Mobility model for convex areas

Obstruction Avoidance Generously
Mobility (OAGM) a new Obstacle
Mobility Model Based on Graph-Theory
17-Apr-2014 V.Vasanthi-10JLDRCS002 1
Research Scholar:
V.Vasanthi
10JLDRCS002
Dept. Computer Science
Karpagam University
Research Guide:
Dr. M. Hemalatha
Prof. Dept. Computer
Science
Karpagam University

 Introduction
 Aim and Objectives
 Background(Literature Review)
 Methodology
 Results and Discussions
 Conclusion
 Future work
 References
17-Apr-2014V.Vasanthi-10JLDRCS002 2

 Ad-hoc and Sensor Network
• It is a self-configuring of nodes connected by wireless
link
• It forms an arbitrary topology
• It is distributed sensing and processing in wide range of
applications
• It consists of new concepts and optimization problems
openly
 Mobility Model
• plays a vital role in movement
• dictates to the nodes their initial places and movement
patterns
• emulate real-life Scenarios

Contd..
Aspects of Mobility Models
 User friendly
 Sufficient and easy to understand
 Mathematical properties
 Scope and Validity
 Realistic model(i.e)
It is not restricted in pre-defined pathways. The movement
pattern of the nodes in a natural way. The types of
Environments such as Urban, Social, emergency services
like fire station, healthcare etc.
Mobility model is divided into sub-models are
 Environmental model
 Movement pattern model
 Signal Blocking model

Simulation- NS2
- discrete event simulator developed by
VINT
project
- approach which can be used to model
large
- complex stochastic Systems
- performance measurement purposes

To Create a new movement model
 Movement patterns of all types of nodes that are
suitable for any environment without any predefined
paths in Ad-hoc Wireless sensor Network
 Incorporate obstacles
 Construct realistic movement (i.e) all types of Real
Environment
 Determine movement pattern, signal blocking and
environment regions created by obstacles

– The Existing Mobility Model mainly focused on movement
patterns of the nodes that are suited for limited
Environments with predefined paths.
– Few existing models does not consider obstacles. In
Obstacle Mobility model a Pre-defined pathways are used to
analyze the movement patterns.
– Mission Critical Mobility(MCM) model the nodes movement
in the simulation terrain without restrictions where the edge
detection is followed.
– To solve the above problems a new model was proposed by
using graph theory technique for movement patterns of all
types of nodes that are suitable for any environment without
any predefined paths.

 Survey of Existing Models
 Performance Analysis of existing Mobility Models
 Design of a new Realistic obstacle based mobility
Model (OAGM)
• Performance analysis of Proposed model.

Classification of Mobility Models
5
3
2

Mobility Models Type Movement pattern
Random Walk Mobility Model
(Zonoozi & Dassanayake)
Entity model
Randomly chooses S/D with
TI
Random Waypoint Mobility Model
(Johnson)
Entity model
Select the destination
randomly and distributed
SPD
Random Direction Mobility Model
(Johnson)
Entity Model Change S/D in time Slot
Realistic mobility model
(A.Kamal, J.AI-Karaki)
Entity Model
S/D follows Distributed
Nodes
A Boundless Simulation Area Mobility
Model
(Z.Hass)
Entity Model Pre-S/D follows with new

Gauss-Markov Mobility Model
(Z.Hass)
Temporal
dependency model
Different level of parameters
City Section Mobility Model
(V.Davies )
Temporal
Dependency
Street in a city -Realistic movement
Reference Point Group Model
(X. Hong, M. Gerla, G. Pei, C. –
C. Chiang)
Group Model Group leader-Member
Column Mobility Model
(Sanchez)
Group Model Straight line- change in time slot
Pursue mobility model
(Sanchez)
Group Model Chance the target
Nomandic Community mobility
model (Sanchez):
Group Model Common reference point
Manhattan mobility model
(F.Bai , sadagopan, A.helmy)
Geographic/Realist
ic model
Vanet-Urban area-vertical/Horizontal

Obstacle Mobility Model
( A.Jardosh)
Realistic/Geographic
Restriction model
pre-defined path(Voronoi
diagram-obstacle)
Pathway mobility model
( J.Tian )
Realistic/Geographic
Restriction model
Predefined edges –street and
pathways
Freeway mobility model
( F.Bai ,N.sadagopan, A.helmy )
Geographic Restriction
model
Lane of a freeway
Environment mobility model
( H.Babaei )
model
Geometric and non-geometric
with different factors
Obstacle aware mobility model
( S.Ahmed ):
model
Anchor concept, Not consider
Ad-hoc
Obstacle based on social networks
( P.Venkateswaran)
model
Social network-with obstacle
Mission Critical mobility model
( C.Papageorgiou)
model
Add-on of OM model-
Emergency, health care etc.

