Intelligent routing strategy in the Internet of things based on deep reinforcement learning - Ruijin DING; Feifei GAO; Ling XING (物联网学报)
At the era of the Internet of things,networking mode that connects everything would bring tremendous increase in the data volume and challenge the traditional routing protocols.The limitations of the existing routing protocols was analyzed when facing the data explosion and then the routing selection problem was re-modeled as a Markov decision process.On this basis,the deep reinforcement learning technique was utilized to choose the next-hop router for data transmission task in order to shorten the transmission path length while network congestion was avoided.The simulation results demonstrate that the congestion probability can be reduced significantly and the network throughput can be enhanced by the proposed strategy.