Implementation of deep belief neural network on energy efficient Routing Algorithm in WSN
Abstract
WSNs (Wireless sensor networks) have lately gained popularity such as remote tracking, a wide range of applications where information transfer through nodes to base stations necessitates a substantial amount of power. As a consequence, efficient routing methods for forwarding data to the base station should be used to decrease energy utilization and thus prolong the network's life span. Thus, Deterministic energy efficient protocol enhanced with deep learning model is proposed to obtain optimal routing that indirectly improves the life span of WSN. Ant Colony (AC) Environment is considered for system optimization with the goal of picking the proper conceivable clusters in the shortest amount of duration in a feasible cluster. Furthermore, in routing optimization to increase the effectiveness of service, a Newly Designed Enhanced Ant Colony has been suggested, where premier operators were used to increase speed of iteration and choose the shortest path. Ultimately, a Deep Convolution Classifier is used to discover the optimal path. Consequently, compared to other existing methods, our suggested model enhances QoS, reduces energy consumption, and provides superior routing to the sensor nodes, all of which mobile nodes increase their lifespan.
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