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Volume 4 Issue 4
April 01,2015
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Volume 4 Issue Special 2
A National Conference on "Recent Advances in Chemical Engineering"
GreenChem-15, on March 20, 2015
Organized By
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Volume 4 Issue Special 4
May 19 & 20, 2015
2nd International Conference on Convergent Innovative Technologies (ICCIT-2015)
On
May 19 & 20, 2015
Organized by
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Editorial Board
Editor in Chief
Dr. R. K. Singh,
Professor and Head, Department of Electronics and Communication,
KNIT Sultanpur U.P., India
Managing Editor
Mr. J. K. Singh, Managing Editor
Innovative Research Publications, Bhopal M.P. India
Advisory Board
1. Dr. Asha Sharma, Jodhpur, Rajasthan, India
2. Dr. Subhash Chander Dubey, Jammu India
3. Dr. Rajeev Jain, Jabalpur M.P. India
4. Dr. C P Paul, Indore M.P. India
5. Dr. S. Satyanarayana, Guntur, A.P, India.

List of Contents
S.No. Manuscript Detail Page No.
1
Dynamic Cluster Head (CH) Node Election and Secure Data Transaction in CWSNs
Vishnu V., Shobha R.
238-242
2
Evolution of Analytics –A Survey
Geetha P., Dr. Suresh L., Dr. Chandrakant Naikodi
243-246
3
A Design of Simulating the Field Devices Based on HART Protocol
Betsy Thomas, Dr. Chandrakant Naikodi, Dr. Suresh.L
247-250
4
Energy Efficient Contention and TDMA MAC Protocols for Wireless Sensor
Networks: A Survey
Anitha K.,Dr. Usha S.
251-254
5
Detecting a Malicious Node using Voting and Secondary Path Techniques in MANETs
Bhargavi M. N., Dr. Chandrakant Naikodi, Sushma B Malipatil, Dr. L. Suresh
255-256
6
Path Oriented Randomly-Generated Keys forEnergy Efficient Uncompromised
Security in MANETs
Shalini S., Dr.Chandrakant Naikodi, Dr. Suresh L., Sushma B Malipatil
257-260
7
Dynamic Transmission Power Control Algorithms for Wireless Sensor Networks
Jabeena Khanam, Anitha k.
261-265
8
Cyber-Physical Control forSmartTransportation Systems: A Review
R. Prabha,Mohan G Kabadi
266-270
9
An Analysis of Multipathaomdv in Mobile ADHOC Networks
S. Muthusamy, Dr. C. Poongodi
271-273
10
Privacy Preserving Web Search Personalized In Secure Web Structure
Priyanka M , Preeti B M ,Neelamma N , Dr. Shashi kumar D R
274-275
11
Smart Cities Using Internet of Things
Smitha Ashok Patil, Soumya L, Krishna Kumar, Suresh L
276-278
12
The Effective and Adaptive Method for Graph Theoretic Clustering of Data
Sowmya N., Vandana B.S.,Dr.Antony P.J
279-281
13
Overpower “Vampire Attack” In Wireless Ad-Hoc Sensor Network Using Secured
Routing Protocols
P R Rakendraj, Shivakumar Dalali
282-287

14
Intelligibility Prediction of Speech using MFCC
Shubha S, SSavitha ,CM Z Kurian
288-291
15
Automationin Security Development Lifecycle as a part of Quality Assurance for Developed
Software’s
Suhaas K.P Yamuna PBhagavant Deshpande
292-294
16
An Approach for Content Delivery in Cloud
Preeti Janardhan, Singh K.
295-298
17
Clustering Scheme with Header Based Proactive Routing Protocol for MANETs
Nagaraja S., Shivakumar Dalali
299-303
18
Location Based Privacy Preserving Services for Achieving Asymmetric Sensing
Coverage in Wireless Sensor Networks
Nandeesh S., Dr.Suresh L., Dr. Chandrakant Naikodi
304-307
19
Managing Large Data Set by Caching manager using Hadoop Map Reduces
Framework
Neelamma Natikar, Raghavendra T. S.
308-311
20
Online Learning Environment Based On Cloud for Enhancing the Student Learning
Abilities
Mamatha S., Janardhan singh
312-314
21
A Social Networking in MANET’s for P2P File Sharing
Deepti Y.J. ,Kavya M.S., K. Satyanarayan Reddy
315-319
22
Secure DataRetrieval for Decentralised Disruption-Tolerant in Wireless Sensor
Network
Aishwarya Cauveramma P. N. Balapradeep K. N. Dr. Antony P. J.
320-322
23
Efficient Sleep Scheduling Strategy to enhance the Network Lifetime of WSN
Mrs. Surekha K.B., Mr. Raghunandan V, Dr. Mohan K.G., Dr. T.G.Basavaraju
323-326
24
R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Networks
Venugopal A.S., Dr.Shashi Kumar D. R., Rohith K.M.
327-331
25
Searching locations via handheld mobile devices using location based server
Koulali shailesh, Janardhan Singh K.
332-335

International Journal of Engineering Research ISSN:2319-6890(online),2347-5013(print)
Volume No.4, Issue Special 4 19 & 20 May 2015
ICCIT15 @ CiTech, Bangalore Page 238
Dynamic Cluster Head (CH) Node Election and Secure Data Transaction in
CWSNs
Vishnu V., Shobha R.
Deptt of DCE, VTU, MRIT-Banglore Karnataka
vishnu.vm7@gmail.com, shobha.r@cmrit.ac.
Abstract- Providing security in wireless sensor networks
(WSNs) is one of the most challenging tasks. Analysis of
WSN suggests that clustering is effective technique to
enhance the system performance. In this paper, dynamic
election of Cluster Head (CH) mechanism and two
evolutionary approaches, SET-IBS and SET-IBOOS have
been applied. This provides security in data transmission
and reduces data losses due to nodes failure, because of less
residual energy in elected CH. The categorisation of the
nodes play a vital roles in providing security and reducing
data transmission failures. We divide nodes into 3 categories
like Advanced nodes, Super nodes and Normal nodes. An
experimental result shows that proposed method achieves
high efficiency and high security.
Keywords : Cluster based WSNs (CWSNs), SET-Identity
Based digital Signature (IBS), SET- Identity Based
Online/Offline digital Signature (IBOOS), nodes
categorisation.
I. INTRODUCTION
A wireless sensor network (WSN) is a collection of
different devices using sensor nodes that monitor
environmental or physical conditions like motion,
temperature, and sound [1]. The development of wireless
sensor networks was motivated by military applications such
as battlefield surveillance; today such networks are used in
industrial and consumer applications. Cluster-based data
transmission in WSNs has been investigated to achieve the
network scalability and management, which is used to reduce
bandwidth and maximizes node lifetime [2].
Figure 1 show the basic architecture of the wireless
sensor network in which sensor node deployed in the sensor
fields and they communicate with each other to collect the
information from the environment or they may directly send
the information to the Base Station (BS). Basically base
station acts as gateway. With the help of gateway data is
transmitted to the internet. Users are directly connect to the
internet. A sensor node that generates data , based on its
sensing mechanisms observation and transmit sensed data
packet to the BS (sink). This process is basically direct
transmission. Since base station may located very far from
sensor nodes, it needs more energy to transmit data over long
distances.
So the better techniques is to have fewer nodes, which send data
to the BS. These type of nodes are called aggregator nodes and
the processes called data aggregation in WSN.
The goal of the proposed efficient and secure data
transaction for CWSNs is to guarantee the secure and
efficient data transmissions between leaf nodes and CHs, as
well as transmission between CHs and BS. In this paper we
aim to solve orphan node problem [4] by using ID based
cryptosystem that guarantees security requirements, and
propose SET-IBS by using the IBS scheme. Furthermore,
SET-IBOOS is proposed to reduce the computational
overhead in SET_IBS with the IBOOS scheme.
Figure 1. Architecture of Wireless Sensor Network
(WSN).
There are some secure data transmission protocols based on
LEACH-like protocols, such as SecLEACH [5], GS-LEACH
[6] and RLEACH [7]. Most of them, however, apply the
symmetric key management for security, which suffers from a
so-called orphan node problem. This problem occurs when a
node does not share a pairwise key with others in its preloaded
key ring, in order to mitigate the storage cost of symmetric
keys, and the key ring is not sufficient for the node to share
pairwise symmetric keys with all of the nodes in a network. In
such a case, it cannot participate in any cluster, and therefore,
has to elect itself as a CH. Furthermore, the orphan node
problem reduces the possibility of a node joining a CH, when
the number of alive nodes owning pairwise keys decreases after
a long-term operation of the network. Since the more CHs
elected by themselves, the more overall energy consumed of
the network [8], the orphan node problem increases the
overhead of transmission.
Even in the case that a sensor node does share a pairwise key
with a distant CH but not a nearby CH, it requires
comparatively high energy to transmit data to the distant CH.

