Genetic Algorithm Based Data Controlling Method Using IoT Enabled WSNs

Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based applications. These applications span a wide range from industry to education, and from military to agriculture. The IoT device plays a significant role in various IoT-based networks, and the functioning of such network depends upon the battery power. Once the devices are deployed in the hostile environments, replacing batteries becomes impractical. Despite a plethora of research addressing this challenge, IoT networks still face issues. In this paper, a genetic algorithm based data monitoring and controlling method using IoT enabled WSNs is proposed by using movable sinks in IoT enabled HWSNs (OptiGeA). The OptiGeA protocol is designed for the election of cluster heads (CHs) by incorporating factors such as density, distance, energy and heterogeneous node capacity into its fitness function. The investigation of OptiGeA is conducted with single sink, multiple static sinks and multiple movable sinks provide an unbiased comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to reduce the transmission distance between the sink and CH during system operation and address hotspot issue. It is evident that the OptiGeA protocol shows an increment of 10.44