Opportunistic Network Routing Strategy Based On Node Sleep Mechanism

Opportunity networks are wireless AD-hoc networks deployed in complex and harsh environments. To mitigate energy loss in opportunistic network routing, we propose a model called Nodes Sleep Scheduling based on Q-learning (NSQ) by combining the node sleep mechanism with classical opportunistic network routing algorithms when the nodes in the network have sufficient buffer space. In the NSQ model, the node sleep scheduling process is modeled as a Markov decision process, the corresponding state set, action set and reward function are defined, and the Q-learning algorithm iterates the value function. The model continuously iterates and converges through the self-learning process of the value function in the nodes, autonomously learns, and ultimately obtains an optimal sleep scheduling strategy. The experimental results show that the NSQ model can effectively reduce nodes' energy consumption and has certain self-learning abilities compared with other node sleep scheduling strategies.