The feasibility of the asymmetric key management has been
shown in WSNs recently, which compensates the shortage
from applying the symmetric key management for security
[9]. Digital signature is one of the most critical security
services offered by cryptography in asymmetric key
management systems, where the binding between the public
key and the identification of the signer is obtained via a digital
certificate. The Identity-Based digital Signature (IBS) scheme
[10], based on the difficulty of factoring integers from
Identity-Based Cryptography (IBC), is to derive an entity’s
public key from its identity information, e.g., from its name or
ID number. Recently, the concept of IBS has been developed
as a key management in WSNs for security. Carman [11] first
combined the benefits of IBS and key pre-distribution set into
WSNs, and some papers appeared in recent years, e.g., [12].
The IBOOS scheme has been proposed in order to reduce the
computation and storage costs of signature processing. A
general method for constructing online/offline signature
schemes was introduced by Even et al. The IBOOS scheme
could be effective for the key management in WSNs.
Specifically, the offline phase can be executed on a sensor
node or at the BS prior to communication, while the online
phase is to be executed during communication.
II. MATERIAL AND METHODOLOGY
Existing system: In this existing system, the WSNs
compromised of spatially distributed devices, using wireless
sensor nodes to monitor physical or environmental conditions,
such as sound, temperature and motion etc. The individual
nodes senses the data, processes it locally and sends that to
one more collection points in a WSN [1].
This kind of system is not energy efficient and
secured. Efficient data transmission is one of the most
important issue of WSNs. Meanwhile, many WSNs are
deployed in harsh, neglected and often adversarial physical
environments for certain applications, such as military
domains and sensing tasks with trustless surroundings.
Proposed System: In the proposed system, an innovative
technique is introduced for dynamic selection of CHs [13] and
a secure data communication for CWSNs is presented. The
contributions of this work are as follows:
• With dynamic CH election mechanism, each sensor
nodes calculates its residual energy. Based on this
calculated residual energy of the nodes, the nodes in
each cluster are categorised into three groups:
i] Advanced nodes (ANs): The nodes which have
residual energy.
Residual energy >= Threshold value + 50%
Threshold value.
ii] Super node (SNs): The nodes which have residual
energy greater than the threshold value but less than
the residual energy of ANs.
iii] Normal nodes (NNs): The nodes which have
residual energy less than threshold value.
The threshold value is the minimum residual energy
required to transmit the sensed data to Base Station
(BS).
Only the Advanced and super nodes can
become the CHs and the priority is given for ANs and
then to SNs. Depending on the residual energy level
the sensor node acts as next CH. This categorisation is
done to prevent loss of data due to nodes failure.
• In CWSNs two secure and efficient data transmission
protocols are presented, SET-IBS and SET-IBOOS, by
using the IBS scheme and the IBOOS scheme,
respectively. The key idea behind the SET-IBS and
SET-IBOOS protocol is to authenticate the encrypted
sensed data, by applying digital signatures. In the
proposed system, secret keys and pairing parameters
are distributed and preloaded in all sensor nodes by the
BS initially, which overcomes the key escrow problem
described in ID-based cryptosystems.
• Secure communication in SET-IBS depend on the ID
based cryptography [3], in which, user public keys are
their ID information. Thus, users can obtain the
corresponding private keys without auxiliary data
transmission, which is efficient in communication and
energy can be saved.
• SET-IBOOS is further used to reduce the
computational overhead. Both SET-IBS and SET-
IBOOS solve the orphan node problem in the secure
data transmission with a symmetric key management.
System modules: The system is divided into five different
modules:
1] Dynamic CH selection mechanism: Each sensor node has
different energy level in its cluster at any given time. The
energy level of the node depends on some factors such as
sleep/wake up schedule, and amount of data transmitted and
received [13]. The residual energy decides the whether node
should be considered as CH or not.
We determine residual energy of each node in WSN on
basis of mathematical model. Let us assume that there is
single-hop communication is used among the sensor nodes to
detect events and to transmit the information. Each node
forwards data ‘d’ at distance ‘r’ within cluster ‘C’ and located
at A*A area of WSN. We determine the residual energy of the
nodes using following formulas:

Where, : Residual energy of the each node; :
Energy consumption of radio; :Energy used for
amplifying radio signal.
Equation (1) shows the residual energy of each node that
sends data to CH.
Where, :Multi-hop fading channel.
Equation (2) shows the residual energy level of CH when
forwarding data to Base Station.
Based on this residual energy the nodes are categorised as
Advanced Nodes (ANs), Super Nodes (SNs) and Normal
Nodes (NNs).
2] Initialisation of SET-IBS protocol:
• Setup Phase: The BS (as a trust authority) generates a master
key ‘msk’ and public parameters ‘param’ for the private key
generator (PKG), and gives them to all sensor nodes.
• Extraction: Given an ID string, a sensor node generates a
private key ‘sek ID’ associated with the ID using ‘msk’.
• Signature signing: Given a message M, time-stamp t and a
signing key θ , the sending node generates a signature SIG.
• Verification: Given the ID, M and SIG, the receiving node
outputs “accept” if SIG is valid, and outputs “reject”
otherwise.
3] Operation of SET-IBS protocol: After the protocol
initialization, SET-IBS operates in rounds during
communication. Each round consists of a setup phase and a
steady-state phase. We suppose that all sensor nodes know the
starting and ending time of each round because of the time
synchronization. Each round includes a setup phase for
constructing clusters from CHs, and a steady-state phase for
transmitting data from sensor nodes to the BS. In each round,
the timeline is divided into consecutive time slots by the
TDMA control. Sensor nodes transmit the sensed data to the
CHs in each frame of the steady-state phase.
4] Initialization of SET-IBOOS protocol:
• Setup Phase: Same as that in the IBS scheme.
• Extraction: Same as that in the IBS scheme.
• Offline signing: Given public parameters and time-stamp t,
the CH sensor node generates an offline signature SIG offline,
and transmit it to the leaf nodes in its cluster.
• Online signing: From the private key sek ID, SIG offline and
message M, a sending node (leaf node) generates an online
signature SIG online.
• Verification: Given ID, M and SIG online, the receiving
node
(CH node) outputs “accept” if SIG online is valid, and outputs
“reject” otherwise.
5] Operation of SET-IBOOS protocol: The proposed SET-
IBOOS operates in the same manner as that of SET-IBS
protocol. SET-IBOOS works in rounds during
communication, and the self-elected CHs are decided
based on their local decisions, thus it functions without
data transmission in the CH rotations.
However, the differences is that digital signature are
changed from ID-based signature to the online signature of
the IBOOS scheme.
Once the setup phase is over, the system turns into
the steady-state phase, in which data are transmitted to the
Base Station.
III. SIMULATION AND RESULTS
In this section, performance benefits have been evaluated
through several simulations. For this purpose we have used
Network Simulator – 2. The network parameters used for
evaluation are described below:
• Our simulation environment consist of 51 nodes
randomly deployed in a field of 100m * 100m.
• All the nodes are identical and Base Station is
situated at the centre of the field.
I] Cluster formation and election of CHs: The mechanism
of formation of the clusters and dynamic election of CH for
each cluster is shown in the Figure 1.
The clusters are formed based on their received signal
strength and its distance from Base Station (BS). The residual
energy value determines whether the node should be
considered as CH or not. Based on the categorisation of the
nodes within each cluster and residual energy value the CH
for each node is selected.
Figure 1. Cluster formation and CH election.
II] Solutions to the attacks and Adversaries: The proposed
SET-IBS and SET-IBOOS provide different types of security
services to the communication for CWSNs [3], in both setup
phase and steady-state phase. Both in SET- IBS and SET-
IBOOS, the encryption of the message provides
confidentiality, the hash function provides integrity, the nonce

and time-stamps provide freshness, and the digital signature
provides authenticity.
A] Solutions to passive attacks: In the proposed SET-
IBS and SET-IBOOS, the sensed data is encrypted by
the homomorphic encryption scheme from [29], which
deals with eavesdropping. Thus, the passive
adversaries cannot decrypt the eavesdropped message
without the decryption key.
B] Solution to active attacks: The SET-IBS and SET-
IBOOS work well against active attacks. Most of the
times the attacks are pointed CHs of acting as
intermediary nodes, because of the limited functions of
the leaf nodes in CWSNS. Figure 2 shows how the
active attack has been removed from the network.
Figure 2. Active attack and solution for it.
The node which is actively attacked is moved out of
the communication area of the network and it is shown
in figure 2.
C] Solutions to compromising attack: In case the
attacks from the node compromising attacker; the
compromised sensor node cannot be trusted anymore
and therefore, that particular node is removed from the
CWSN.
Figure 3. Comparison of FND time in different
protocols.
Figure 3 illustrates the time of FND (First Node to Die).
Since we are using dynamic CH selection mechanism, the
time of FND for SET-IBS and SET-IBOOS are more.
But in case of LEACH or LEACH-like protocols, sometimes
nodes with energy less than threshold value is also opted as
CH, so the time of FND is less here. In that case that
particular node will die soon, which decreases the network
lifetime.
Since we use online/offline technique in SET-IBOOS
protocol, the time of FND is more for SET-IBOOS on
comparing to SET-IBS technique based networks.
IV. CONCLUSION AND FUTURE SCOPE
In this paper, we have first shown dynamic cluster formation
and election of CHs based on their level of residual energy.
By doing so, we have reduced data losses due to node’s
failure. The deficiency of symmetric key management for
secure data transmission has been discussed. We then
presented two secure and efficient data transmission protocols
respectively for CWSNs, SET-IBS and SET-IBOOS. The
presented two protocols have gave solutions to the various
kind of attacks in CWSNs and the dynamic CH selection
mechanism has improved the lifetime of network by
increasing time of FND.
The information provided in this paper would be
beneficial for researchers to work in this area. This approach
can be applied to variety of applications, by varying the
threshold value of node’s residual energy level. Since we are
reducing data losses and improving security of data
transmission, in future this approach can be well implemented
in the areas, where data is of high importance.
Acknowledgement
I thank my guide and the college for their valued
cooperation and advice for preparation of this paper.
References
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vii. K. Zhang, C. Wang, and C. Wang, “A Secure Routing Protocol for
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460

