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The Wireless Sensor Network (WSN) is a network with limited resources that uses more energy to transmit and receive data. The small sensor nodes are typically difficult to recharge after deployment so that, the routing protocol's energy consumption can have an impact on the lifespan of a WSN. In general, data aggregation is used to lower the quantity of data transferred in a WSN by minimizing and/or eliminating data redundancy at each node. This paper proposes a new energy-efficient hybrid routing protocol to enhance the network lifetime in WSN model. This protocol accomplishes the best data transmission through the selected path. The clustering approach used to distribute WSN nodes must be effective in order for the complete structure to achieve a longer network lifespan. Using the Fuzzy Density Peak Clustering (FDPC) Method, the whole network is divided into multiple clusters during clustering. Furthermore, the cluster head (CH) selection procedure requires careful consideration in order to provide effective data transfer to the sink node via the chosen CH and to improve node accessibility inside the cluster. An Adaptive Donkey Theorem Optimization (ADTO) technique is used to identify the CH necessary for effective data transfer. The data is sent to the sink node over the chosen CH using an efficient Hybrid Deep Marine Reinforcement Learning (H-DMRL) approach. The proposed hybrid routing protocol attains an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The proposed scheme will be implemented in MATLAB platform and the performance is evaluated and compared with earlier clustering and routing scheme with respect to Quality of service parameters such as network lifetime, energy consumption, packet delivery ratio, routing overhead, packet loss, bit rate, throughput, transmission delay, and jitter.
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