Wireless sensor networks consist of many tiny sensor nodes which are deployed in various geographical locations for sensing the normal spectacles and also to transmit the collected information to the base station which is also named destination node through multiple nodes present in the network. Most of the existing heuristics algorithms used for finding the optimal routes have limitations in the provision of effective solutions for routing and clustering mechanisms in larger search spaces. Hence, when the search space increases exponentially, the chance of creating the optimal solution for clustering and routing is decreasing and ultimately an un-optimized process depletes the sensor node resources. In order to address the challenges and limitations present in the existing routing systems, two new heuristics algorithms namely gravitational approach based clustering method and a clustered gravitational routing algorithm have been proposed in this paper for providing an optimal solution for efficient clustering and effective routing. Moreover, a fuzzy logic based deductive inference system has been designed and used in this work for selecting the most appropriate nodes as cluster head nodes from the nodes present in each cluster. The simulation results obtained from this work show that the clustering accuracy and the network lifetime are increased and the energy consumption as well as delay are reduced with the application of these proposed algorithms.

无线传感器网络由许多微小的传感器节点组成，这些传感器节点部署在各个地理位置，以感测正常眼镜，并通过网络中存在的多个节点将收集到的信息传输到基站，该基站也称为目标节点。用于找到最佳路线的大多数现有启发式算法在为较大的搜索空间中的路线和聚类机制提供有效解决方案方面存在局限性。因此，当搜索空间呈指数增长时，为群集和路由创建最佳解决方案的机会正在减少，最终未优化的过程会耗尽传感器节点资源。为了解决现有路由系统中存在的挑战和局限性，提出了两种新的启发式算法，即基于引力方法的聚类方法和聚类的引力路由算法，为有效的聚类和有效的路由提供了最优的解决方案。此外，已经设计了基于模糊逻辑的演绎推理系统，并在这项工作中用于从每个群集中存在的节点中选择最合适的节点作为群集头节点。从这项工作获得的仿真结果表明，通过使用这些算法，可以提高聚类的准确性和网络寿命，并减少能耗和延迟。