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seminar presentation
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06-06-2010, 10:47 PM


Presented By:
Dr. S.Arumugam 1
N.Radhika 2
Additional Director, Directorate of Technical Education, Chennai, India
Research Scholar, Government College of Technology, Coimbatore, India


Smart spaces are composed of smart and dummy nodes
which act as the source for providing service. A large
number of cluster groups are formed in the smart space.
Some of these smart spaces are mobile, some are Adhoc
and others are sensor based smart spaces. These smart
spaces work on a service discovery protocol and the
interoperability of these protocols are built in the context
aware middleware which rests in the middleware layer
created between the network and routing layer of the
protocol hierarchy. The nodes within the cluster are
movable and they can move from one peer group to the
other. The request from the users are sensed by the sensor
node and immediately the required service is offered back
to the user. In this paper we have taken the application of
smart educational campus offering four broad services -
admission to the students, Placement allotment - if the
profile of the student satisfies the criteria,
library access in case of requirement and finally the
medical facility given to the students at the time of need.
As the student enters the smart space, his profile is
authenticated and the authentication technique used to
check the validity of the user in the smart campus
environment is by means of digital signature.
Keyword: Smart Space, Clustering, authentication,
Digital signature


Clustering of the nodes is done randomly based
on the service. Nodes that offer a particular service and
remains in the network for the longer period of time are
taken as the cluster head. All the nodes in the peer group
are movable. The nodes which are smart are capable of
offering service and other dummy nodes are the ones
which consumes the service of the provider. The sensor
nodes in the clusters helps to sense the user profile and by
seamless routing the required service is delivered from the
node that is at the shortest distance from the user [1]. The
clustering here is not with centralized nodes but with peer
to peer nodes that offer services to the user. Because the
nodes are peers they have equal power to offer service
and also to consume the service offered. One of the most
important problems with the mobile - sensor nodes is that
the nodes loose their energy and once they become energy
less they automatically tend not to offer service. To
overcome this difficulty some of the nodes can be made
passive or dummy for certain period of time and could be
reactivated latter. Four peer clusters are taken in to
consideration in our work and each of the peer clusters
offers a service . Even if the nodes move within the
cluster or across the clusters, still they continue to offer
their services seamlessly.

2.1 Smart Educational campus

Our aim is to design a smart educational campus with
peer to peer clustering of nodes. The clustering algorithm
chosen is the ant colony algorithm with swam intelligence
to group all the nodes together even if they move across
the smart space [3]. Swam intelligence is an artificial
intelligence technique based on the study of collective
behavior in decentralized self organized systems. Here
ants generate pheromone which detects intelligently the
best node which offers the exact service as requested by
the profile of the user. The personalized seamless request
is transferred to that node and the service is retrieved back
in to the device of the user. Here the smart spaces are
made to interoperate seamlessly by protocol
interoperability. The usage of Ant colony algorithm so far
in the existing work is in routing and also for centralized
optimization. In Peer to Peer system the algorithm is used
to find the next server to hop across. But in our paper the
ant colony algorithm is used for peer to peer seamless
clustering to bring the nodes that offer similar services
together even if some of the nodes move across smart
spaces. The interoperability brings the smart spaces
together and it is the ant colony optimization algorithm
merged with interoperability that brings similar node in a
cluster together [4].Peer to peer clusters are built in the
smart space and these smart spaces rest on a service
discovery protocol. The interoperability of service
discovery protocol is brought about in the context aware
middleware .
Fig1: Clustering of Nodes in smart spaces
2.2 Ant colony Algorithm
Ant colony Algorithm could be applied to clustering of
nodes by swam intelligence [2]. By the intelligence of the
sensor nodes in the smart space, the algorithm could be
applied for connecting the nodes of the cluster together
virtually wherever they move across the smart space.
2.3 Steps in the design of the Algorithm
1) The cluster groups are formed with the header node.
The header nodes are selected based on the condition that
they are smart nodes and secondly depending on the
longevity life of the nodes in the network.
2) All the smart spaces are made interoperable by the
service discovery protocol interoperability. And the
clusters are randomly distributed in the smart spaces.
3) The nodes are free to move within the cluster in the
same smart space and also across smart spaces.
4) Each node is considered to work on the principle as
that of Ant searching for its food. The Ant generally
generates a pheromone which traces the location of the
food grain directly and easily and thus the entire path of
5) Here in our Algorithm optimization of the Ants are
done on the header nodes which are smart ones. And
through the swam intelligence of the sensor nodes the
request of the personalized profile is routed seamlessly
to the best matching node that offers the service and the
required information is given to the user.
6) By this process the nodes that offer similar service
could be traced by the pheromone and could be grouped
together irrespective of the position of the nodes and
thereby optimization in transmission, clustering and
header node tracing could be obtained.