Proactive protocols
◦ Traditional distributed shortest-path protocols
◦ Maintain routes between every host pair at all times
◦ Based on periodic updates; High routing overhead
◦ Example: DSDV (destination sequenced distance vector)
Reactive protocols
◦ Determine route if and when needed
◦ Source initiates route discovery
◦ Example: DSR (dynamic source routing-Johnson96)
Hybrid protocols
◦ Adaptive; Combination of proactive and reactive
◦ Example : ZRP (zone routing protocol)

 Reactive or On Demand
 Developed at CMU in 1996
 Route discovery cycle used for route finding – on Demand
 Maintenance of active routes
 No periodic activity of any kind – Hello Messages in AODV
 Utilizes source routing (entire route is part of the header)
 Use of caches to store routes
 Supports unidirectional links -> Asymmetric routes are
supported

 Routes maintained only between nodes who need to
communicate
 reduces overhead of route maintenance
 Route caching can further reduce route discovery
overhead
 A single route discovery may yield many routes to the
destination, due to intermediate nodes replying from
local caches

Mobility
Models
Average
Connectivity
Graph
Protocol
Performance
Performance
Metrics
 Random waypoint(RWP)
 Reference Point Group Mobility(RPGM)
 Gauss-Markov Mobility model(GM)
 Manhattan Mobility Model(MHN)
 Mission Critical Model(MCM)
1.Generated Packets
2.Packet Delivery Ratio%
3.End to End Delay
4.Dropped data
5.Control Overhead
6.Received Packets
DSR
17-Apr-2014V.Vasanthi-10JLDRCS002
17
Performance of Different Mobility models

Duration 300s
Traffic Sources CBR, 512 byte packet, 4 packets per second
Transport protocol UDP
MAC protocol Mac/802.11
N/W interface Phy/wireless phy
Propagation model Two ray ground
Radius of node 250m
Antenna Omni Antenna
Area Size 1000m*1000m
Mobility Models RWP,MHN,RPGM,MCM,GM, OAGM
No of Nodes 50-250 (interval of 50)
Speed m/s 0-10m/s (interval of 2m/s)
Table: Simulation Parameter set

Performance metrics:
1.Generated Packets: The Number of packets send.

No of
Nodes
50 100 150 200 250
No of
Packets
generated
3480 5798 9272 11586 13898
Simulation Results
Here, all the mobility models use the nodes 50-250
(with the interval nodes of 50) with different Speed
0 to 10 ms with the time interval of 2ms (maximum
speed = 10 m/s). The Generated Packets (GP)
remains same even in the change of number of
Speed varies.

The ratio of the data packets delivered to the destinations
to those generated by the sources. Mathematically, it can
be expressed as:
where p is the Ratio of successfully delivered packets, c
is the total number of flow or connections, f is the unique
flow id serving as index, Rf is the count of packets
received from flow f and Nf is the count of packets
transmitted to f.

This includes all possible delays caused by buffering during route
discovery latency, queuing at the interface queue, retransmission
delays at the MAC, and propagation and transfer times. It can be
defined as:
where D is the number of successfully received packets, i is
unique packet identifier, ri is time at which a packet with unique
id i is received, si is time at which a packet with unique id i is sent
and D is measured in ms. It should be less for high performance.
Performance metrics

The ratio of the data packets not delivered to the
destinations to those generated by the sources.
Mathematically, it can be expressed as:
where DP is the Number of Dropped Packets, i is unique
packet identifier, ri is time at which a packet with unique
id i is received, si is time at which a packet with unique
id add with it and N is the number of connections, flows,
i is sent .
Performance metrics

5. Control Overhead(CO)
The control overhead is defined as the total number
of control packets (i.e Nf) exchanged successfully.
Performance metrics

6.Received Packets (RP)
It is defined as number of packets received to the
destination successfully. It is declared as Rf i.e
count of packets received from flow f
Performance metrics

 Random Models are not realistic.
 Group models will take more time to reach
Destination from source.
 Geographic Restriction Models use obstacle by
assumption in the simulation terrain which is not
realistic.
 Obstacle models are restricted in pre-defined
pathways.
 The MCM model nodes moves to the destination
through the edges of obstacles.
 These models are not in real-life trace. The MCM
model is best suited only for emergency and health-
care. In these models PDR is low result and End to
End delay high variance.

