6. Design of an Optimized Multicast Routing Algorithm for Internet of Things

Abstract

Internet of Things (IoT) is a fast-growing technology in ongoing research field that includes wireless sensor networks, cloud computing, big data analytics, ubiquitous computing, distributed decentralized systems, pervasive computing, embedded systems, mobile computing, machine learning etc. The above mentioned fields are mainly connected with IoT smart portable devices such as smartphones, home appliances, healthcare device, smart vehicle devices automation industry devices, etc. Though IoT enabled devices has been increased in many fields, the industries still faces many problem with connectivity issues because of several factors like mobility nature of devices; limited processing power and resource availability which includes energy, bandwidth constraints, routing cost and end to end delay; communication between node to node via intermediate mobile nodes towards destination may also fail links frequently, there by affecting the network performance. These limitations of existing topology based on reactive tree and mesh based routing protocols create challenging task while designing an optimized stable routing algorithm for IoT. In such a situation, resource optimization is an essential task to be performed by the IoT networks. In the proposed work resource optimization was done by Designed Optimized Multicast Routing Algorithm (DOMRA) for IoT. The DOMR algorithm implemented has route discovery process with nodes positions, directions of nodes, velocities of nodes, and then the path stability bases to overcome the connectivity issues. The proposed algorithm focusing to deploy various real time IoT enabled applications such as smart home automation, smart cites, smart agriculture, automation industry etc. To finalize the simulation results shows maximized system throughput, goodput, packet delivery ratio, network lifetime, network routing performance and reduced control overheads. The proposed algorithm hence produced better routing performance when compared with other existing algorithm in wireless networks. Index Terms: Internet of things, stabile path, optimal route, improve network life time.

