Bi-Directional Gravity-Oriented Search Optimization Protocol for Wireless Sensor Networks

In the current intellectual milieu, the central focus is directed towards the scientific domain of the IoT, fortified by the framework of wireless sensor networks. A WSN encompasses an assembly of sensor nodes engaged in intercommunication, aggregating environmental data, sending it to a server or base station to facilitate judicious decision-making. In light of their constrained battery longevity and untimely exhaustion, demanding subsequent redeployment, these sensor nodes pose intricate challenges. Recent scholarly pursuits have utilized a clustering approach in order to improve overarching lifespan of network. Utilizing cluster heads facilitates the communication of information from nodes to the central head waiter. Notably absent in current clustering techniques is the optimal methodology for forwarding data from sensors to the server and selecting the most suitable cluster heads. This paper introduces an innovative approach for WSN in the situation of IoT applications, employing Mobile Agents (MA) to assembledata from nodes. The methodology is grounded in the Two-Fold Gravitational Search Algorithm (GSA). In this process, the Mobile Agent's route for congregation facts from feeler nodes undergoes optimization using GSA subsequent to its efficient selection of cluster heads. The optimization criteria encompass variables like nodes' residual energy and the associated communication cost in relation to the base station (BS). In the evaluation of network longevity and energy efficiency, the proposed methodology has been juxtaposed against alternative clustering protocols. Furthermore, the success rate and energy consumption associated with task execution in the MA's itinerary planning, based on the GSA, have been scrutinized in comparison to alternative itinerary planning techniques. Comparative analysis reveals that, in contrast to alternative approaches, the suggested protocol exhibits enhanced association immovability, abridged the energy consumption, and a heightened success rate for MA.