Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. While clustering, the entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention for achieving efficient data communication towards the sink node via selected CH and also for increasing the node reachability within the cluster. In this proposed framework, an energy efficient deep belief network (DBN) based routing protocol is developed, which achieves better data transmission through the selected path. Due to this the packet delivery ratio (PDR) gets improved. In this framework, initially, the nodes in the whole network is grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes that belong to the particular cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. At last, the performance of proposed deep network based routing protocol is evaluated using different evaluation metrics they are network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with few existing algorithms. Among all these algorithms, the proposed DBN routing protocol has achieved better network lifetime.