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International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-8 Issue-2, July 2019
4048
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication
Retrieval Number: B3372078219/19©BEIESP
DOI: 10.35940/ijrte.B3372.078219
Abstract: Internet of Things (IoT) is a fast- growing technology
in on-going research field that includes wireless sensor networks,
cloud computing, big data analytics, ubiquitous computing,
distributed decentralized systems, pervasive computing, embedded
systems, mobile computing, machine learning etc. The above
mentioned fields are mainly connected with IoT smart portable
devices such as smartphones, home appliances, healthcare device,
smart vehicle devices automation industry devices, etc. Though
IoT enabled devices has been increased in many fields, the
industries still faces many problem with connectivity issues
because of several factors like mobility nature of devices; limited
processing power and resource availability which includes energy,
bandwidth constraints, routing cost and end to end delay;
communication between node to node via intermediate mobile
nodes towards destination may also fail links frequently, there by
affecting the network performance. These limitations of existing
topology based on reactive tree and mesh based routing protocols
create challenging task while designing an optimized stable
routing algorithm for IoT. In such a situation, resource
optimization is an essential task to be performed by the IoT
networks. In the proposed work resource optimization was done by
Designed Optimized Multicast Routing Algorithm (DOMRA) for
IoT. The DOMR algorithm implemented has route discovery
process with nodes positions, directions of nodes, velocities of
nodes, and then the path stability bases to overcome the
connectivity issues. The proposed algorithm focusing to deploy
various real time IoT enabled applications such as smart home
automation, smart cites, smart agriculture, automation industry
etc. To finalize the simulation results shows maximized system
throughput, goodput, packet delivery ratio, network lifetime,
network routing performance and reduced control overheads. The
proposed algorithm hence produced better routing performance
when compared with other existing algorithm in wireless
networks.
Index Terms: Internet of things, stabile path, optimal route,
improve network life time.
I. INTRODUCTION
Internet of things is a wireless and wired network
composed of mobile nodes or non-mobile nodes operated in
absence of infrastructure or infrastructure based networks
dependents on the environment [1]. There are no dedicated
routers, servers, access points, and cables.
Because of its speedy and convenient deployment;
robustness and low cost, an IoT can find its applications in
the following areas [2].
Revised Manuscript Received on July 15, 2019.
D. Kothandaraman1*, Department CSE, S R Engineering College,
Warangal, India.
M. Sheshikala2, Department CSE, S R Engineering College, Warangal,
India.
K. Seena Naik3, Department CSE, S R Engineering College, Warangal,
India.
Y. Chanti4, Department CSE, S R Engineering College, Warangal, India.
B. Vijaykumar5, Department CSE, S R Engineering College, Warangal,
India.
• Smart agriculture
• Home automation
• Smart cities
• Military use (e.g. a network in the battlefield)
• Investigation and rescue.
• Vehicle-to-vehicle communication in intelligent
transportation.
• Momentary networks in urgent business meeting, etc.
• Personal area networks connecting mobile phones, laptops,
smart watches, and other portable computers etc.,
In the present Design of an Optimized Multicast Routing
Algorithm (DOMRA) for the Internet of Things, routing
algorithm focus only wireless mobile nodes. If two nodes are
within in the transmission range, it can communicate with
each other directly; otherwise, the nodes select the alternate
path have to forward the packets. In such a case, every IoT
enabled mobile node has to function as a router to forward the
packets for others in the networks [18-19].
Traditional routing protocols used in hardwired or wireless
networks, such as distance vector protocols (e.g. Routing
Information Protocols) and link state protocols (e.g., Open
Shortest Path First) etc., cannot be applied in the IoT directly
for the following reasons:
• There may be uni-directional links between nodes.
• There is no more than one eligible path between two
nodes.
• The consumption of bandwidth and energy incurred by
periodic routing information updates.
• The routing topology change rapidly.
Most of the research effort has been put in the routing
protocols for IoT [3]-[4]. Topology-based routing protocols
can be divided into the following categories [5]:
1. Star based routing (Proactive routing)
2. Mesh based routing (Reactive routing)
In a review, each and every routing protocol has its strengths,
weakness, and aims at a specific application. As a result, the
prospective standard for the design to optimize multicast
routing algorithm in IoT is very likely to combine with some
other techniques. Also it gives better routing performance
when compared with the other routing algorithms [15].
II. RELATED WORKS
Existing various routing protocols in IoT, are compared as
follows: 1. Depth-First Forwarding (DFF) protocols (Packet
Delivery Ratio (PDR) is increased, End to End Delay (EED)
is average and Energy Consumption (EC) is average), 2.
Multipath Lossy and Low powered protocol (PDR is
increased, EED is Less and
EC is Average) [6], 3.
Energy Efficient Probabilistic
Routing Algorithm (EEPR)
Design of an Optimized Multicast Routing
Algorithm for Internet of Things
D. Kothandaraman, M. Sheshikala, K. Seena Naik, Y. Chanti, B. Vijaykumar