ICCIT15 @ CiTech, Bangalore Page
Evolution of Analytics –A Survey
Geetha P.1
, Dr. Suresh L.2
, Dr. Chandrakant Naikodi3
1
Dept of CSE, 2,3
Cambridge Institute of Technology, Bangalore
geetha.cse@citech.edu.in
Abstract - Data is the core of every analytics practice. Effective
analytics on data from basic reporting to in depth analysis
allows data analysts to obtain insights into valuable data that
can be used for strategic decision making. The emergent trend
of applications today generates a need to handle an enormous
amount of data. Unlike traditional structured data, the volume
and type of data today is insurmountable. This may include
financial data in accounting databases, vast amount of online
data, and data from social networking sites like Facebook and
Twitter, web logs, data collected from sensors and a wide
variety of similar unstructured data. Traditional techniques of
capture, storage and analysis of such huge volume of data will
no longer suffice. This paper makes a survey of the analytics
techniques, both in the conventional and big data set up, and
discusses the key challenges in big data analytics.
Keywords: Big Data, Analytics, Knowledge Mining,
Challenges
1. Introduction
We are in a Digital Economy where every piece of data collected
is a valuable asset. Learning the fundamental value of raw data
and gaining experience from it is of primary importance in
today’s interaction based society [10].All diversified investment
sectors, namely Business, Health, Industry, Education, social
and government sectors revolve around data in all forms.
Though data was existent, the type, amount and speed at which
the data is generated today has seen a tremendous change,
inevitably driving a need to reconsider the conventional modes
of data management and processing. Today large scale
interaction via emails, mobiles, text, and social media has
revolutionized the data era. Not only is the data generated
massive, but also highly unstructured. Driven by the explosion in
the volume, variety and velocity of data [1][10], data analysts
are continuously looking at better and more effective methods of
collecting, storing, and analyzing data, to obtain faster and
promising judgments from them. This data explosion, in size,
kind and rate has led to an era of Big Data. The traditional and
long-established practices of data storage and analysis are
inadequate for Big Data applications. With Big data, an
intelligent data driven decision support system [2] which meets
the demand of such fast growing data is indispensable.
2. Defining Big Data
2.1 Big data Perspectives
There is an enormous scale of data being collected and
processed today. The amount of data generated is very large and
requires high end processing capability. Size is not the only
metric that qualifies Big data. The kind of data that is generated
ranges from web content, data from social networking sites, data
built into spreadsheets and word documents, sensor data,
financial data, and a wide variety of structured, unstructured and
semi-structured data [1][5][6]. Moreover, there is a steep rise in
the rate at which the data is generated. These varied dimensions
of data have given rise to the 3V model of big data, defined by
Volume, Variety and velocity of data. [1][11][7].These attributes
typically describe the different dimensions of big data. In this
paper the focus is on essentially the 3V’s of big data that
originally surfaced as its challenging traits, and the drastic
transformation that it has brought to in the field of analytics.
2.1.1 Volume
It goes without saying that data volume is of paramount
importance when it comes to defining Big data. Volume refers to
the magnitude of data. Data created across various organizations
from a myriad of fields is growing at an exponential rate.
According to statistics [21], the social networking site,
Facebook, alone generates 4.5 billion likes per day. The data
generated from smart phones and tablets has seen a steep rise
since the last three years. As many as 3.63 billion of the world’s
population is mobile users. The datasets handled today are in the
order of petabytes and exabytes.
2.1.2 Variety
The data originates from multiple disparate sources and they are
of varying formats such as blogs, emails, sensor data, text, video,
audio etc…to name a few. Traditionally data processing was
confined to highly structured data with a well defined schema.
But the kind of multifaceted data that computers deal with today
from varied fields requires a robust environment that develops
storage to suit the data.
2.1.3 Velocity
Besides volume and variety, speed of data generation and
delivery is one other challenging factor characterizing big data.
Datasets such as log streams, click streams from websites,
message streams, real time data collected from video cameras
etc. [18] are examples of data that gets delivered in real time.
These data that get generated constantly at a tremendous speed
requires continuous processing, unlike data that is completely
available in an established un-fluctuating computing
environment.

Volume No.4, Issue Special 4 19 20 May 2015
3. Defining Analytics
Analytics is a fact based examination and study of data for
obtaining valuable information from data sources that can play
an important role in constructive decision making in information
industry.[20] Hidden patterns in data and unknown relationships
between them can be used to improve business value,
personalize customer experience at par with market standards.
The largest online store Amazon’s tags such as “Frequently
bought together” [22] and “Customers who bought this item also
bought” [22] are examples of improvising customer experience
by using item based real time filtering. Big data is changing the
way businesses, government agencies, healthcare, banks,
websites etc….capture, store and manage information. Although
data collection and processing has been customary across all
type of organizations, the advent of big data has brought a
significant transformation in the way it is processed.
3.1. Traditional Analytics
Traditionally focus of analysis was on data that is well
understood, already collected and stored in the database,
typically built on top of a relational data model. It can be
considered as a static pool of data. Data analysts determine the
nature of questions that need to be answered and design the
structure of database according to the data collected. This
structure builds a stable environment [4] and captures only the
most essential data. In other words data is structured so as to suit
the analysis. The analysts followed a reactive [17] approach,
where questions are pre-worked, hypotheses [13] [12] are
framed and the job of analyst is to verify or reject the
hypotheses. This is a form of repetitive analysis.
3.1.1 Operational Processing
The idea of analytics for decision making dates back to
operational systems commonly referred to as Online Transaction
Processing systems (OLTP) [23].These are systems that operate
on transactional data generally built on the relational database
model aimed at responding very quickly to user requests.
Transactional Processing systems operate on data that gets
frequently updated and supports faster query processing enabled
by query processing languages like SQL. Operational Reporting
[23] [9] for day-day transactions and summarization of results
were the initial objectives of analysis. This kind of analysis
started with basic querying and presentation of results. Querying
and Reporting [23] is an analysis technique which prepares
questions to be answered, obtains pertinent data from the
database and prepares a report that is displayed in a format
convenient to the end user. Such report generation and querying
is generally driven by analysts. Retrieving correlated data
elements, grouped data elements or just displaying summarized
results may be the only objective of such an analysis. But these
systems cannot adapt to ad-hoc and complex analytical queries
on vast amount of data. Firstly, the ad-hoc analytical querying
on operational systems degrades performance to a great extent.
Secondly a dedicated decision support system that provides
support for such complex querying without affecting the
operation of the transaction systems is imperative. This paved
the way for separation of the transactional and decision making
environments, so as to reduce conflicts and enhance performance
of the operational set up as well as the decision support system.
3.1.2 Analytical Processing
With the focus of analysts turning towards knowledge discovery
for decision making, day to day processing migrated to complex
and ad-hoc query processing. This gave rise to multidimensional
[23] [24] analysis of data, which enabled complex analytical
processing on large amounts of data. Here, the aim is to explore
data in greater detail, identify complex relationships between
them, work at different levels of granularity [23] [24], and
display results on the fly. Storage support for analytic processing
started materializing in the form of data warehouse [24].A Data
Warehouse is a database system maintained separately from an
organization’s operational database [24], that organizes data
collected from heterogeneous data sources. This requires
aggregation of data with different underlying data structures that
needs to be organized before analysis. Typically warehouse
supports analysis of historical data which does not have
intermittent updations and gives insight into hidden patterns of
value at the managerial end. Operations like aggregation [24],
summarization [23], drill-down [24], roll-up [24] are examples
of operations that enable viewing data from multiple
perspectives. The data goes through a series of preprocessing
stages, extracting only the relevant fields that are required for
analysis.
4. Towards Big Data Analytics
4.1 Discovery Analytics
Analyzing big data and obtaining knowledge from it are
fundamentally very different from traditional statistical querying
on small samples of data. Basic transactional processing on
operational systems and analytical querying on pre-built
structured data started getting outdated with onset of big data.
Big data applications deal with massive, unstructured
continuous flow of data that needs to be captured and processed
progressively. In relational database systems, the relationship
between data is known, and the data is structured before
analysis, In contrast, big data deals with unrelated, unstructured
and uncategorized data which is extracted into a schema-free
databases [7] or NoSQL [7] databases. Since relationships
between the data are unknown, uncovering insights is an
iterative process. Algorithms which extract knowledge from
such a data driven storage and a system that runs these
algorithms demanded capabilities much more than a structured
pre-built storage architecture. Firstly, an automated data
processing system that dynamically learns data, builds a model
on the current data and uses it for futuristic decision making was
found desirable. Secondly, such a data processing system should
not only possess learning capabilities for knowledge engineering
[20], but also have a sound and adaptive database design for data
storage that suits all kinds of applications transparently. The first
of these factors promoted extensive research in the field of
algorithms that can productively give useful insights from data.
Machine learning [20] algorithms, data mining [24], text mining

