level , unicast address as well as
aprox. time of data transfer but not greater than tmax .
3. After connection establishment data is transferred. And sink moves to
next location.
4. If data transfer takes more time than tmax then sink terminates the
connection and moves to next sojourn location.
5. If any event happens suddenly then cluster head uses multihop
communication to forward the event information to next cluster head
which is nearer to Mobile Sink.

35. SIMULATION and RESULT
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36. Simulation Result
 Parameters:-
• Area of sensing field:-200mx200m
• Initial Energy of node:- 50Jule
• Initial location of sink:-(630,150)
• Transfer rate between CH and sink is 20Kbps
• Data generation rate of sensor:-200bps
• Transmission range of sensor:- 25 meter.
• Maximum Length of sojourn Tour:- 628m
• Maximum distance between two sojourn point:-40m
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37. Comparison of DCRP v/s MILP
Name of Parameter DCRP MILP
Average Throughput[kbps] 197.55 162.03
Average Delay 89.598 287.293
Packet Delivery Ratio 0.9999 0.8084
Total Energy Consumption 204.125 187.049
Avg Energy Consumption 5.1475 6.44996
Overall Residual Energy 1298.47 1262.95
Avg Residual Energy 45.457 43.55
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38. Performance Evaluation
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Graph showing Life time
Number of Round
NumberofDeadNodes

39. Performance Evaluation
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Graph showing Energy Consumption
Number of nodes
EnergyConsumption

40. Performance Evaluation
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Graph showing delay
Delay
Number of nodes

41. Performance Evaluation
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Graph showing Throughput
Number of nodes
Througput

42. CONCLUSION
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43. Conclusion
• We have stated how to design sojourn tour and use variable sojourn
time to bound delay.
• Experimental results show that the proposed model(DCRP) is better
than MILP.
• We have used the proposed model with clustering with mobile sink,
while earlier work was done by using gateway node.
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44. References
[1] Gupta, C. P., and Arun Kumar. "Wireless Sensor Networks: A
Review."International Journal of Sensors Wireless Communications and
Control 3.1 (2013): 25-36.
[2] Akyildiz, Ian F., et al. "Wireless sensor networks: a survey." Computer
networks 38.4 (2002): 393-422.
[3] Ganesan et.al., Networking issues in wireless sensor networks, J. Parallel
Distrib. Comput. 64 (2004) 799–814.
[4] Ahmed, Nadeem, Salil S. Kanhere, and Sanjay Jha. "The holes problem in
wireless sensor networks: a survey." ACM SIGMOBILE Mobile Computing and
Communications Review 9.2 (2005): 4-18.
[5] Horst F. Wedde and Muddassar Farooq. “A Comprehensive review of nature
inspired routing algorithms for fixed telecommunication networks” Journal of
Systems Architecture, Vol. 52, 2006, pp. 461- 484.
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45. References
[6] Luo, Jun, et al. "Mobiroute: Routing towards a mobile sink for improving
lifetime in sensor networks." Distributed Computing in Sensor Systems
(2006): 480-497.
[7] Frederick Ducatelle, Gianni Di Caro and Luca M. Gambardella. “AntHocNet:
An Adaptive Nature-Inspired Algorithm for Routing in Mobile Ad Hoc
Networks”. European Trans. On Telecommunications, Vol. 16, 2005, pp. 443-
455.
[8] Urgaonkar, Rahul, and Bhaskar Krishnamachari. "FLOW: An efficient
forwarding scheme to mobile sink in wireless sensor networks." Proceedings
of ACM SECON (2004).
[9] Song, Liang, and Dimitrios Hatzinakos. "Architecture of wireless sensor
networks with mobile sinks: Sparsely deployed sensors." Vehicular
Technology, IEEE Transactions on 56.4 (2007): 1826- 1836.
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46. References
[10] Li, Xu, Amiya Nayak, and Ivan Stojmenovic. "Sink mobility in wireless
sensor networks." Wireless Sensor and Actuator Networks (2010): 153.
[11] Yang, Tao, et al. "Impact of Mobile Sink for Wireless Sensor Networks
considering Different Radio Models and Performance Metrics." Broadband,
Wireless Computing, Communication and Applications (BWCCA), 2010
International Conference on. IEEE, 2010.
[12] Jordan, Edward, Jinsuk Baek, and Wood Kanampiu. "Impact of mobile
sink for wireless sensor network." Proceedings of the 49th Annual
Southeast Regional Conference. ACM, 2011.
[13] Liu, Wang, et al. "Performance Analysis of Wireless Sensor Networks
With Mobile Sinks." Vehicular Technology, IEEE Transactions on 61.6
(2012): 2777-2788.
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47. References
[14] Rasheed, “Security Schemes for Wireless Sensor Network with Mobile
Sink” PhD Thesis, http://repository.tamu.edu/bitstream/ handle/1969.1/ETD-
TAMU-2010-05-7844/rasheed-dissertation.pdf? Sequence=3.
[15] Jerew, Oday, Kim Blackmore, and Weifa Liang. "Mobile Base Station and
Clustering to Maximize Network Lifetime in Wireless Sensor Networks."
Journal of Electrical and Computer Engineering2012 (2012).
[16] Di Francesco, Mario, Sajal K. Das, and Giuseppe Anastasi. "Data collection
in wireless sensor networks with mobile elements: A survey." ACM
Transactions on Sensor Networks (TOSN) 8.1 (2011): 7.
[17] Majid I. Khan, Wilfried N. Gansterer, Guenter Haring “Static vs. mobile
sink: The influence of basic parameters on energy efficiency in wireless
sensor networks” Computer Communications, Available online 7 November
2012 http://dx.doi.org/10.1016/ j.comcom.2012.10.010
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48. Thank You
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