Design of an Optimized Multicast Routing Algorithm for Internet of Things
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DOI: 10.35940/ijrte.B3372.078219
(PDR is average and EC is decreased), 4. Avoidance
Multipath Routing Protocol (CA-RPL) (PDR is increased,
EED is decreased and EC is average), 5. Movement-Aided
Energy Balance (MAEB) (PDR is increased and EC is
decreased), 6. Least Path Interference Beaconing (LIBP)
(PDR is increased and EC is decreased), 6. Cognitive
machine-to-Machine RPL Protocol (CoRPL) (PDR is
increased, EED is Average and EC is average) [16-17],
[20-21].
In existing multicast routing protocols, wireless networks
concentrate for both independence and dependences
applications. Whereas in the present algorithm, focus is only
on the independence application based multicast routing
algorithm [9]. Mesh based routing approach is used to modify
the proposed algorithm and its main intention is to minimize
the control overhead, increase network lifetime, especially
battery enabled IoT mobile nodes [7]-[8].
An energy efficient routing protocol for wireless IoT
sensor networks has been implemented by clustering
mechanism which forms cluster head inside so that least
energy expensive path and efficient computation in real-time
routing has been created. Still path selection in IoT networks
cannot assure adequate network of lifetimes and sustained
sensing coverage [13], [16, 14].
Wireless routing protocols for IoT is a survey design
intercommunication protocol for different devices and it
dynamically support cloud computing environment for big
data. Performance measures used are transparency,
availability and privacy of big data [17]. Routing algorithm
design for supporting IoT network architecture using fog
computing is responsible for reducing the amount of data sent
to the cloud. This algorithm yields lower installation cost and
delays constraints unicast traffic under delay constraints
which is a severe problem [18].
Multicast Ad-hoc On-demand Distance Vector
(MAODV) protocols in each node maintains its routing table;
tree structure can be constructed more quickly and
efficiently, in the group leader then floods the hello,
messages to intermediates nodes toward the destination [10].
Ad-hoc multicast routing algorithm are channels created
continuously between pairs of group numbers then multicast
distribution tree which is constructed periodically on the
mesh links available, also it communicates using unicast
routing protocol approach [11]-[12].
Differential Destination Multicast (DDM) is unicast
routing protocol for forwarding the packets from source to
destinations with reduced control overhead on the multicast
routing structure which maintains the multicast routing
information by the intermediate nodes [19].
Tree Based Multicast Ad-hoc On-demand Distance
Vector (MAODV) routing protocols for working
mechanisms such as single path between the source to
destination and uses join tree messages to construct a tree and
created by each source as many numbers of trees as a source
[4].
Multicast Ad hoc On-Demand Distance Vector Routing
Protocol (MAODV) discovers multicast route on demand
based routing involves different stage such as Multicast
Route Discovery (RREQ packets), Reverse Path Setup
(RREQ packets), Forward Path Setup (RREP packets),
Multicast Route Activation (MACT packets), Group Hello
Messages (GRPH messages), and Mesh-Based Protocols
(ODMRP)[4].
Mesh based On Demand Multicast Routing Protocol
(ODMRP) floods join query packets once received
destination node then it join reply packets establish multicast
routes towards source node. Different stages for ODMRP
working mesh creation, join query process, and join reply
process [14].
While comparing MAODV with ODMRP: ODMRP has a
better packet delivery ratio than MAODV, ODMRP is more
robust than MAODV due to its minimal packet loss and
availability of multiple routes, ODMRP is less scalable
compared to MAODV as the number of senders or multicast
group size is increased and finally MAODV has minimal
control overhead as compared with ODMRP. In that both
protocols advantage and disadvantages are discussed. To
overcome the drawbacks in this case optimized multicast
routing algorithm in IoT has been proposed here.
III. PROPOSED APPROACH
The proposed algorithm is to design an optimal multicast
routing with stable path and shortest distance through
destination node as well as increased goodput, throughput,
reduced control overhead compared with other existing
routing protocols in wireless ad-hoc networks and IoT as
shown in fig. 1. In the proposed algorithm source node
initiate the route discovery process with nodes positions,
directions, and velocities of nodes, and then the path stability
continuously maintains the route discovery up to reach the
destination node. Otherwise, it is select to an alternate path to
reach the destination node which is optimal with a stable path
towards the destination node in the IoT networks.
Figure 1: Block diagram of optimized route discovery
process using IoT network
A. Algorithm for Optimal Route Selection in IoT
In the proposed DOMR algorithm is to detect the optimal
stable path using origin and destination matrix techniques as
shown in fig. 2.

