Region Threshold Distributed Energy Efficient Clustering (R-TDEEC): A Protocol For Area Monitoring Wireless Sensor Networks Using Reinforcement Learning

In today’s world due to extensive use of internet and its supporting devices has led humans to use cutting edge technologies in their lives. To adopt this technology, we need wireless sensor networks which facilitate the IOT devices for intercommunication. Critical application includes military area monitoring where sensor nodes need to intercommunicate without human intervention. All sensor devices require wireless sensor network to operate which is reliable, energy efficient, robust and provides sufficient security which is one of the key challenges. Existing protocols solution includes variants of clustering based hierarchical-topologies but they are still known to have sub-standard reliability, stability, throughput and network lifetime. To overcome these constraints, it is proposed a hybrid routing network protocol named Region-Threshold Distributed Energy Efficient Clustering. The proposed protocol efficiently utilizes multi-tier function permitting some of the nodes to transmit data packets directly to the sink node whilst others use clustering techniques in the network to send data packets towards the sink node. While sending data packets, the routing between the nodes is established using the PD-BSDRL method. The proposed protocol is also compared with existing protocols such as Enhanced Distributed Energy Efficient Clustering (EDEEC) and Threshold Distributed Energy Efficient Clustering (TDEEC). The simulation results show that the proposed R-TDEEC outperforms in terms of reliability, energy efficiency, network complexity and network lifetime.