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Only two of the various applications for Wireless Sensor Networks (WSNs) are environmental monitoring and smart city infrastructure. Mostly the efficacy of these networks depends on the choice of cluster heads, which manage data aggregation and communication. Conventional approaches as the Hybrid Energy-Efficient Distributed (HEED) algorithm have become relatively popular for cluster head selection because of their simplicity and efficiency. These techniques, meanwhile, sometimes assume a homogeneous node distribution—hardly the case in real-world scenarios. From this follows lower network lifetime and less than optimum energy consumption. Extending network lifetime and improving energy economy depend mostly on maximizing cluster head selection in non-uniformly distributed WSNs. The standard HEED approach ignores the non-uniformity in node distribution, so inefficient energy use and reduced performance can follow. This article provides a new way integrating mathematical modeling and statistical analysis with a non-uniform deep artificial neural network (ANN) to improve the HEED algorithm. The proposed method combines this information with the spatial distribution of sensor nodes using a deep ANN, so simulating the cluster head selecting process. Trained to predict perfect cluster heads, the ANN is evaluated in terms of node density, energy levels, and communication expenses. Mathematical modeling of the network's energy dynamics yields validations for the model using statistical analysis. The optimal approach was tested in a simulated WSN environment including non-uniform node distribution. Compared to the traditional HEED method, results reveal a 25% increase in network lifetime and a 30% drop in energy use. Moreover showing a clear improvement in data aggregation performance, the deep ANN-based approach cut the communication overhead by 20%.
Sandip Kanase (Thu,) studied this question.