International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-8 Issue-2, July 2019
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Retrieval Number: B3372078219/19©BEIESP
DOI: 10.35940/ijrte.B3372.078219
Figure 2: Origin and Destination (OD) matrix
Routing objective function is to minimize the routing cost Eq.
(1):
11
mn
ij ij
ij
Minimize d c


(1)
Constraints for optimal stable routing in IoT, Eq. (2) is An ith
nodes, Eq. (3) is jth nodes and in Eq. (4) dij is the shortest
distance in terms of routing cost for M*N matrix format in
the network:
1/ / ,( 1,2,.... )
m
ij i i i
id S IN D i m



(2)
1/ / ,( 1,2,.... )
n
ij j j j
jd S IN D j n



(3)
0;( 1,2,.... );( 1,2,.... )
ij
d i m j n  
(4)
Where:
dij  distance between ith and jth nodes
Cij Time taken between ith and jth nodes
Si / Sj = (ith, jth ) or Sm /Sn= (m, n) = Source nodes
INi / INj = ( ith, jth ) or INm/INn = (m, n) = Intermediate
nodes
Di /Dj = (ith, jth ) or Dm/Dn = (m, n)= Destination nodes
B. Routing Formulation using OD Matrix
Step 1: Sm/INm/Dm mth positions of the nodes
S1,S2, …., Sm,  Source nodes
IN1, IN2,.....,INm  Intermediate nodes
D1, D2,...., Dm  Destinations nodes
Step 2: Sn/INn/Dn  nth position of the nodes
S1,S2,......,Sn  Source nodes
IN1,IN2,......INn  Intermediate nodes
D1,D2,…..,Dn  Destinations nodes
Let be ai  0; i=1,2, ….m Time available at the
S1/IN1/D1,....,ith nodes.
Let be bj0; j=1,2,….n Time available at the
S1/IN1/D1,......, jth nodes.
Step 3: Route Request transmitter nodes || Route Request
receiver nodes and Route Reply transmitter nodes || Route
Reply receiver nodes.
S1 / S2 or Si / Sj = (ith, jth ) or Sm /Sn= (m, n) = Source
nodes
IN1 /IN2 or INi / INj = ( ith, jth ) or INm/INn = (m, n) =
Intermediate Nodes
D1 /D2 or Di /Dj = (ith, jth ) or Dm/Dn = (m, n) Destination
nodes
Step 4: Distances b/w: Source node || Intermediate node ||
Destination node.
d11,d12,d21,d22 = Single Transmitter and receiver node
di1,di2= ith, or dm= m =Transmitter node
d1j,d2j=jth, or dn= n = Receiver node
dij= ith , jth or dmn= m, n =Transmitter and receiver nodes
Step 5: Routing cost b/w: Total no. of Transmitter and
Receiver in the network.
Cost has considered = Routing time taken between nodes
C11,C12,C21,d22 = Single Transmitter and receiver node
Ci1,Ci2= ith or Cm= m = Transmitter node
C1j,C2j=jth or Cn= n= Receiver node
Cij= ith , jth or Cmn= m, n, =Transmitter and receiver nodes
Step 6: ∆dij distance between node i and j, ∆vij velocity of
node i and j.
Where:
∆dij=di – dj
∆vij=(Vi cosθ – Vj cosθ)
Assumptions:
All the vertices in the Graph (G) = [V, E] are labeled as [1, 2,
…..,M] and [1,2,……,N] and the graph is represented by
adjacency matrix of order M*N.
 Vertices[V] = Nodes in MxN routing environment
 Edges[E] = Paths in M*N nodes.
Time complexity T= O(d-1), d=No. of hops in between
Source to Destination nodes.
Length [1,2,…….M, and1,2,……………N]=array of
distances.
Path [1,2,………M, and 1,2,…………….N]=array of
vertices.
Set [1,2,…………M, and1,2,………….N]=array of
boolean tags.
Rij = Route discovery between source to destination nodes.
In the Rij ‘1’ is path available from ith nodes to jth nodes the
network, otherwise ‘0’.
C. Algorithm for Detecting Optimal Routes Between
Sources to Destination Nodes
Input:
DatasetsAdjacency_matrix.txtmth rows and nth columns
&& Node_coordinator.txt N is the No. of nodes in IoT
networks.
Output:
Route stability level: VeryLow(L), Low(L), Medium(M),
Average(A), High(H),Very High(VH).
Step 1: Route discovery process:
If (S1/IN1/D1 ==1 ith nodes & mth rows) && (S1/IN1/D1
==1 jth nodes &nth columns)
{
Stable path b/w node i and j
if(S1/IN1/D1==0ith nodes & mth
rows)&&(S1/IN1/D1 ==0 jth nodes & nth columns)
{
Route discovery process has
fails, then to find the alternate
path b/w node i and j.
S1/IN1/D1S2/IN2/D2Sn/INn/Dn
S1/IN1/D1d11 d12 d1n a1
S2/IN2/D2d21 d22 d2n a2
S/IN/
D….-- -- -- a...
di1 di2 dij --
Sm/INm/Dmdm1 dm2
--
dmn am
Sb/INb/Dbb1b2
-- --
bn
Sj/INj/Dj
d1j
d2j
Sa/INa/Da
Si/INi/Di
-- --
--
--
--
--
-- a...
--
--
--
--
--
--
--
b... bjb...
--
ai
SOUTH
WEST
C11
Cij
C2n
C1nC1j
C21
C12
C22
Ci2
C2j
Ci1
CmnCm2Cm1
S/IN/D…. S/IN/D….
S/IN/D….--
--
--
--
-- --
--
-- -- --
--
------
--
-- --
-- --
-- --
--
NORTHEAST
SOUTHEAST
SOUTHWEST
NORTHWEST
NORTH
EAST

