Design of energy-aware QoS routing protocol in wireless sensor networks using reinforcement learning

Nowadays a major class of wireless sensor networks (WSNs) applications require a minimum quality of service parameters to be satisfied while the wireless sensor nodes are mobile. Most of the standard WSN routing protocols greedily choose the neighbor node with the best quality of service (QoS) parameter(s) as a next hop. However, the data packet might be able to be routed through other neighbors as it might require less QoS. So the energy of the neighbor node with the best QoS will deplete earlier than other nodes which will result in the reduction of network lifetime. Therefore, it is important that QoS routing protocols of WSNs be capable of efficiently balancing energy and other resources consumption throughout the network. In this paper, we proposed EQR-RL, energy-aware QoS routing protocol in WSNs using reinforcement learning. We compare the network performance of our proposed protocol with two other protocols (QoS-AODV and RL-QRP). The packet delivery ratio, average end-to-end delay and impact of the different traffic load on average end-to-end delay are investigated. Simulation results indicate the superiority of our proposed protocol over two others by considering different network traffic load and node mobility in terms of average end-to-end delay and packet delivery ratio.