| Summary: | Green communication for different kinds of wireless networks has begun to receive significant research attention recently. Green communication focuses mainly on the issue of improving energy efficiency substantially. A wireless sensor network (WSN) consists of a large number of randomly and widely deployed sensor nodes, and these nodes themselves have the ability to wireless communicate, detect and process data. Sensor nodes can thus detect their surrounding environment, and transmit related data to a sink via wireless communication. This study proposes two two-tier data dissemination schemes based on Q-learning for wireless sensor networks. In the proposed schemes, a source node uses Q-learning to find the most energy efficient data dissemination path from the source node to the sink. The first scheme is called TTDD-QL, and the second scheme is called TTDD-QL-A which is an advanced version of TTDD-QL. In TTDD-QL, the reward is determined by the distance between the current dissemination node and the sink. In each iteration, the proposed scheme will update the Q values. After multiple learning iterations, the Q values are converged, and the data dissemination path is found according to the Q values. In TTDD-QL-A, the reward is determined not only by the distance between the current dissemination node and the sink but also by the remaining energy of the current dissemination node. Simulation results show that TTDD-QL and TTDD-QL-A can reduce sensor node energy consumption and extend the lifetime of the WSN.
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