Design of an Optimized Multicast Routing Algorithm for Internet of Things
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DOI: 10.35940/ijrte.B3372.078219
}
}
Step 2: Route link stability selection:
if(∆d==low && ∆V==negative)||(∆d==low &&
∆V==Zero)||(∆d==low&&
∆V==positive)||(∆d==medium&&∆v==zero)||(∆d==medium
&& ∆v==positive) || (∆d==high&& ∆v==zero)||(∆d==high
&& ∆v==positive)
{
if(Link_stability==(medium)||(very
high)||(low)||(high)||(average) ||(very low)
{
Link stable paths to forward the packets
}
}
Step 3: Packet forwarding:
/*Detect the optimal paths*/
/*Balanced Stable Path (BSP)*/
if (Sm/INm/Dm+Sn/INn/Dn-1)
{
Balanced_Route(Sm/INm/Dm+Sn/INn/Dn-1= Cmn)
Choose the most positive value in BSP
}
else if
{
Unbalanced route, and then continue the process until when
BSP occurred.
After found new BSP has obtained, then perform to find BSP,
otherwise repeat the step: 3.
}
/*Unstable Path*/
elseif(∆d==medium&& v==negative)||(∆d==high&&
∆v==negative)
{
if (Link_stability==(low)||(very low)
{
Unstable path to choose an alternate path
}
}
Step 4: Finally, calculating the path expiry time Eq (5).
Position of ith node and jth node at Time t given by (xi,yi)
and (xj,yj) [10].
Let vi and vj is the velocity of node(θi ,θj ) is the direction
of ith node and jth node.
Transmission range of of ith node and jth node r
Path Expiration Time Dt
 