 Networks can be represented by graphs
 The mobile nodes are vertices
 The communication links are edges
Vertices
Edges

 In this model the Features are as follows
1. Node Movement process
2. Hierarchical node organization
3. Physical obstacle placement
4. Source selection and Destination selection

Step1 : Placement of Obstacle i.e. Rectangle or Square
Step2 : Placement of nodes randomly
Step3: Select the nodes initial point and obstacle position are
stored in files
Step4 : Movement process using graph theory(Hybrid bellman-
ford Dijkstra )
Step5 : Selecting min and max Speed
Step6 : Shortest path is finding and then repeat until it
reach the Destination
Step7: Check whether obstacle is available if not reach
the destination
Step8: If is obstacle is available then step 4
Step 9: Till Simulation time ends
Step 10: Stop process

Algorithm1: The Movement Node Process
i = 0
Ci S
While there is an obstacle between Ci and D do
if ||D - Ni1|| ≤ ||D - Ni2|| then
if Mindis(V1,V2) = =1
Q1=Rp(V)
Else
Ni1V (Mindis[V1|V2])
Qi Ni1
else
QiNi2
end if
Q  Q + {Qi}
Ci+1 Qi
i  i + 1
end while
Qi D
RETURN Q
Features of the Proposed OAGM
1.Node Movement Process

Figure 1: An example of how a node moves towards its destination point
around the obstacles in the network area according to the Proposed mobility
model.

Figures: An example of how a node moves towards its destination point around the
obstacles in the network area according to the Proposed mobility model.

Hybrid Bellman ford algorithm to find the
shortest path
Initialization
d(v) ∞← , for all v є V
π(v) ← nil, for all v є V
d(s) ← 0
Relax(u, v)
if d(u) + c(u, v) < d(v)
d(v) ← d(u) + c(u, v)
π (v) ← u
Plain scan
for each edge (u, v) є E
Relax(u, v)
Dijkstra scan
S ← є
while (there is a vertex in V S with d < ∞) do
find vertex u in V S with the minimal value of d
S ← S {є u}
for each edge (u, v) ∈ E /* scanning u */
Relax(u, v)
Dijkstra(G, s)
Initialization
Dijkstra scan
return(d, )
Bellman-Ford(G, s)

Initialization
i ← 0
do
i++
Plain scan
until ((there was no change of d at Plain scan) or (i = |V |))
if (i < |V |) return(d, )
else return(”There exists a negative cycle reachable from s.”)
Algorithm Bellman-Ford-Dijkstra (BFD) is as follows:
Bellman-Ford-Dijkstra (G, s)
Initialization
i ← 0
do
i++
Dijkstra scan
until ((there was no change of d at Dijkstra scan) or (i = |V | − 1))
if (i < |V | − 1) return(d, )
else return(”There exists a negative cycle reachable from s.”)
Notice : BFD may be considered as a particular version of BF, since at each
round, Relax is executed on all edges reachable from s.

2.Hierarchical Node Organization
•The nodes are organized in groups with a pre-
defined leader/group.
•GS Each group contains certain no of nodes.
•GS is a parameter that can be act based on specific
characteristics of the scenarios.
•Each member group is set the Destination selection
and a point within a constant distance from its leader’s
destination point referred as distance and begins
towards it.

 The obstacle can be placed anywhere inside the
simulation area.
 The shape normally assumed is rectangle or square.
 We select the four corners as the block edges and
store them in a file to be used during mobility
generation.
 The obstacle has to be placed before we place the
nodes in their initial position.

 The source and destination nodes are selected
randomly from the total no. of nodes simulated.
 We have taken approximately 5% of nodes in
communication at any given time during the
simulation interval.
 Total 10% of the nodes will be either source or
destination and remaining nodes will work as
forwarding nodes.

