Improving lifetime of wireless sensor networks based on nodes’ distribution using Gaussian mixture model in multi-mobile sink approach

Saving energy in Wireless Sensor Networks (WSNs), is critical in different applications, such as environment monitoring, keeping human awareness and etc. Many studies have investigated energy consumption and improved the WSN lifetime longevity by reducing the energy consumption. Still, proposed approaches overlook the nodes’ distribution role in energy model and routing protocol, which is a key factor in a WSN. In this work, we propose a novel approach; namely GDECA; which assumes nodes’ distributions are mixtures of Gaussian distribution, as an assumption applied in real world. So GDECA rely on a distribution estimation borrowed from Machine Learning (ML) to fit the Gaussian Mixture Model (GMM) to the nodes and calculate the parameters for these distributions. Next, the estimated parameters are employed in Cluster Head CH selection policy. Besides, sinks routing is determined based on nodes distribution. Results showed the improvement close to 40–50% in energy consumption. As another outcome, GDECA keeps all the nodes active until end of the simulation. Observations also demonstrate that sinks path calculation using this approach is optimum, and randomly changing number of sinks increases energy consumption.