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