An Approach to Construct Weighted Minimum Spanning Tree in Wireless Sensor Networks - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing - page 66

1 Introduction

Wireless Sensor Network
relatively cheap cost and
likely continue to have im
nodes in a WSN are s
capabilities, processing
decreasing the size of the
circuits have become sm
critical issue while devel
and maintaining any wir
nodes are deployed rando
is formed by self-config
algorithm has the capabil
efficient manner. Fig. 1 de

ks (WSNs) have become more prevalent due to the
increased sensor node processing capability. WSNs wi
mportant roles in both civil and military applications. Th
small devices which possess sensors, communicatio
units and memories and power sources. Progress i
e battery has not kept up with the rate at which electron
maller, and thus, the power source has become a ver
oping WSNs and associated algorithms for constructin
reless sensor network. In many applications, the senso
omly in the application's region of interest. The networ
guration of the nodes; a simple and local distribute
lity for the nodes to configure themselves in an energ
epicts a set of randomly deployed sensor nodes.

eir
ill
he
on
in
nic
ry
ng
or
rk
ed
gy

Fig. 1 240 sensors are deplo
interest. A sink node is dep
represented by the small blu
by the green circle centered a

oyed in uniformly random manner in a 1000m x 1000m area
picted by the large solid red circle while the other nodes a
ue dots. Each node's maximum communication area is depicte
at the node and encircling it.

of
are
ed

In the literature, Minim
make the Connected Dom
backbone for the WSN [
Spanning Tree, in which
research, the authors disc
networks to make the MS
Euclidian MST (also kn

mum Spanning Tree (MST) algorithm has been utilized t
minating Set of the nodes resulting in the communicatio
5]. Much research has been conducted on the weighte
h the nodes are chosen by their rank or weight. In th
cuss two basic approaches to form the wireless senso
ST: Random Nearest Neighbor Tree (Random NNT) an
nown as Geometric weighted MST). The authors hav

to
on
ed
his
or
nd
ve