Genetic algorithm-based data controlling method using IoT-enabled WSN in power grid

Internet of Things (IoT)-enabled Wireless Sensor Networks is not only an encouraging research domain but also a promising industrial trend which permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. The IoT device plays a significant role in various IoT-based networks and working of such network depends upon the battery power. Once the devices have deployed in the hostile environments, replacement of batteries is not feasible. To address this, challenge a plethora of research has been conducted but IoT networks still suffer one or the other way. In this paper, a genetic algorithm is integrated with an efficient clustering for power grid application where monitoring and controlling process is proposed by using movable sinks in IoT-enabled HWSNs (OptiGeA) . The OptiGeA protocol is depicted for cluster heads election by joining the factor of density, distance, energy and heterogeneous node's capacity for its created fitness function. The investigation analysis of OptiGeA is prepared to work with single sink, multiple static sinks and multiple movable sinks to have an unslanted comparative assessment. The novel deployment technique and multiple mobile sinks approaches are proposed to shorten transmission distance between the sink and CH during system operation and pact with the hotspot issue. It is evident from a simulation study that the proposed OptiGeA protocol outflank state-of-the-art protocols on determinations of particular execution measurements precisely stability period, system's residual energy, network lifetime, throughput, and number of clusters per round execution.

Automated summary

Internet of Things (IoT)-enabled Wireless Sensor Networks is a promising research domain and industrial trend. This paper proposes a genetic algorithm integrated with efficient clustering for power grid applications in IoT-enabled networks. The OptiGeA protocol is developed for cluster heads election based on density, distance, energy, and heterogeneous node's capacity. The investigation analysis of OptiGeA shows superior performance compared to state-of-the-art protocols in terms of stability period, system's residual energy, network lifetime, throughput, and number of clusters per round execution.