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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:

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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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tabu search final

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