Results of Proposed OAGM model
Nam File :Node-50-250 with obstacle

1.Packet Delivery Ratio (PDR)
in terms of Percentage(%)
No of
Nodes
Speed
2 m/s
Speed
4 /s
Speed
6 m/s
Speed
8 m/s
Speed
10
m/s
50 99.9425 98.3046 97.7874 97.5 98.2759
100 99.8103 98.9997 98.7237 97.0852 97.8441
150 99.299 93.5828 84.215 97.977 10.2351
200 60.2106 34.4813 43.5526 5.57569 2.64112
250 15.2166 4.48266 4.49705 1.8276 0.7627

2.End to End Delay (ED)
in terms of Milli Seconds(m/s)
No of
Nodes
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed
10 m/s
50
16.59 19.623 60.9207 72.4227 31.0448
100
22.7325 17.9425 44.0934 23.5896 39.7311
150
47.2069 230.437 72.9717 52.3945 131.1
200
572.687 120.992 35.4563 226.012 101.117
250
96.1891 107.408 72.2491 180.103 111.06

3. Dropped Packets (DP)
in terms of Packets
No of
Nodes
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed
10 m/s
50
0 35 53 70 40
100
8 43 63 148 112
150
60 572 1452 172 8310
200
4886 7574 6516 10925 11268
250
12572 13259 13256 13629 13778

4.Control Overhead (CO)
in terms of Packets
No of
Nodes
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed
10 m/s
50
105 1058 2015 1867 2001
100
555 2164 3377 4990 3946
150
2660 13888 19878 7320 280861
200
63436 229250 162985 590990 648099
250
563427 687386 717255 874759 1039263

5. Received Packets (RP)
in terms of Packets
No of
Nodes
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed
10 m/s
50
3480 3445 3427 3410 3440
100
5790 5755 5735 5650 5686
150
9212 8700 7820 9100 962
200
6700 4012 5070 661 318
250
1326 639 642 269 120

17-Apr-2014 V.Vasanthi-10JLDRCS002 45
Packet Delivery Ratio - Nodes 50 & 100
ModelsSpeed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 99.6839 98.6782 98.5345 96.3506 94.5115
MHN 57.4713 87.2414 84.6552 88.5345 83.3908
RPGM 80.1437 49.7414 88.5345 46.8391 44.9713
RWP 99.569 98.6494 96.7816 92.9598 95
MCM 99.9425 98.3046 97.7874 97.5 98.2759
Proposed
OAGM 100 98.9942 98.797 97.988 98.8505
GM 98.9307 97.5681 97.3094 79.1652 95.05
MHN 97.5681 60.5726 84.5981 41.9283 29.5964
RPGM 91.1866 96.6368 94.3601 61.0383 53.484
RWP 94.4636 98.1373 77.3888 93.7047 94.2221
MCM 99.8103 98.9997 98.7237 97.0852 97.8441
Proposed
OAGM
99.862 99.2583 98.9134 97.4473 98.0632

17-Apr-2014
V.Vasanthi-10JLDRCS002 46
Packet Delivery Ratio - Nodes 150 & 200
ModelsSpee
d
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 99.3097 40.962 28.5375 19.6721 23.1881
MHN 16.9866 30.6838 4.54055 11.7882 3.50518
RPGM 98.3391 97.9832 28.1385 22.9724 9.72821
RWP 15.2286 67.4612 41.2748 24.9569 13.0069
MCM 99.299 93.5828 84.215 97.977 10.2351
Proposed
OAGM
99.3528 93.8308 84.3399 98.1449 10.3753
GM 2.76195 2.0801 1.05299 2.5807 1.17383
MHN 11.1514 6.40428 1.90747 3.25393 5.09235
RPGM
7.31918 3.6337 1.5536 0.768168 0.258933
RWP
15.5533 3.66822 4.01346 2.9691 12.4115
MCM 60.2106 34.4813 43.5526 5.57569 2.64112
Proposed
OAGM
57.8284 34.6279 43.7597 5.70516 2.74469
1-sep-2012

Packet Delivery Ratio – Nodes 250
ModelsSpe
ed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 2.71262 2.18017 2.93567 0.928191 0.582818
MHN 10.6418 1.39588 2.53274 1.57577 0.712333
RPGM 2.21615 0.215858 2.64067 0.366959 0.539646
RWP 3.0436 6.47575 0.143906 3.07958 0.151101
MCM 15.2166 4.48266 4.49705 1.8276 0.7627
Proposed
OAGM
9.5409 4.59778 4.61936 1.93553 0.86343