[1] [3], natural language processing [8] [20], predictive
modeling [10] etc. are some of the techniques that have gone
through extensive research for big data analytics. Alongside,
developed many different technologies that can provide strong
support for data storage such as in-memory database [17],
column store database [19], and Map-Reduce [16] [17] for
Hadoop [16] [17]. The following section does a survey of some
of these techniques for analytics. Figure 4.1 shows the transition
phases from relational databases to Big data.
4.1.1 Data Mining
Data mining evolved as one of the foremost techniques in the
field of knowledge discovery from database [24].It is the
application of specific algorithms to find patterns in data and is
considered as part of the knowledge discovery process. It
explores data, finds interesting and consistent relationships [24]
among them, builds a model to fit the existent data and uses
these results on new sets of data. Although the objective of
mining and traditional analytical processing is similar, the
difference lies in how they operate on data. While traditional
analytic querying tools concentrate on multidimensional
analysis, and compiles ways of querying on them, data mining
focuses on extracting futuristic information that influences
managerial decisions. Data-mining relies heavily on known
techniques from machine learning, pattern recognition, and
mining methods, such as classification [24], regression, [24]
clustering [24], decision trees, [24] and association rule mining
[24].
Figure 4.1 Transition from traditional to Big data analytics
4.1.2 Statistics/Machine Learning
Statistics and machine learning involves use of algorithms that
allow a program to infer patterns from training [8] data, that in
turn allows it to speculate and make predictions about new data.
During the learning phase, numerical parameters are calculated
that exemplify a given algorithm's underlying model. The
learning can be supervised or unsupervised [20] .In supervised
learning [8] [20], each item in the training data is labeled with
the correct answer. On the other hand, the learning process tries
to recognize patterns automatically in unsupervised learning.
The results are validated and applied on new datasets.
4.1.3 Text Mining
The increase in the availability of electronic documents from a
variety of sources, such as WWW, research publications, blogs,
online articles, digital libraries [14] has added a new dimension
to mining data. Text Mining, refers to mining useful information
from textual sources, and is a form of unstructured data mining.
Unlike numeric data, text is often nondescript, and difficult to
deal with. Text mining generally performs analysis of numerous
text documents by extracting key phrases, concepts, etc.,
prepares processed text for subsequent analysis with other data
mining techniques. For instance, data mining techniques may
find related occurrences of particular word with another. The
mining comprises all phases’ right from information retrieval
[14] to document classification [1] [8] to document clustering [1]
[8].
4.1.4 Natural Language Processing (NLP)
NLP is a technique that focuses on mining free text syntactically
as well as semantically. Traditionally NLP focused on syntactic
analysis by making use of linguistic concepts such as part-of-
speech (noun, verb, adjective, etc.).But syntax and grammatical
structure of text does not guarantee semantics. Since the
objective of Natural Language Processing is to extract
meaningful information, syntax can be supplemented by rules
[14] to derive meaning in cases of ambiguity. Ambiguity occurs
when the same word may have different meanings in different
contexts. But the rules to manage ambiguity become
unmanageable with increase in size of text. This motivated the
onset of statistical NLP [14] [8] with the help of machine
learning algorithms which had annotated text that trained
algorithms to extract patterns.
5. Challenges in Big Data Analytics
5.1 Data Integration
It is evident that the intent of any big data application is to
process and analyze massive amounts of data. But facilitating an
environment for big data processing by bringing data collected
from multiple, heterogeneous and distributed sources [2] is a fact
that is often overlooked. Retrieving data from the different
sources, deciding in a dynamic fashion, about what data to be
recorded and what to be discarded is a key challenge.
5.2 Meeting the need for speed/size
Speed and size are flip sides of the same coin. Scaling of Big
data is enormous [15], both in volume and rate. A data
processing system that manages massively increasing volumes
of different kinds of data generated continuously is a growing
challenge with big data analytics. The larger the data to be
processed longer is the time for analysis. If large-scale analysis
has to be practiced effectively in a time bound manner, a sound,
adaptive database design that suits all kinds of applications is
Relational
Database (OLTP)
Terabyte
Data
Operational
Processing
Petabyte
Data
Predictive
analytics
Knowledge
Mining
Terabyte
Data
DataWarehouse/datamarts
Analytical
Processing
Prescriptive
analytics
DataWarehouse/datamarts
BIG DATA
Exabyte
Hadoop/Map Reduce
Advanced analytics
Mining/Decision
Making
Descriptive
analytics

required. A design that deals with size will result in a system that
analyzes data faster.
5.3 Data Availability
An intelligent data processing system is rewarding, only if it is
able to provide data accessibility to a wide range of applications
with complete transparency [18]. A system that is able to
provide quality data seamlessly across platforms and support an
expanding collection of simultaneous end-users is a challenge.
5.4 Skills Gap
Despite the onset of big data technologies, the reality is that the
expertise level in the field is not substantial. Many of these
experts are still clueless when it comes to the practical aspects of
data modeling, data architecture, and data integration, although
they use many Big Data tools available in the market. Statistics
predicts[22][18],that by 2018, there will be an acute shortage of
deep analytical skills that requires skills for about 4.4 million IT
jobs in the field of Big Data.
6. Conclusion
Big data has revolutionized the information industry. Competent
and useful analysis of large volumes of data has the potential to
boost growth in various domains, provided the challenges in data
management are addressed in the long run. These challenges
necessitate the need to rethink the aspects of existing data
management methodologies while keeping open the choices of
retaining the desirable aspects. These challenges vary across
domains, and it is necessary to identify them, and promote
fundamental research in these areas, which is believed to
generate huge economic value for years to come.
References
i. Richard K. Lomotey and Ralph Deters, “Towards Knowledge
Discovery in Big Data”, 2014 IEEE 8th International Symposium on Service
Oriented System Engineering
ii. Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding, “Data
Mining with Big Data”,IEEE transactions on knowledge and data engineering,
vol. 26, no. 1, January 2014
iii. Ganapathy Mani, Nima Bari, Duoduo Liao, Simon Berkovich”,
Organization of Knowledge Extraction from Big Data Systems”, 2014 Fifth
International Conference on Computing for Geospatial Research and
Application
iv. Anirudh Kadadi, Rajeev Agrawal, Christopher Nyamful, Rahman
Atiq”, Challenges of Data Integration and Interoperability in Big Data”, 2014
IEEE International Conference on Big Data
v. Shunmei Meng, Wanchun Dou, Xuyun Zhang, and Jinjun Chen,”A
Keyword-Aware Service Recommendation Method on MapReduce for Big Data
Applications”, IEEE transactions on parallel and distributed systems, vol. 25,
no. 12, december 2014
vi. Rongxing Lu, Hui Zhu, Ximeng Liu, Joseph K. Liu, and Jun Shao,”
Toward Efficient and Privacy-Preserving Computing in Big Data Era “,IEEE
Transactions on Knowledge Data Engineering, Vol 28,issue 4,Dec 2014.
vii. Sangeeta Bansal,Dr.Ajay Rana ,”Transitioning from Relational
Databases to Big Data”,vol 4,January 2014,International Journal of Advanced
research in Computer Science and Software Engineering
viii. F. S. Gharehchopogh, and Z. A. Khalifelu, “Analysis and evaluation
of unstructured data: text mining versus natural language processing,”
Application of Information and Communication Technologies (AICT), 2011 5th
International Conference, vol., no., pp.1-4, 12-14 Oct. 2011, doi:
10.1109/ICAICT.2011.6111017
ix. Pattern-Based Strategy: Getting Value from Big Data, in WWW July
2011.
x. Sinno Jialin Pan and Qiang Yang, “A Survey on Transfer Learning
“IEEE transactions on knowledge and data engineering, vol. 22, no. 10, october
2010
xi. www.datameer.com,”Beyond BI: Big Data Analytic Use Cases”, in
WWW 2013
xii. Big Data Advanced Analytics in Oracle Database, in WWW 2013
xiii. Thomas H. Davenport, Jill Dyché,” Big Data in Big Companies”, in
WWW May 2013.
xiv. V. Gupta and G. S. Lehal, “A Survey of Text Mining Techniques and
Applications,” Journal of Emerging Technologies in Web Intelligence, vol. 1,
No. 1, August 2009.
xv. “Challenges and Opportunities with Big Data”, a community
whitepaper developed by leading researchers across the United States.
xvi. Kyuseok Shim,” Map Reduce Algorithms for Big Data Analysis”,
http://vldb.org/pvldb/vol5/p2016_kyuseokshim_vldb2012.pdf
xvii. SAS 2013 Big Data Survey, page 1:
http://www.sas.com/resources/whitepaper/wp_58466.pdf.
xviii. David Loshin,” Addressing the five big challenges of big data”, white
paper,www.progress.com,
xix. WWW, 2013,IBM Research, “Analytics-as-a-Service
Platform,”:Http://researcher.ibm.com/researcher/view_project. , in
xx. Anne Kao and Stephen R. Poteet, a textbook on “Natural Language
Processing and Text Mining”, 2012
xxi. Arindam Banerjee,”Data Analytics: Hyped Up, Aspirations or
TruePotential?”www.vikalpa.com/pdf/articles/2013
xxii. Drowning in numbers -- Digital data will flood the planet—and help
us understand it better. The Economist, Nov 18, 2011.
xxiii. Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell,Eunsaeng
Kim, Ann Valencic,”Data Modeling Techniques for Data
Warehousing”,www.redbooks.ibm.com ,Feb1998
xxiv. Jiawei Han, Micheline Kamber, Jian Pei,”Data Mining,concepts and
Techniques”

