Abstract
Applications of wireless sensor networks are blooming for attacking some limits of social development, among which energy consumption and communication latency are fatal. Effective communication traffic control and management is a potential solution, so we propose a novel traffic-control system based on deep reinforcement learning, which regards traffic control as a strategy-learning process, to minimize energy consumption. Our algorithm utilizes deep neural network for learning, inputs the state of wireless sensor network as well as outputs the optimal route path. The simulation experiments demonstrate that our algorithm is feasible to control traffic in wireless sensor network and can reduce the energy consumption.
•In this paper, an artificial agent is adopted to control traffic in WSNs for energy efficiency.•We set the learning goal of the agent as minimizing energy consumed during mobile agent’s movement.•The artificial agent learns from experience and outputs the optimal action to take in WSNs.•We train the artificial agent using reinforcement learning with neural network and achieve good performance in simulated WSNs.•Applying deep reinforcement learning to control traffic in WSNs, and redefining each part of reinforcement learning in combination with WSNs.•A simulation experiment is designed and compared with other algorithms in WSNs to verify the efficiency of deep reinforcement learning.