tabu search final
TRANSCRIPT
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(IEEE TRANSACTIONS ON MOBILE
COMPUTING, VOL. 8, NO. 4, APRIL 2009)
A Tabu Search Algorithm for Cluster
Building
in Wireless Sensor Networks
VISHNU DEV LAWANIYAM.TECH. (CAD)
REG. No 1531010025
UNDER THE
GUIDENCE OF
Dr. KINGSLEY J. SINGH PRESENTED BY:
PUBLISHED BY:
Abdelmorhit El Rhazi,
Samuel Pierre, Senior(Member, IEEE)
DEAN,
(SCHOOL OF MECHANICAL DPT.)
SRM UNIVERSITY,
TAMILNADU
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ABSTRACT
The main challenge in wireless sensor network deployment pertains
to optimizing energy consumption when collecting data from sensor
nodes.
This paper proposes a new centralized clustering method for a data
collection mechanism in wireless sensor networks, which is basedon network energy maps and Quality-of-Service (QoS) requirements.
The clustering problem is modeled as a hypergraph partitioning and
its resolution is based on a tabu search heuristic. Our approach
defines moves using largest size cliques in a feasibility cluster
graph.
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Compared to other methods (CPLEX-based method,
distributed method, simulated annealing-based method),
the results show that our tabu search-based approach
returns high-quality solutions in terms of cluster cost and
execution time.
As a result, this approach is suitable for handling network
extensibility in a satisfactory manner.
Index TermsWireless sensor network, energy map, data
collect, clustering methods, tabu search.
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INTRODUCTION
SENSORS :- capture and measure specific elementsfrom the physical world (e.g., temperature, pressure,
humidity).
Two mechanisms are used to reduce energy
consumption:
Message aggregation
Filtering of redundant data
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These mechanisms generally use clustering
methods in order to coordinate aggregation and
filtering.
Clustering methods belong to either one of two
categories:
Distributed
Centralized
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The centralized approach assumes that theexistence of a particular node is cognizant of the
information pertaining to the other network nodes.
Then, the problem is modeled as a graph
partitioning problem with particular constraints thatrender this problem NP-hard. The central node
determines clusters by solving this partitioning
problem. However, the major drawbacks of this
category are linked to additional costs engenderedby communicating the network node information
and the time required to solve an optimization
problem.
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This paper proposes a new centralized clustering
mechanism equipped with energy maps and
constrained by Quality-of-Service (QoS)requirements. Such a clustering mechanism is used
to collect data in sensor networks. The first original
aspect of this investigation consists of adding these
constraints to the clustering mechanism that helpsthe data collection algorithm in order to reduce
energy consumption and provide applications with
the information required without burdening them
with unnecessary data. Centralized clustering ismodeled as hyper graph partitioning. The novel
method proposes the use of a TABU SEARCH
heuristic to solve this problem.
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Data aggregation and data filtering are two
methods that reduce the quantity of data received
by applications.
The aggregation data mechanism allows for the
gathering of several measures into one record
whose size is less than the extent of the initialrecords.
we propose a novel data collection approach for
sensor networks that use energy maps and QoS
requirements to reduce power consumption while
increasing network coverage.
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During the first phase, the applications specify their
QoS requirements regarding the data required by
the applications. They send their requests to a
particular node S, called the collector node, whichreceives the application query and obtains results
from other nodes before returning them to the
applications. The collector node builds the clusters,
optimally using the QoS requirements and theenergy map information.
The mechanism comprises two phases:
ROBLEM FORMULATION7/29/2011
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During the second phase, the cluster heads must
provide the collector node with combined
measurements for each period. The cluster head is incharge of various activities: coordinating the data
collection within its cluster, filtering redundant
measurements, computing aggregate functions, and
sending results to a node collector.
The goal of the clustering algorithm is to
I. Split the network nodes into a set of clustersGi that satisfies the application requirements,
II. Reduce energy consumption,
III. Prolong the network lifetime.
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Clusters are built according to the following
criteria:
Maximize network coverage using the energy map.
Gather nodes likely to hold redundant
measurements.
Gather nodes located within the same zone
delimited by the application.
