OPTIMIZED BALANCED ENERGY CONSUMPTION THROUGH ENGINEERED CORONA BASED-SENSOR NODE DEPLOYMENT STRATEGIES IN WIRELESS-SENSOR NETWORK

A wireless sensor network (WSN) is composed of a large nwnber of sensor nodes,
relay nodes, and a base station that are deployed randomly in an environment to
collect information. Nowadays, WSNs are getting more widespread use and have
been adapted to a vast array of applications, such as forests, habitat monitoring,
farmlands, precision agriculture, smart homes and military surveillance. As energy
efficiency is the most challenging problem due to the limited battery capacity, it is
necessary for various applications to deploy sensor nodes in as an efficient way as
possible to monitor the event precisely and extend the network lifetime. Traffic
patterns in wireless sensor networks follow a converge-cast (M: 1) pattern. In this
pattern, nodes that are closer to a sink carry heavy traffic loads. These loads result in
energy depletion within the area at an increased rate, and ultimately lead to energy
holes around the sink. The formation of energy holes means that data can no longer be
delivered to the sink on a certain path. This particular time instance has been defined
as "the total network lifetime". Therefore, in order to have an efficient use of sensor
node energy to achieve balanced energy consumption and extend the lifetime of the
WSN, there must be proper placement of sensor nodes in the environment. In this
research work, non-uniform sensor node deployment strategies are proposed, wherein
sensors are deployed in non-corona and corona fashion.
The proposed work consists of six major parts: First, the optimum number of
sensor nodes is found in the sink vicinity to mitigate the energy hole problem and then
to find the optimum energy level of the sink neighbouring node. Second, non-corona
based sensor node deployment strategies have been developed in which the sensor
nodes are distributed in four different layouts, i.e., engineered uniform, random
uniform, engineered Gaussian and random Gaussian layouts. Third, the sensor nodes
are distributed in corona based deployment strategies using arithmetic and geometric
proportions as compared to the engineered uniform corona based deployment
strategy.