2 2 2 2
22
( ) ( )
t
D ab cd a c r ad bc
ac
     

(5)
Where:
a= vi cosθi - vj cosθj
b= xi-xj
c=visin θi -vjsin θj
d=yi-yj
/* Paths has valid only within the transmission range of
nodes, otherwise go to Step 4*/
IV. RESULT AND ANALYSIS
The experimental work was carried out using the Network
Simulator (NS3) which is a discrete event network simulator.
Present algorithm has been evaluated and tested with random
waypoint mobility models as shown in fig. 3 and following
parameters are used to evaluate the algorithm in Table 1. So
the proposed algorithm shows a better result when compared
with existing protocols (MAODV and ODMRP).
A. Performance Metrics
(i) Packet Delivery Ratio (PDR)
PDR= ∑[No. of packet received at destination node]
∑[No. of packet send by source node]
(ii) Throughput (T) and Goodput(G)
T= No. of received packets *packet size * 8
Total simulation time
G= Max No. of packets received by the Rx in sequence
Total No. of packets sent sender Tx
(iii)Control overheads (CO)
(i)Average queue length (E(N))=[Arrival rate(λ)*E(W)]
(ii)Average packet delay (E(W))
CO =∑ [(No. of slot time for each successful packet
transmission)]
∑ [Total no. of slot time for each successful transmission
packet)]
Figure 3: Implemented by DOMRA screen shot
B. Performance Comparison of DOMRA, ODMRP and
MAODV
Fig. 4 shows the graphical representation of data between
the varying constant velocity of nodes (m/s) and the packet
delivery ratio in case of both the proposed DOMR algorithm
which is represented in green color line, existing MAODV
which is in black color line and ODMRP algorithm which is
in red color. When the velocity increases from 2 to 10m/s, the
PDR was observed to be 89% in the case of DOMR based
algorithm, while the same was observed to be 77% in the case
of ODMRP and 66% for MAODV algorithm.

International Journal of Recent Technology and Engineering (IJRTE)
ISSN: 2277-3878, Volume-8 Issue-2, July 2019
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Retrieval Number: B3372078219/19©BEIESP
DOI: 10.35940/ijrte.B3372.078219
Table 1: Simulation Parameters
Fig. 5 shows the graphical representation of data between
the varying number of nodes and the throughput. In case of
the proposed DOMR algorithm, it is represented in blue color
line, existing MAODV is in black color line and ODMRP is
with red color. When the number of nodes increases from 0 to
100, the throughput was observed to be 80% in the case of
DOMR based algorithm, while the same was observed to be
68% and 55% in the case of ODMRP and MAODV
respectively.
2 3 4 5 6 7 8 9 10
0.40.4
0.5
0.50.5
0.6
0.60.6
0.7
0.70.7
0.8
0.80.8
0.8
0.90.9
0.9
1.01.0
Packet Delivery Ratio
Constant Velocity(m/s)
Figure 4: Number of Nodes Vs Packet Delivery Ratio
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100
0
10000
20000
30000
40000
50000
60000
70000
80000
Throughput(bps)
Number of Nodes
Figure 5: Number of Nodes Vs Throughput
Fig. 6 shows the graphical representation of data between the
varying number of nodes and the goodput. In case of the
proposed DOMR algorithm it is represented in blue color
line, the existing MAODV is in black color line and ODMRP
is with red color line. When the number of nodes increases
from 0 to 100, the goodput was observed to be 85% in the
case of DOMR based algorithm, while the same was
observed to be 75% and 54% in the case of ODMRP and
MAODV respectively.
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
Goodput(bps)
Number of Nodes
Figure 6: Number of Nodes Vs Goodput
Fig. 7 shows the graphical representation of data between the
varying number of nodes and the control overhead. In case of
proposed DOMR algorithm it is represented in green color
line, the existing MAODV is in black color line and ODMRP
is with the red color line mentioned in the graph. When the
number of nodes increases from 0 to 100, the control
overhead is reduced and was observed to be 65% in the case
of DOMR algorithm, while the same was observed to be 80%
and 85% in the case of ODMRP and MAODV respectively.
10 20 30 40 50 60 70 80 90 100
300
400400
500
600600
700
800800
900
10001000
1100
12001200
Control Overhead
Number of Nodes
Figure 7: Number of Nodes Vs Control Overheads
V. CONCLUSIONS
Designed optimized multicast routing algorithm is used to
improve the network lifetime, throughput, goodput, and to
reduce the end to end delay along with control overhead. The
present algorithm focused on mesh based multicasting
routing techniques and are used to enhance the routing
performances, lower energy consumption, efficient
bandwidth utilization, minimize control overheads, and
minimize routing cost with the help of DOMRA. The
simulation result shows that the proposed DOMRA can
considerably improve the
performances over the
existing MAODV and
ODMRP. The PDR was
Parameter
Value
Simulator
Number of Nodes
NS-3
100
Simulation Range
200x200m
Simulation Time
50ms
Transmission
Range
255m
Bandwidth
10mbps
Traffic Type
CBR
Data payload
1000 bytes