Two protocols primarily considered for routing in our
case is the proactive and reactive protocol. Aodv is
considered for proactive and Dsdv is considered for
reactive protocol study. Routing of service occurs either
by choosing proactive or reactive or partially proactive
and partially reactive protocols. This is being chosen by
the network automatically and seamlessly by taking in to
consideration the distance between the sender and the
receiver [5] and the hop assigned for the transmission.

3.1 Experimental Results of Routing Using

Two main protocols considered are proactive and reactive
protocol and also routing half way through proactive and
other half way through reactive depending on the nodes.
Times vs delay ratio in all these protocols cases are
Fig 2: Time Vs delay in Dsdv Protocol
search is optimized.
on the nodes. And based on the coloring of the nodes the
nodes in turn form clusters. In our Ant Optimization along
with the SOAC algorithm we also inculcated honey bee
optimization algorithm. Here the network corresponds to
the world of honey bees which seek rich nectar source and
finds path with higher capacity to profitable nectar source.
Each hive develops the number of scots to find the most
profitable path for each destination. Each router than
maintains the information received from all the nodes in
the network to calculate the shortest distance to each
destination based on the chosen metrics [6]. In Our
Algorithm, Scots does not maintain the routing table but
seamlessly and automatically decides on the routes. The
Fig3: Time Vs Delay in Aodv+dsdv
packets are then routed from source to the destination
depending on the information of the scouts. So the
mixture of SOAC and honey bee algorithm is adapted.


Swam optimization Ant Clustering is a swam intelligence
based concept used as an optimization tool for optimal
cluster formation in MANET. Its application in graphs
and networks involves ants hopping node to node,
analyzing a set of local variables exposed by the node and
changing the color of the node. A group of neighboring
nodes with the same color form a cluster. The SOAC
algorithm is limited to node coloring only [6]. No
centralized control is need in SOAC. Therefore it is used
in Mobile Adhoc environment. Our algorithm chooses the
header node based on the long livelily of the node in the
network and also based on the distance of the node from
all other nodes in the network.


Our Algorithm originates from finding that in the
societies of ants the larvae and food are distributed
through out the heterogeneous platforms available and
across the heterogeneous platform. This is the different
from that of the algorithm [6] that the food material is
imagined to be arranged in a homogenous pie. For the
explanation of the algorithm we have considered
Multiple numbers of heterogeneous smart spaces
Mobile nodes are scattered on the smart spaces
Each node can be modeled to hop from one
node to another
Each Ant may change the color of the mobile
nodes based on the probabilistic rules
The probabilistic rules requires the global
information for routing
In order to evaluate the metrics we donâ„¢t maintain a
separate table at each node but seamless clustering is done
The SOAC algorithm determines the coloring of the node
and thereby grouping the nodes in to clusters and the
honeybee does the optimization and chooses the
minimum distance even when the nodes moves and joins
some other clusters in and across the smart space


Peer to Peer clustering of the nodes occurs in the smart
space. The service discovery protocol and routing
protocol are chosen by the nodes seamlessly by sensing
the profile of the user without any request-reply query
embedded in it. Personalization of the user is attained.
Security in the service offering node and the the
authentication of the user attaining the service are attained
by means of Digital signature. As the user or the service
offering nodes enters the smart space, the validity of the
user is checked and then the user is given permission to
access the service. This holds good for service providing
nodes as well.


[1] Artur Andrzejak, Sven Graupner, Vadim Kotov, Holger
Trinks, Algorithm for Self Organization and adaptive
service placement in dynamic distributed systems, internet
systems and storage laboratory, HP laboratories, PaloAlto,
September 17th 2002.
[2] Zhenyu. U.Liu, Marto.Z.KwiatKowska, Costas
Constantine, A Biologically Insp ired Optimization to
AODV Routing Protocol, University of Birmingham,
England BIS2TT.
[3] Heinmeling, Albertomontresor, Ozal.P.Bubaoglu, Peer
to peer Document sharing using the Ant Paradigm.
[4] Arne-JorgenBerre,Axel Hahn, David Akahurst, Jean
Bezivin, Aphordite Tsalgatidou,Francois Vermaut,
LeaKutvoeen, Peter.F.Linington, State of the art for
Interoperability Architecture Approaches.
[5] Radhika.N and Dr.S.Arumugam,
Comparison of Mobile reflective Context Aware
M iddleware in Adhoc Smart Space with Cross Layering
Approach.Journal of ComputerScience.
[6] Anandamoysen, Swarm Intelligence based optimization
of Manet cluster formation, University of Arizona, 2006
Ad-Hoc Networks
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