The considered network contains a set V of m
stationarynodes whose localizations are known. The
communication
model can be described as multihop.
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An application can specify the following QoS
requirements:
Data collection frequency, fq. The network provides
results to the application every time the duration fqexpires.
A measurement uncertainty threshold, mut. If the
difference between two simultaneous measurements from
two different nodes in the same zone (fourth requirement)is inferior to mut, then one of them is considered
redundant.
A query duration, T. The network required for the query
run a total time whose value is equal to T.A zone size step. The step value determines the zone
length. Within a single zone, measurements are
considered redundant. If an application requires more
precision, it could decrease the step value or even ignore
the transfer of such value.
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A TABU SEARCH A ROACH
In order to facilitate the usage of tabu search for
CBP, a new graph called Gr is defined. It is capable
of determining feasible clusters.
Nodes that satisfy Constraint, i.e., ensure zone
coverage, are called active nodes. The vertices of Grrepresent the network nodes. An edge ( i , j) is
defined in graph Gr between nodes i and j if they
satisfy Constraints (8) and (9). Consequently, it is
clear that a clique in Gr embodies a feasible cluster.A clique consists of a set of nodes that are adjacent
to one another.
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Five steps should be conducted in order to adapt
tabu search heuristics to solve a particular
problem:
1. design an algorithm that returns an initial solution,
2. define moves m that determine the
neighborhoodN(s) of a solution s,
3. determine the content and size of tabu lists,
4. define the aspiration criteria,
5. design intensification and diversificationmechanisms.
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The algorithm ends when one of the following
three conditions occurs:
1. All possible moves are prohibited by the tabu lists;
2. The maximal number of iterations allowed has been
reached;
3. The maximal number of iterations, where the bestsolution is not enhanced successively, has been
reached.
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It starts sorting active nodes according to their degree in
graph Gr decreasingly. For each iteration, the first active
node i, not yet covered by the initial solution F0, is selected.
The algorithm determines the largest size clique that
contains the selected active node i with its adjacent nodes in
graph Gr, which have yet to be covered by F0. This clique is
considered a new cluster and node I becomes the clusterhead.
Initial Solution
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The algorithm does not ensure that all nonactive nodesare assigned to a cluster. Consequently, if node i is not
covered by any cluster when the algorithm ends, it is
assigned to a cluster whose head is adjacent to node i.
However, this leads to the fact that an initial solutioncould not be feasible, i.e., nodes made up of at least
one cluster does not consist of a clique in the graph
Gr. A penalty equation to evaluate a solution is
proposed in the following sections.
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The Neighborhood N(s) Definition
Two types of moves are distinguished: the first move
involves an ordinary node, i.e., a nonactive node,and the second move involves an active node. This
is due to the fact that an active node could be a
cluster head and thus build a new cluster.
Furthermore, the third move that involves a clusterhead
and allows removing an existing cluster from a
solution is
also considered.
A Move Involving a Regular Node.
A Move Involving an Active Node.
A Move Involving a Cluster Head.
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COM UTATIONAL EX ERIENCE AND RESULTS
In order to evaluate the performance of these novelalgorithms, they were implemented with the use of
C++ and the Boost Graph Library (BGL) [10] and
tested with sensor networks of different sizes and
topologies. On the one hand, several experimentswere conducted to evaluate the impact of the tabu
search method parameters. On the
other hand, another approach was devised to solve
the partitioning problem based on CPLEX [18] inorder to compare the quality of the solutions found by
tabu search.
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The algorithms run on a 1.80-GHz Pentium 4,
equipped with a Linux server (Red Hat 3.4.4-2), a
1-Gbytememory, and an Intel processor.
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Analysis of the Impact of Tabu Search arameter
The best results are obtained using tabu lists whose
size is similar to the number of nodes, i.e., between 75
and 120.
Impact of the tabu list size on the solution cost.
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Impact of the maximum number of iterations on the
solution cost.
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Comparing execution time (tabu search and CPLEX):
Square topology.
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Comparing Tabu Search-Based to Simulated Annealing-
Based Approaches
Comparing the solution cost (tabu search and
simulated annealing).
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