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Author-3
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Author-3
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observed to be 89% for DOMR algorithm than compared
with existing ODMRP which is 77% and for MAODV it is
66%. Throughput was observed to be 80% for DOMR
algorithm when compared with existing ODMRP which is
68% and for MAODV it is 55%. Goodput was observed to be
85% for DOMR based algorithm, than compared with
existing ODMRP which is 75% and for MAODV it is 54%.
Control overhead is reduced and was observed to be 65% for
DOMR algorithm compared with existing ODMRP which is
to be 80% and for MAODV it is 85%.
ACKNOWLEDGMENT
The authors thank to S R Engineering College
(Autonomous) management for providing the facility
regarding research works.
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AUTHORS PROFILE
Dr. D. Kothandaraman received his B.E. (CSE) from
Dr. Pauls Engineering College (Anna University),
M.Tech.,(CSE-IS) from Pondicherry Engineering
College(Pondicherry University(Govt. of India)) and
Ph.D. (CSE) from College of Engineering, Guindy,
Anna University(Govt. of Tamil Nadu). To his credit,
he has 8 years of teaching and research experience.
His area of research interest is computer networks, Wireless Sensor
Networks (WSN), Mobile Ad-hoc Networks (MANETs) and Internet of
Things (IoT). He has published various papers in International Journals and
in conferences. Currently, he is working as Associate Professor in
Department of Computer Science and Engineering at S R Engineering
College, Warangal, TS, India.
Dr. M.Sheshikala, completed her Ph.D from K L
Educational Foundation in Computer Science and
Engineering, Andhra Pradesh in March 2018. She is
an Associate professor in the department of CSE at S
R Engineering College. Her research interests are
related to Data Mining, Machine Learning. She had
published 28 publications in various national and international journals,
conferences and proceedings. Her total teaching experience is 14 years.
Dr. Seena Naik Korra received his Ph.D, in Computer
Science and Technology in 2016 from Sri
Krishnadevaraya University, India. He received his
M.Tech., in 2008 from the Department of CSE, JNTU,
Anantapur, India. He received MCA in 2006 from Sri
Krishnadevaraya University, India. He received his
BCA degree in 2003 from Sri Krishnadevaraya University, India. His
research interest includes Computer Networks, Wireless Sensor Networks,
Big Data Analytics, IoT, and Image Processing. He has the teaching
experience of 13 years. Currently, he is working as Associate Professor in
Computer Science and Engineering Department at S R Engineering College,
Warangal.
Yerrolla Chanti received M.Tech in Computer Science
and Engineering in 2016 from Jawaharlal Nehru
Technological University, Hyderabad, India. He has
the teaching experience of 3 years. Currently, he is
working as Assistant Professor in Department of
Computer Science and Engineering at S R Engineering
College, Warangal, India. His research areas include Networking, Big Data
Analytics.
Vijay Kumar Bura received his B.Tech., in Computer
Science Information Technology from JNTUH in 2006
and M.Tech in Software Engineering form JNTUH,
Telangana, India in 2011. He worked as Software
Engineer at ITP Software India Private Limited,
Hyderabad for 2 years. He developed various web
applications for different clients. He worked as Asst. Prof. in the Dept. of
IT, SVS Institute of Technology, Warangal for 2 years. Presently he is
working as Assistant Professor in the Department of CSE in S R Engineering
College, Warangal, Telangana, India.