Contd.,
48
Avg End to End Delay- Nodes 50 & 100
ModelsSpeed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 13.9736 13.9353 13.8526 43.5703 183.149
MHN 1.19682 86.5941 94.05 47.4315 82.6999
RPGM 0 0 27.876 0 0
RWP 10.1589 19.8505 16.314 203.267 166.994
MCM 17.059 20.623 65.9207 76.4227 34.0448
Proposed
OAGM 16.59 19.623 60.9207 72.4227 31.0448
GM 53.9017 120.265 164.505 138.261 193.137
MHN 90.7024 194.59 45.3421 41.2104 53.8812
RPGM 602.06 109.977 20.2429 0 162.487
RWP 64.9215 61.4054 31.3641 109.851 142.677
MCM 26.6325 20.9425 49.0934 28.5896 42.5311
Proposed
OAGM 22.7325 17.9425 44.0934 23.5896 39.7311

Avg End to End Delay- Nodes 150 & 200
ModelsSpeed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 35.6382 28.9812 55.5902 189.594 92.8308
MHN 434.395 273.517 125.39 229.668 68.162
RPGM 91.0513 96.8685 46.879 218.415 110.119
RWP 767.155 65.1344 119.009 163.428 79.2614
MCM 50.4069 237.337 77.6717 58.2945 138.8
Proposed
OAGM 47.2069 230.437 72.9717 52.3945 131.1
GM 352.245 183.359 1844.37 357.489 109.346
MHN 108.664 143.324 145.84 141.862 118.817
RPGM 239.495 147.135 336.254 216.127 771.9138
RWP 238.449 126.748 266.723 191.723 250.348
MCM 576.887 124.842 40.0563 230.31 105.717
Proposed
OAGM 572.687 120.992 35.4563 226.012 101.117
Contd.,

Avg End to End Delay- Nodes 250
ModelsSpeed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 37.2977 197.2 77.2596 276.34 52.1666
MHN 358.323 124.226 130.495 174.786 40.025
RPGM 59.4544 61.411 236.92 146.368 46.9833
RWP 263.826 119.906 27.136 83.7661 240.532
MCM 345.534 111.504 78.7249 184.903 115.26
Proposed
OAGM 96.1891 107.408 72.2491 180.103 111.06
Contd.,

17-Apr-2014 V.Vasanthi-10JLDRCS002
51
51
Dropped Packets- Nodes 50 & 100 in terms of Packets
ModelsSpee
d
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 11 47 51 128 190
MHN 1477 427 534 357 573
RPGM 627 1719 367 1828 1908
RWP 15 47 114 249 173
MCM 2 57 72 87 60
Proposed
OAGM 0 35 53 70 40
GM 61 140 156 1199 288
MHN 140 2241 890 3331 4069
RPGM 478 181 320 2219 2656
RWP 319 110 1301 362 324
MCM 11 59 74 167 127
Proposed
OAGM 8 43 63 148 112
Contd.,

52
Dropped Packets- Nodes 150 & 200 in terms of Packets
ModelsSpeed
Speed
2 m/s
Speed
4 m/s
Speed
6 m/s
Speed
8 m/s
Speed 10
m/s
GM 66 5365 6521 7246 7004
MHN 7648 6293 8627 8030 8587
RPGM 152 185 6509 7109 8084
RWP 7690 2952 5214 6871 7780
MCM 65 564 1459 182 8089
Proposed
OAGM 60 572 1452 172 8310
GM 11203 11136 11091 10891 10893
MHN 10115 10573 10907 10848 10450
RPGM 10382 10606 10996 11079 10921
RWP 9529 10724 10716 10788 10020
MCM 4568 7402 6469 10556 10738
Proposed
OAGM 4886 7574 6516 10925 11268
Contd.
,

Packet Delivery Ratio(PDR%)

Contd.
,

V.Vasanthi-10JLDRCS002 55
End-to End
Delay

Contd..

Control
Overhead

Contd.,

Received
Packets

Contd.,

Dropped Packets

Contd.,

– Existing mobility models like obstacle mobility model
forces the nodes to move in a predefined pathways still
some pathways will result in congestion.
– The Mission Critical Model(MCM) is a realistic model
that are restricted to the environment like Health Care
and Emergency services.
– The proposed Obstruction Avoidance Generously
Mobility(OAGM) model is realistic too, and can able to
place obstacle any where in the simulation terrain in
user friendly manner and suitable for any environment.
– The Overall Performance Result of this model gives
higher percentage of 2% than MCM mobility model.

Future Work
• This research work can be enhanced by
detecting the shapeless obstacle.
• The study of effects of mobility with different
routing Protocols.

