Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks

Abstract

Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an essential approach for extending the lifetime of wireless sensor networks (WSNs), which makes sensor nodes adapt to changing environments by calculating optimal decision solutions in real time. However, as WSN grows, error propagation cannot keep up with topological changes in the networks, which prevents the sensor nodes’ routing decisions from being timely optimized. In this paper, we propose the DNN-HR algorithm, which optimizes the reinforcement learning process of WSN in three ways. First, this paper constructs a two-level learning structure. Discrete nodes perform reinforcement learning during the interaction with their neighboring nodes. Simultaneously, the neural networks that fit the V values guide the underlying nodes’ learning. Second, the algorithm divides the sensor networks into two-level subspaces according to their geographic locations, and cluster heads are elected in each subspace to form weight incremental propagation trees. Third, each sensor node sends the weight increments it learned to its first-level cluster head. At the first-level cluster head, the weight increments sent by multiple nodes are received and accumulated, and then the results are sent to the second-level cluster head. The base station acquires the weight increments of all the second-level cluster heads and then broadcasts the updated neural network weights to all the WSN nodes. Experiments determine the appropriate heuristic parameters. Compared to other algorithms, the experiments show that our proposed scheme significantly improves the packet delivery rate, node survival rate, and energy standard deviation of nodes.