A Design of Simulating the Field Devices Based on HART Protocol
Betsy Thomas1
, Dr. Suresh.L3
1,2
Department of CSE, 3
CiTech, Banglore Karnataka India
betsypt@gmail.com
Abstract—A design for simulating the field devices based on
HART protocol is mentioned in this paper. The step bystep
procedure for preparing software simulation for HART
devices by loading the SVD file of the device,establishing a
communication with DTM and responding to the messages
are explained in detail. The practical implementation of the
design is used to explore the applications of HART as well as
in the studyfor resolving the interference and noise in the
normal and extreme conditions of a HART loop which
isexpensive to simulate using an actual hardware device.
Keywords – HART protocol, DTM, SVD file
I. Introduction
HART – Highway Addressable Remote Transducer –
is a bidirectional digital communication protocol that provides
data access between intelligent field instruments and host
systems (which can be software like control systems or any
other hardware devices). The communication is based on a 4-
20mA signal. The protocol is now an industrial standard
generally used to create various field devices and their
communication using field bus which serves in integrating the
field instruments with their automated control and management
systems in the industry.
The HART protocol is at present the most widely used
standard in the automation industry with around 30 million
installed devices worldwide and uses various enhancements
including Wireless HART and HART – IP. The newly available
version of HART is its revision 7 put forward by the HART
communication foundation. The study of the various
applications of HART protocol as well as the interference and
noise during a communication with field devices is also a major
area of interest which helps in exploring and improving the
protocol.
At present in software labs, testing the applications
based on HART communication protocol is mainly carried out
by connecting the control systems or other related hardware to
the actual field device. This throws out a few important
disadvantages. First of all the limitations of the hardware will
restrict the testing of the advantages or enhancements of the
protocol thereby the engineers will be forced to update or
restrict the software to adjust with the limitations of the
hardware system. Secondly the installation and maintenance
required for the hardware cost around 25% - 30% effort in
software labs. Finally testing the software in its extreme
conditions is not possible as it lacks the hardware support.
This paper discusses a procedure for the software
simulation of the field devices based on the HART protocol.
The corresponding software can be communicated by DTM in
the same way as it communicates to hardware devices. It mainly
helps in the development, maintenance and enhancement of the
control systems in the industrial automation domain as well as
for exploring the possibilities of HART.
The DTM is software that directly communicates to
a device. It handles device type and knows its specific
parameters, behavior, and limitations. It is used for diagnosis
and parameterization of intelligent field devices via protocol
communication.
II. Literature Survey
HART protocol is an industry standard developed in
late 1980’s and is used worldwide now. The protocol is
maintained by HART foundation [1] having a current revision
of 7.3. Several instruments uses HART interface to realize
their additional capabilities. Huang Han[2] applied the HART
interface to design a temperature transmitter and Chen Quang
[3] used it to design pressure transmitter. Lin Xiaoning[4]
came up with a design for an interface model for the hardware
devices to communicate with its software counterparts.
HART protocol is used by all industry majors in
their devices. The research and implementation related to the
work in this paper is carried out by referring ABB’s tools and
command set guide [5]. The HART USB modem by IFak [6]
is used for implementation for testing the communication
between DTM [7] and the simulator. Several references from
Wikipedia and other sources are used to enhance the core idea
of simulation [8] using HART[9, 10] and its corresponding
implementation
Literature survey is also carried out in order to
analyze the background of the current project which helps to
find out flaws in the existing system guides on which
unsolved problems we can work out. So, the following topics
not only illustrate the background of the project but also done
the testing of DTM without the actual device, so it saves 25-
30% of the lagging time spend on device related problems. A
variety of research has been done to simulate the services of
devices based on the protocols.
III. Design
1. Components of Simulation
The Figure 1 explains the frame format of a packet
send by the objects communicating through HART protocol.
The DTM sends request packets with a command in it. The
device simulator responds with corresponding response codes
for the command in the same frame format.
Figure 1: HART Frame format

Smart Vision Description (SVD) file is a device
information model in xml format. It contains the fields like
General information, Identify Objects, Easy Setup, Observe
objects, Menu, Objects, Records, and Sequences.
Device Simulator loads the SVD file of the device
which it needs to simulate and uses it contents to respond to
requests send by DTM. The data corresponding to each request
from DTM is stored in the SVD file in the form of records.
Upon receiving a read request, the simulator responds with the
data stored in the SVD file. Upon receiving a write request, the
data stored in the SVD file is altered with the given data.
Table 1: HART frame description
For simulating the various use cases, user is given an
option to simulate the data and response. The simulated
information is then used in the communication with DTM.
Figure 2: Communication b/w DTM Device Simulator
There are two types of instructions to which the
simulator responds to. Figure 2 explains the requests type
handled by the simulator. The Simulator can respond to read
requests and write requests. The requirements and parameters
needed for communication (request Ids, response codes etc.)
should be defined in the SVD file of the device. The file should
be loaded in the simulator prior to the communication with
DTM. During a read / write request is processed by the
simulator, the response codes corresponding to the request
will be send back to the DTM.
2. Designing the Simulation Software
The designing of the simulation software includes
automating the components / characters involved in
simulation. The Figure 3 explains a high level design with the
modules to be included in the simulator.
The user interface subsystem displayed in the figure
is optional when compared to the actual goal of this paper.
But this subsystem helps the user to get an idea on the
runtime of the simulator. This subsystem deals with
displaying IN OUT data – communicating between DTM and
simulator, configuring the port for communication and
browsing and loading SVD files. The contents of the
subsystem can either be done manually by giving customized
options for the user in the GUI or can be automated by either
hardcoding the information providing the input and output in
the files.
The next level of the system contains a device model
layer and Simulation service subsystems. The device model
layer logically process the data send by the DTM. As
mentioned earlier, DTM sends either a read or write request.
During a write request, the data send by the DTM needs to be
written into the SVD file or in a memory map corresponding
to the SVD, if the software doesn’t immediately perform a
file operation. During a read request, the data from the SVD
file is send to the DTM as a response.
Figure 3: Logical View HART

Simulation service handles both data and response
simulation. This subsystem is the major component of the
system which constitutes a major goal of this paper. The
simulation service can be used to test the extreme conditions of
the communication by simulating the response and data. This
helps the simulator to overcome the barriers or restrictions of an
actual hardware and explore the possibilities of the device and
protocol. As described for the user interface module, the
simulation can be either automated or can be customized for the
user to interrupt at runtime. The simulated values are given to
the Device model layer to use during the communication with
DTM
The HART layer is the next layer which deals with the
frame format. The purpose of the layer is to get the command
requests from the physical layer, decompose the message
according to the format, transfers the command to the device
model layer, gets the response from back from the device
model, prepare the corresponding response code, compose the
response in the required format and transfer it to the physical
layer.
The physical layer handles the sending and receiving
of the packets. It uses a port to listen the requests from DTM
and send the request to HART layer. Upon receiving the
response from HART layer, it send the information to the DTM
which basically is another software located in either same or a
different machine.
3. Requirements
• SVD files
o Smart Vision Description is in xml format.
o SVD are device information file for the devices of
ttx300 etc.
• Com ports are loaded on both DTM DS
o Using Comport Listener the binary inputs are loaded
in binary format which can be decoded as bytes.
o It uses Virtual comport - “comOcom” to set up the
port pairs. One port is configured in DTM and other
in DS.
o Or it uses Is HRT Driver Configuration to set up the
Serial comports based on the devices like HART-
IFAQ modem with USB. One port is configured in
DTM and other in DS.
4. A sequence diagram
Figure 4 explains sequence diagram of the software
which shows the communication between the modules. The
DTM initially establish a connection with the simulator; send
the request to serial port then to HART protocol. After receiving
the request, the data send to device object model for processing
the simulations via protocols. This shows to the user interface.
If the error occurred while sending the data then it sends the
error response code to the DTM, otherwise it sends the response
data to DTM.
Figure 4: Sequence Diagram
IV. Implementation
The Device Simulator is now tested with ABB
TTX300 (Temperature transmitter) ABB 266 PDP HART
(Pressure transmitter) etc.
The Device Simulator is constructed with HART
Data Link Layer and HART Application layer. The physical
layer needs FSK (Frequency Shift Keying) overlapping of 4-
20 mA primary signals and the secondary digital signal. The
Device Simulator is a simulation service. So, it does not need
the physical layer. The HART Data Link Layer of Device
Simulator will separate the command number from the
request. The command number will be sent to Application
Layer. The Application layer will recognize the command
number whether it is read command or write command. If it is
a read command, the responses are collected from the .xml of
temperature transmitter and send response to Data Link
Layer. If it is a write command, the data from DTM is
uploaded into Device Simulator. For read and write, The Data
Link Layer will frame the response in the format of HART
frame and sent to DTM. This frame consists of Preambles,
Delimiter, Address, Command number, Byte Count,
Response Bytes, Data, and Checksum.
To implement the software concepts explained in
this paper, software is developed in C# as a windows
application using the concepts of WPF, MVVM and Prism.
The implementation uses a GUI to load SVD file of the
devices and simulating the data and response information.
COM port is configured for communication and the
connection is established with the DTM. The results of the
implementation also encouraged the developers to extend the
idea to be used for other communication protocols like
PROFIBUS and MODBUS. As mentioned in the goals of the
paper, the software will be used for exploring the various
possibilities of HART, studying the noise and interference

and their possible solutions and for the enhancements of the
control system software without having the additional cost of
requiring a hardware device for each development environment.
IV. Results
Figure 5: Response simulation in Device simulator
Figure 5 have a screenshot of the device simulator in action.
The simulator is loaded with ABB’s pressure transmitter device
ABB 266DSH. The port is configured in DTM and is selected
in the simulator. The response codes are simulated. When a
request arrives from DTM in background, the new codes will be
send back as response data.
V. Conclusion
The design explained in this paper has wide
generalization. It can be used to simulate a wide range of
HART protocol based field devices. HART protocol,
although be a transitional agreement; due its popularity in the
automation industry, has an extended life cycle and wide
market. Hence the simulators have an important role to play
in exploring the applications of HART as well as
communication with control system. Moreover the design can
also be extended to load and simulate the devices of other
protocols such as PROFIBUS and MODBUS as well.
REFERENCES
i. HART Communication Foundation, HART communication
protocol application guide
ii. Huang Han. The design of intelligent temperature transmitter
based on the HART protocol
iii. Chen Qiang. The design of intelligent pressure transmitter based
on HART protocol
iv. Lin Xiaoning, A Design of Interface Model Based on HART
protocol
v. ABB, HART – Protocol – Overview of HART commands for
standard software
vi. http://www.ifak-system.com/
vii. https://extranet.mm-
software.com/fdtspec/Specification/Fundamentals/TheDeviceTypeManagerD
TM.htm
viii. http://en.wikipedia.org/wiki/Simulation_software
] http://en.wikipedia.org/wiki/Highway_
Addressable_Remote_Transducer_Protocol
ix. http://en.hartcomm.org/hcp/tech/aboutprotocol
/aboutprotocol_how .html