I thank Karpagam University and Karpagam trust
members for doing my research at this
university with award of KURF supported Grant
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Network Connectivity, Lifetime and Hop Count of Routes for Mobile Ad hoc
Networks” Journals of Networks 5(5),may2010 pp..
 Christos Papageorgiou at el,2012.Modeling human mobility in obstacle-
constrained ad hoc networks” Ad-hoc Networks 10 pp.421-435
 Youssef Saadi et al, 2012 Simulation Analysis of Routing Protocols using
Manhattan Grid Mobility Model in MANET, International Journal of Computer
Applications 45 (23), pp.24-30.
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Report CS-10-04,Ben-Gurion University of the Negev, 2010.
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 Vasanthi.V and Hemalatha.M, 2013. Simulation of Obstruction Avoidance
Generously Mobility (OAGM) Model using Graph-Theory Technique. Research
Journal of Applied Sciences, Engineering and Technology.5(07):2799-2808. ISI
Thomson-Scopus Indexed
 Vasanthi.V and Hemalatha.M, 2012. Simulation and Evaluation of different mobility
model using DSR protocol Ad-hoc Sensor Network Using Bonnmotion Tool. Recent
Trends in Computer Networks and Distributed Systems Security SPRINGER LNCS
335:157-167.
 Vasanthi,V and Hemalatha.M, 2012. Mobility Scenario of Dissimilar MobilityModel
using the DSR protocol in Ad-hoc Sensor network-A Survey. Spl Issue of Int. J.
Computer Applications 1(4):1-14. Impact Factor:0.814
 Vasanthi.V and Hemalatha.M, 2012. A Proportional Analysis of Dissimilar Mobility
Models in Ad-Hoc Sensor Network over DSR Protocol. Int. J. Computer Applications
42(15): 26-32.
 Vasanthi.V, Ajith Singh.N, Romen Kumar.M and Hemalatha.M, 2011. A Detailed
Study of Mobility Model in Sensor Network. Int. J. Theoretical and Applied
Information Technology, 33(1): 7-14 (Impact Factor: 1.71 Scopus Indexed).
 Vasanthi.V and Hemalatha.M, 2011. Empirical Study on Security Attacks in Wireless
Sensor Network. Int. J. Advanced Research in Computer Science, 2(1):23-28.
 Vasanthi.V, Nagarajan.P, Bharathi.B and Hemalatha.M, 2010. A Perspective
Analysis of Routing Protocols in Wireless Sensor Network. Int. J. Computer Science
and Engineering 2(8):2511-2518.

International and National Conference
 Vasanthi. V and Hemalatha.M, 2013. Obstruction Avoidance Generously Mobility Model for Adhoc
and Sensor Networks- International Conference on Computer Science and Information Technology
(ICCSIT), Coimbatore, Feb 17.IOAJ sponsor
 Vasanthi.V and Hemalatha.M,2012. Mobility Scenario of Dissimilar Mobility Model using the DSR
protocol in Ad-hoc Sensor network-A Survey –ICNICT2012 –Sri Krishna Institute of Engineering
and Science. Sep.7 & 8.pg.no 15-21.
 Vasanthi.V and Hemalatha.M,2012. Simulation and Evaluation of different mobility model using
DSR protocol Ad-hoc Sensor Network Using Bonnmotion Tool -SNDS 2012-Indian Institute of
Information Technology and Management – Kerala. Springer Proc. Int. Conf. Recent Trends in
Computer Networks and Distributed Systems Security CCIS (335); pp 157-167 .
 Vasanthi.V and Hemalatha.M, 2012. A Review on Path Planning Algorithms for Ad-Hoc Wireless
Sensor Network. National Conference on Simulations in Computing Nexus (NCSCN 12),
Coimbatore Institute of Engineering & Technology, Narasipuram, Thondamuthur(via), Coimbatore.
Mar. 2.
 Vasanthi.V and Hemalatha.M 2011. Impact of Different Mobility Models using DSR Protocol for
Wireless sensor Networks. Proceeding of Int. Conf. on Networks Intelligence and Computing
Technologies (ICNICT 2011), Department of Computer Science, Karpagam University, Coimbatore
pp: 580-583, Dec 15-16.
 Vasanthi,V, Ajith Singh.N, Romen Kumar.M and Hemalatha.M, 2011. Evaluation of Protocols and
Algorithms to improve the performance of Tcp/Ip over Wired/Wireless networks. Int. Conference
on Computational Intelligence and Information Technology (CIIT), Pune. Nov. 7&8. Springer
LNCS, Communication in Computer and Information Science, 250(1):521-525 (Scopus Indexed)

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