Few heterogeneous nodes, such as nodes with greater computational capability, can be strategically placed in Wireless Sensor Networks (WSN) to maximize network lifetime availability. Despite the success of many heterogeneous WSN clustering algorithms, finding the ideal network structure remains a difficult task due to the large number of alternative sensor clusters. In this research, we present a method for clustering heterogeneous sensor nodes that uses a Genetic Algorithm to maximize the exhaustion of energy. In the DCHGA, the network's framework is dynamically chosen after each round of message transmission. The average improvement above the 2nd-great performing model (using stable nodes) depending upon the first-node-die and then the last-node-die was 33.9% and 14%,. When compared to state-of-the-art approaches, DCHGA considerably prolonged the life of network. In contrast, the improvement in sensor mobility heterogeneity ranged from 12.7% to 9.9%. The network lifetime was significantly increased by the balanced energy consumption, which also allowed the energy in the sensors to drain gradually. DCHGA's computational efficiency can be compared to that of the competition, and the total average experiment time counted 0.7 seconds having a standard deviation of 0.07 seconds.

Dynamic clustering, Energy efficient, Wireless sensor networks, IOT