Abstract
Present trend of Internet of Things (IoT) and sensors deployment increased in every sectors enormously from last one decade. But the deployment challenges of sensors and their networks with respect to their contextual dynamics and system performance is not much investigated. Hence there is a need to investigate the deployment challenges of sensors supporting the computing system that exactly imitates the phenomenon by understanding the context and other influencing parameters, i.e., to sense the environmental parameter values accurately and precisely from the respective embedded sensor system. In this paper, a methodology is proposed to analyze the performance of embedded Wireless Sensor Networks (eWSNs) with respect to energy efficiency based on sensors deployment. The method involves in clustering the sensor nodes based on distance from the phenomenon and its physical location. Sensors and sensor network lifetime energy consumption for data acquisition is analyzed using Markovian model. Simulation platform for random deployment of sensor nodes along with Self Organizing map neural network for clustering with various cases of sensors deployment, network dynamics and environment are studied to understand the performance of the embedded WSN system for energy efficiency.
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