ICCIT15 @ CiTech, Bangalore 51
Energy Efficient Contention and TDMA MAC Protocols for Wireless Sensor
Networks: A Survey
Anitha K.,Dr. Usha S.
Department of CSE, RRCE, Bangalore, India
anithakrishna14@gmail.com, sakthivelusha@gmail.com
Abstract— The Medium Access Layer (MAC) is one of two
sub layers that make up the Data Link Layer. The MAC in
Wireless Sensor Network is less important to save energy
than, in recent years it is important to have high packet
delivery ratio and low latency when an emergency event
occurs. In this paper MAC protocol classified into three
categories Contention based, TDMA based, Hybrid, in which
advantages and disadvantages are discussed in each category.
The future improvements in design of MAC protocols are
discussed.
Keywords— Wireless sensor networks (WSN), Hybrid
Protocol, CSMA, TDMA
I. Introduction
The WSN is described as it is a network consisting of
sensor nodes and communicating wirelessly. However, WSNs
are different from typical computer networks in that each node
consists of a one or more sensor, processing unit, and low-
power radios and battery operated. These nodes are installed in
unattended environment with limited battery and sensing
capabilities. The sensor nodes are depleted in health monitoring
applications, Once a battery is depleted, it is often very
difficult, if not impossible, to recharge or replace it, so the node
is considered dead. Another example application is forest, but a
sensor network may be deployed by dropping nodes from a
plane. In this case there is no control over the network topology
and no way to recharge the batteries.
The WSNs consist of distributed tiny sensor nodes. Since the
major power consuming component of sensor node is the radio
which is controlled by the MAC protocol. The batteries are
cannot be rechargeable. That is why the primary objective is to
maximizing the life time of sensor nodes, and secondary
objective is to improve the performance metrics. The driving
force behind WSN research is to develop energy-efficient and
performance improved MAC protocol for WSN.The major
source of energy wastage are [1]:
• Idle listening: MAC protocol cannot tell when a
message will be sent. Therefore, the radio must be kept on at all
times or a node would miss some of the messages being sent to
it. This is so-called idle-listening.
• Overhearing: The node receives message that is
intended for another node.
• Collision: The node needs retransmission of packets
due to collision.
• Control packet overhead: Energy is consumed while
transmitting control packets used in control data transmission.
In this paper we analysing evolution of MAC protocol. The
remainder of paper structured as follows. Section II gives
performance metrics used in design of MAC protocol. Section
III presents three categories Contention based, Scheduled
based, Hybrid MAC Protocols. Section IV concludes the
paper.
II. Performance Metrics
In order to design MAC protocol the following
performance metrics need to be considered [1].
• Energy consumption per bit: It is defined as total
energy consumed per total number of bits transmitted in
network joules/bit.
• Average delivery ratio: It is defined as total number
of packets received per total number of packet sent.
• Latency: The delay taken by packet to reach the sink
node.
• Throughput: The total number of packets transmitted
to the sink per unit time.
III. Categories of MAC Protocols
The MAC protocol has two modes.
• DCF (Distributed Coordination Function)
Mode with no central device controlling the communication.
DCF is based on CSMA/CA concept. The CSMA/CA can
work in any of the following ways. The first way is Carrier
sensing: a node which is having data to send senses the
medium. If it is idle, the node transmits the data frame. If the
medium is busy, the node waits until it becomes idle again.
The node waits for a random time and transmits. The receiver
node sends ACK (acknowledgment) control frame after
receiving data frame. While transmitting data collision
occurs, then node wait for random time and try again.
The Second method is Virtual carrier sensing: a node wants to
transmit a data senses the medium. If it is idle, the node sends
a RTS (request to send) control frame, which contains the
intended receiver address and the transmission delay time. If
the destination node agrees to communicate, it will answer
with a CTS (clear to send) control frame which also contains
the delay. The source node can send data that must be
acknowledged by ACK. All other nodes cannot transmit data
frames until the medium is idle again and hearing RTS or
CTS.

• PCF (Point Coordination Function)
this function contains a access point (AP), a special node polls
every node and controls the communication process. A beacon
control frame with parameters and invitations to join the
network is broadcasts periodically by AP. IEEE802.11 works
on PCF and it supports TCP and IP, which in turn provide
access to the Internet this way they can send information to
anywhere in the world. Disadvantage is control and data packet
overheads. The MAC protocols are mainly classified into
Contention based and Schedule or TDMA based protocol. The
TDMA based protocol can be represented in following
categories [2].
• Time Division Multiple Access (TDMA): In this
several nodes share same frequency channel with the different
time slots. This gives advantages of collision free, since each
node will have predefined time slot for transmission or
receiving. The synchronization and scalability are drawbacks.
• Frequency Division Multiple Access (FDMA): It
provides different carrier frequency for radio spectrum. This
needs additional hardware for communicating different
frequency. This leads to more cost on sensor nodes.
• Code Division Multiple Access (CDMA): It uses
spread spectrum technology and special codes are used. The
single channel is multiplexed to multiple users. The special
coding requires more computation is a major drawback of this
type.
A. The contention based MAC protocols
The contention based MAC protocols based on carrier sense
multiple access (CSMA) and carrier sense multiple
access/collision avoidance (CSMA/CA) approaches. In the
WSN nodes want to communicate they contend with each
other. The node want to send message it sense the medium, if
found free then node sends the information. If the channel is
not free node has to wait for random time. In this method there
is no guaranteed to be successful. It is used when nodes are not
assigned fixed time slot for sending data. The contention based
protocol classified as sender –initiated and receiver-initiated
protocols. In sender initiated packet transmissions are initiated
by the sender node. Single-channel sender-initiated protocols –
the total bandwidth is used as it is, without being divided.
Multi-channel sender-initiated protocols – available bandwidth
is divided into multiple channels; this enabled several nodes to
simultaneously transmit data. In the receiver initiated protocol
the receiver node initiates the contention resolution protocol.
Contention-based protocols with reservation mechanisms
reserving bandwidth a priori to use. It is categorized into
Synchronous protocols require time synchronization among all
nodes in the network. The synchronization is made globally it is
called global time synchronization is generally difficult to
achieve. In addition synchronization can be done locally.
Asynchronous protocols do not require any global time
synchronization; usually rely on relative time information for
effecting reservations.
Asynchronous protocols based on preamble sampling
technique .The node maintain its own schedule to process the
information. The node cannot be active for long period it has
to wake up periodically to check data is available. This
method reduces cost for synchronization, but it is sending
long preamble with the data to intended receiver. This long
preamble utilizes the channel for longer period this leads to
limited throughput. There are various asynchronous MAC
protocols are designed for various applications. BMAC
(Berkely MAC) [3] also use preamble sampling in addition it
sends clear channel assessment (CCA) before preamble
sampling. This is called as Low Power Listening
(LPL).BMAC does not solve hidden terminal problem. Sparse
topology and energy management (STEM) [3] protocol is
designed to overcome hidden terminal problem. In STEM it
uses two radios one for data and another for preamble
sampling. STEM-T uses traditional preamble sampling
method except separate data transmission channel. STEM-B
(STEM-Beacon) [3] uses series of beacon packets are used
for preamble sampling. Beacon packet contains address of
both sender and intended receiver. The collision, hidden
terminal problems cannot be overcome due to long preamble
in these protocols. The long preamble is a problem that can be
overcome by packetization in ENBMAC (Enhanced
MAC).Based on the gap between the packets it is categorised
into continuous preamble sampling and Strobed preamble
sampling. According to that time a node will decide to stay
active or in sleep mode. X-MAC [3] protocol uses a series of
short preamble packets with the destination address
embedded in the packet. It is a kind of strobed
preamble sampling protocol in which after sending first
preamble and successfully received at the receiver
acknowledge(ACK) will be sent and node can send data
immediately. Hence we can avoid idle listening and
overhearing and reduce the data transmission delay and
energy efficient protocol. RC-MAC [5] is a receiver initiated
protocol coordinates multiple sender’s transmissions by
piggybacking a scheduling message to an ACK.
Synchronous MAC protocols [3] Time Synchronization
is required so that receiver remains awake when
sender sends the message. In Time Synchronization Period,
first step is the node listen the SYNC packet which contains
the sleep schedule of the neighbours from that it setting up the
sleep schedule of the neighbour. Once the node receives
its neighbour’s sleeping schedule, it adopts that schedule and
re-transmits the schedule for other neighbouring nodes to
adopt. If a node does not receive a SYNC packet within a pre-
decided timeout period, the node will set and broadcast its
own schedule. Border nodes (nodes between two active
schedules) may receive two different schedules from different
nodes. The border nodes can either adopt both or one of the
schedules. This node acting as a bridge between two clusters.
SMAC (Sensor MAC) [4] is a synchronous protocol
consisting of three stages SYNC, ACTIVE, SLEEP stages. In
SYNC node wait for sleeping schedule of neighbouring
nodes, if node does not received from neighbours node
broadcast its own scheduling time its neighbour. In ACTIVE
stage nodes can communicate with the exchange of RTS
(Request to send) and CTS (Clear to send) and sends the data.

After sending information they go to SLEEP period. By using
handshaking signals RTS-CTS collision and overhearing can be
avoided. The T-MAC (Time out MAC) [4] is based on dynamic
sleep period. T-MAC active period is dynamically adjusted
based on network traffic. The T-MAC allows the node to sleep
when there is no network traffic. The advantages of contention
based MAC protocols are: These protocols have good
scalability that indicates it gives better performance when
nodes can be included or any changes in the network. These
protocols do not require cluster head formation, so cluster
overhead can be reduced. The drawbacks are collision, idle
listening, and excessive control over head.
B. TDMA based MAC protocols
TDMA [3] based MAC protocols are also called as
Contention-based protocols with scheduling mechanisms. It
assigns unique time slot for each individual node in the network
to send or receive data. The problem faced in contention based
protocol such as hidden terminal problem, collision can be
overcome .The interference between the nodes while message
passing guaranteed to be eliminated ,hence it is called as
collision free protocol. The control word overhead can be
eliminated. The examples of TDMA based protocols are µ-
MAC, DEE-MAC.
µ-MAC assigns a schedule to each node based on prediction of
traffic behaviour. It works in two modes contention period and
contention free period. In contention period based on prediction
of the traffic, assigns the slots to each node in the network. In
contention free period the data is send between nodes.
Disadvantage is knowledge of traffic in the network is
impossible to predict. DEE-MAC works on cluster formation
phase and transmission phase. In cluster formation phase it
forms a cluster head based on the battery power. After making
a cluster head in transformation phase it is dived into
contention period and data transmission period, in contention
period each node radio is on it sends the information of time
slot to cluster head. After contention period, the cluster head
knows which node has data to transmit then decides TDMA
slot and broad cast to each node, then depending on the time
slots nodes are awakened. The collision and hidden terminal
problem can be eliminated but it consists of some drawbacks. It
requires special hardware for synchronisation and latency is
more for data. There is an overhead on cluster head to assign
slot for each node .TDMA is based on fixed time slot so it is
difficult to adopt time slots for change in traffic.
C. Hybrid MAC protocol
In recent years, there has been a design of hybrid protocol
[7], which combines the advantages of contention based
protocol with that of TDMA based protocol. All these protocols
divided transmission channel into two parts. The first part is
control packets which is send in contention period, second part
data transmission which is send in scheduled slot. The hybrid
MAC protocol exhibits better scalability, high energy
efficiency then compared to contention based protocol and
TDMA based protocol. The some of recently designed hybrid
protocols are Z-MAC (Zebra MAC), Q-MAC, ER-MAC,
IHMAC, MDP-MAC.
Z-MAC [8] is a hybrid MAC protocol. It combines the
strengths of TDMA and CSMA. Z-MAC uses DRAND for
time slot assignment algorithm used in Z-MAC. The nodes
are allotted different time slot. The each node allowed to
transmit in their own time slot. In this protocol highest
priority given to owner slot then the non owner slot. ER-
MAC (Emergency Response MAC) [9] hybrid protocol that
based on CSMA and TDMA approaches. ER-MAC initially
communicates using CSMA/CA with a random-access
mechanism. During the start-up phase, the data gathering tree
and TDMA schedules are created. ER-MAC has a pair of
queues to separate high priority from low priority packets. In
this firstly, schedules collision free TDMA time slots, then
the node wake up for their scheduled slots, otherwise node
will move to power-saving sleep mode. When an emergency
situation occurs, nodes used for the emergency monitoring
change their MAC behaviour to TDMA mechanism to
achieve high delivery ratio and low latency. Q-MAC (Queue-
MAC) [10] saves more energy by avoiding contention by a
node that owns a slot, was developed in Z-MAC. Also it
improves on Q-MAC by eliminating permanently on cluster
heads thus saving more energy. Queue-length aware MAC
(Queue-MAC) [10] is a multi-hop beacon enabled hybrid
MAC protocol that addressed the issue of fixed cycle of
ER-MAC. It uses fixed CSMA duty cycle and the dynamic
TDMA duty cycle. This makes frames to be dynamically
adjusted depending on the traffic for the transmission of
more packets within a frame. Similarly, CSMA and TDMA
are used interchangeably according to volume of traffic. The
Q-MAC protocol saving energy that would have been wasted
for idle listening and collisions, it is also used for applications
having fluctuating traffic. The overhead energy cost increases
due to beacon, ACK packets and updating of the queue length
indicator lead to limits the performance of Q-MAC. The IH-
MAC [12] also combines TDMA and CSMA. The IH-MAC
is completely different from hybrid MAC protocol. In IH-
MAC, each node calculates its own slot locally and
independently, this is very ﬂexible. Moreover, the IH-MAC
uses broadcast scheduling and link scheduling dynamically to
improve the energy efficiency. The IH-MAC dynamically
switches from broadcast scheduling to link scheduling based
on the network loads. Another important feature of IH-MAC
is that it reduces energy consumption by suitably varying the
transmit power and it reduces the latency by exploiting the
concept of parallel transmission. Furthermore, IH-MAC uses
Request-To-Send (RTS), Clear–To-send (CTS) handshakes
with methods for minimizing packet loss
probability.MDP(Markov Decision Process)[13] based
centralized channel access MAC based on CSMA/CA and
TDMA hybrid methods. This scheme contains a central
controller that requires the information of traffic in the
network. It works in two modes ,that is CAP(Contention
Access period)in this mode it transmits the information to
coordinator using CSMA/CA ,on the other hand it transmits
the packet using TDMA in CFP(Contention Free Period).It
uses Markov decision process to access channel in contention
period and contention free period. The table-I gives the
comparison of MAC protocols.

TABLE I
[1][2][3][7]COMPARISION OF MAC PROTOCOLS
Protocol Type Synchronization
needed
Scalability
B-MAC Asynchronous
contention
based
No Good
X-MAC Asynchronous
contention
based
No Good
S-MAC Synchronous
CSMA,
Contention
-based
No Good
T-MAC Synchronous
CSMA,
Contention
–based
No Good
DEE-
MAC
TDMA based Yes Weak
Z-MAC CSMA and
TDMA based
Yes Good
ER-MAC CSMA and
TDMA based
Yes Good
IV. Conclusion
In this paper we have discussed various categories of MAC
protocols. The energy efficiency is a major factor for designing
a MAC protocol. The MAC protocols are categorized into
major three types. We have analyzed from these categories that
the contention based protocol and TDMA based protocol are
less energy efficient and also couldn’t give better result under
dynamic traffic load. It is better to use for designing a MAC
protocols both contention and TDMA based methods. The
contention method faces a idle listening problem and control
word over head, TDMA needs strict clock synchronization. The
contention based protocol performs well under low contention
and TDMA under heavy traffic. Hence the advantages of
Contention based and TDMA protocols are considered, which
gives hybrid MAC protocol. These protocols are complex in
implementation. In recent years several MAC protocols are
designed by researchers. In addition, the research can be done
on security for MAC protocol. The node mobility in health care
applications is another research area in MAC protocol.
References
i. Chander Shekhar, Priyanka Kaushal, Kota Solomon Raju ,
“Energy Saving Mechanisms in Hybrid Media Access Control Protocol for
WSNs” , International Journal of Applied Engineering Research, ISSN 0973-
4562 Vol.7 No.11 (2012).
ii. Pei Huang, Li Xiao, Senior Member, IEEE, Soroor Soltani,
Student Member, IEEE ,Matt W. Mutka, and Ning Xi, Fellow,”The Evolution
of MAC Protocols in Wireless Sensor Networks:A surey “, IEEE
Communications Surveys Tutorials, vol. 15, NO. 1, First Quarter 2013.
iii. Rahul R Lanjewar, Dr D S Adane “Comparative Study of MAC
Layer Protocols in Wireless Sensor Networks: A Survey “,International
Journal of Engineering Trends and Technology (IJETT) – Volume 12
Number 1 - Jun 2014.
iv. SONG Wen-miao, LIU Yan-ming, ZHANG Shu-e “Research on
SMAC protocol for WSN” IEEE, 2008 .
v. Ilker Demirkol, Cem Ersoy, and Fatih Alagöz, Bogazici
University , “MAC Protocols for Wireless Sensor Networks: A Survey”,
IEEE Communications Magazine, April 2006.
vi. M. Riduan Ahmad, Eryk Dutkiewicz and Xiaojing Huang,” A
Survey of Low Duty Cycle MAC Protocols in Wireless Sensor
Networks”, Emerging Communications for Wireless Sensor Networks,
ISBN:978-953-307-082-7, 2011.
vii. Sumita Nagah1, Arvind kakria2, ” Hyrbrid MAC protocols for
wireless sensor network, International Journal of Emerging Technologies in
Computational and Applied Sciences, , March-May, pp.217-220 , 2013.
viii. Injong Rhee, Senior Member, Ieee, Ajit Warrier, Mahesh Aia,
Jeongki Min, And Mihail L. Sichitiu, Member ” Z-Mac: A Hybrid Mac For
Wireless Sensor Networks”, IEEE/ACM Transactions On Networking, Vol.
16, No. 3, June 2008.
ix. Lanny Sitanayah,Cormac J. Sreenan,Kenneth N. Brow, ” ER-
MAC: A Hybrid MAC Protocol for Emergency Response Wireless Sensor
Networks”, Fourth International Conference on Sensor Technologies and
Applications, 2010 .
x. Shuguo Zhuo , Ye-Qiong Song , Zhi Wang, Zhibo Wang , ”Queue-
MAC: A queue-length aware hybrid CSMA/TDMA MAC protocol for
providing dynamic adaptation to traffic and duty-cycle variation in wireless
sensor networks” 9th
IEEE International work shop ,2012.
xi. B.Priya ,S.Solai Manohar, “CH-MAC: Congestion Control
Hybrid Mac For Wireless Sensor Network “,4th
international conference of
the Computing communication and networking(ICCCNT) technologies ,
JUNE 2013.
xii. Mohammad Arifuzzaman, Student Member, IEEE, Mitsuji
Matsumoto, Senior Member, IEEE,and Takuro Sato, Fellow, IEEE,” An
Intelligent Hybrid MAC With Traffic-Differentiation-Based QoS for Wireless
Sensor Networks”, IEEE Sensors Journal, vol. 13, NO. 6, JUNE 2013.
xiii. Bharat Shrestha, Ekram Hossain, Senior Member, IEEE, and Kae
Won Choi, Member, IEEE “Distributed and Centralized Hybrid CSMA/CA-
TDMA Schemes for Single-Hop Wireless Networks”, IEEE Transactions on
Wireless Communications, vol. 13, no. 7,July 2014.
xiv. Ibrahim Ammar, Irfan Awan and Geyong, ”An Improved S-MAC
Protocol Based on Parallel Transmission for Wireless Sensor Networks”,
13th International Conference on Network-Based Information Systems,2010.
xv. Wei Wang,Honggang Wang,Dongming Peng,Hanmid Sharif, “An
energy-efficient MAC protocol for wireless sensor networks “, 21st
International Annual Joint Conference of the IEEE Computer and
Communications Societies (INFOCOM 2002), New York, NY, USA, June,
2002

ICCIT15@ CiTech, Bangalore ! #$$
Detecting a Malicious Node using Voting and Secondary Path Techniques in
MANETs
Bhargavi M. N.1
, Sushma B Malipatil3
, Dr. L. Suresh4
1,2,3
Deptt of CSE 4
CiTech, Banglore Karnataka India
Abstract : Mobile ad hoc network is one of the fast
emerging areas in the present world. These mobile ad hoc
networks are self-organizing, self-administering without
the need of any particular predefined infrastructure. So we
consider these networks are infrastructure less. In this
paper, we are trying to overcome some drawbacks that are
present in the existing work of MANETs. One such
drawback is that, when communication in the MANETs is
based on randomly generated keys. Even though these keys
provide security while transmitting the packet or data, they
fail to detect the malicious node present in the network. So,
in order to overcome this challenge, as a proposal of the
work, two techniques are applied. First being voting and
another is secondary path technique. It is observed that
these proposed techniques are able to detect the malicious
node at very good rate and even able to find the shortest
path and secure path from source to destination node.
Meanwhile this technique provides still more security by
enabling the message integrity, delay and Qos etc.
Keywords: randomly generated keys, voting and
secondary path technique.
I. Introduction
The mobile ad hoc network is autonomous collection of
mobile devices like phones, laptops etc which communicates
with each other through wireless links in order to provide
necessary network connections. These mobile ad hoc
network does not require any fixed infrastructure, thus they
got the name has infrastructure less networks.
Mobile ad hoc networks provide wide range of network
applications in the present world. i.e, we can browse the
internet connections from any place and at any time across
the globe.
Since MANETs have a specific nature where nodes in the
network can join and leave easily. Because of this nature it’s
difficult to design, develop and implement the constant
routing path. And one of most important issue that we
observe here in MANETs is security. Why security is
challenge or issue in MANETs, because they are more
vulnerable to attacks. Where hackers can easily modify the
data. So it is necessary to provide security for MANETs in
order to safeguard the data and protect against hackers. Some
of the security requirements are availability, integrity,
confidentiality, authentication and non repudiation.
Comparing wired network with wireless links, most two
common challenges has been find out that is time and cost.
Time required for setting up the devices at one particular
place and the maintenance cost. But these kinds of problems
cannot be seen in wireless network, because they can be
configured themselves and set up the connection immediately.
Figure 1 and Figure 2, are the simple diagram of cellular
networks and mobile ad hoc network.
Figure 1: Network Structure
Figure 2: WSN Structure
Thus finally this paper presents a novel approach to provide
security so as to avoid the misuse of data during transmitting
process from source to destination.
The structure of the paper goes like this section 2 briefs about
recent research in security of MANETs communication.
Detailed design and its implementation with result have been
explained in section 3. Finally, section 4 conclude the paper and
gives an outlook to further research.
II. Literature Survey
Preeti and sumitha[2] has proposed security challenges that are
presently faced by the network. Here BFOA(bacterial foraging
optimization algorithm) algorithm behaves like bacteria that is
it exhibits the behaviour of bacteria. For example if bacteria
enter human body it spread to other parts and affects. Likewise
in terms of security this algorithm spread to the entire network
and secure the ad hoc network.
Li shi chang et al[4] specified about security architecture design
and providing security to MANETs and finding out the security
threats that affect the behaviour of MANETs.OSI reference

ICCIT15@ CiTech, Bangalore %'( )*+
model used to design the security architecture. The research
on each layer of OSI model has been done. Thus this OSI
model gives framework for planning and designing safe
network.
Shakshuki et al[5] analyzed about MANETs and specified
that they are more vulnerable to malevolent attackers. In
order to prevent such kind of issues, author et al specified to
implement the intrusion detection mechanisms to protect
MANETs from attacks during development stage.
Reference [6] tells about the components of security level of
MANETs. The security level architecture, its categorization
and applications are specified.
Reference [7] in this paper, they highlighted the mobile ad
hoc network challenges and its operations. This paper
describes about the limitations in MANETs like bandwidth,
power backup and computational capacity, cost etc. thus
these factors affect the security and make MANETs more
vulnerable to attacks.
Reference [8] Al zubaidy made a specification on optimal
key generation problem for a threshold security scheme in
MANETs. Nodes in this kind of network have limited energy
and critical security issues.
Reference [9] in this paper, they concentrated on the energy
desires. And they have measured delay, packet delivery ratio
and routing overhead to calculate security algorithm.
Reference [10] in this paper, few drawbacks has been traced
out. While generating keys for nodes in the network,
automatically keys have been generated for malicious node
also.
Since in this paper we observe all the keys have been sent to
the threshold node, but there is no guarantee if threshold
node itself is a malicious node, so here we can observe lack
of security. And thus there are chances of misusing the
private data.
One more disadvantage is creating the alternative path for
sending keys consumes more time. As a result it shows the
effect on energy, battery, memory, bandwidth etc.
In order to overcome these drawbacks novel approach has
been proposed. This has been explained briefly in design and
implementation section of this paper.
III. Design and Implementation
To overcome previous drawbacks, we have proposed two
techniques one followed by another, see below strategies.
Strategy 1: Voting algorithm
This algorithm distinguishes the nodes in the network among
highest and lowest performing ratio. In order to make this
procedure to work, following steps are required.
First set up the network. Then make sure that details of all
the nodes that are present in the network are stored in the
server. Server will send the registration key for the nodes
that has been registered with it. In order to make the network
secure. Once the network is established properly, neighbour
nodes are detected using neighbour discovery algorithm.
After this procedure, the server or head node will give voting
request to all the registered nodes. Once voting is done the
server will analyze between the highest and lowest
performing nodes and store there details separately in a table.
The nodes with highest performance are considered as trusted
nodes and with lowest performance are un-trusted nodes.
Strategy 2: Secondary path technique
When creating the path using secondary path technique, we
distribute n no of path among the network in order to find out
the shortest path. During this path selection process the path can
be established even among the un-trusted nodes since this node
details has already stored by server it will tell us that they are
malicious node and helps us to deny the communication. Thus
by storing the information about malicious node previously we
can able to detect the culprit. Finally using this proposed work
we can able to detect the malicious node present in the network.
This paper is still an ongoing project; hence simulation and
results are still pending.
IV. Conclusion
A novel approach has been presented in this paper, to detect the
malicious node present in the network. Here voting algorithm
play an important role in finding out a un-trusted nodes that is
malicious node. Finding out the malicious node make sure that
private data is protected and this data achieve message integrity,
QOS, delay etc.
Finally using this proposed work we are able to establish the
secure communication between source and destination
meanwhile data integrity is achieved at very good rate.
References
i. K. Dhanalakshmi, B. Kannapiran, and A. Divya. Enhancing manet
security using hybrid techniques in key generation mechanism. In Electronics
and Communication Systems (ICECS), 2014 International Conference on,
pages 1–5, Feb 2014.
ii. P. Gulia and S. Sihag. Article: Review and analysis of the security
issues in manet. International Journal of Computer Applications, 75(8):23–26,
August 2013. Published by Foundation of Computer Science, New york USA.
iii. Nikola Milanovic Miroslaw Malek, Anthony Davidson, Veljko
Milutinovic. Routing and security in mobile adhoc networks. In Published by
the IEEE Computer Society,pages 61–65, 2004
iv. L. Shi-Chang, Y. Hao-Lan, and Z. Qing-Sheng. Research on manet
security architecture design. In Signal Acquisition and Processing, 2010.
ICSAP ’10. International Conference on, pages 90–93, 2010.
v. Shakshuki, E.M. and Nan Kang and Sheltami, T.R. Eaack:a secure
intrusion-detection system for manets. volume 60, pages 1089–1098, 2013.
vi. M. Qayyum, P. Subhash, and M. Husamuddin, “Security issues of
data query processing and location monitoring in MANETs”, International
Conference on Communication, Information Computing Technology (ICCICT),
pages 1–5, 2012
vii. S. J. Sudhir Agrawal and S. Sharma, “A survey of routing attacks
and security measures in mobile ad-hoc networks”, JOURNAL OF
COMPUTING, VOLUME 3, ISSUE 1, ISSN 2151-9617, pages 41–48, 2011.
viii. Javad Pashaei Barbin, Mohammad Masdari, ”Enhancing name
resolution security in mobile ad hoc networks”, International Journal of
Advanced Science and Technology, pages 41–50, Jan 2013 UE 1, ISSN 2151-
9617, pages 41–48, 2001
ix. Tamilarasi, M and sundararajan, T.VP.secure enhancement scheme
for detecting selfish nodes in manet. In computing,communication and
application(ICCCA),2012 international conference on,pages 1-5,2012
x. Sharing Randomly-Generated Keys via Alternative Route in a
Threshold based Path Oriented Network for Robust Security in MANETs.
Chandrakant N

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