Transmission Scheduling for Solar-Powered Wireless Monitoring with Data Immediacy.

In this paper, a green wireless environment monitoring system is proposed, in which multiple solar-powered clients with sensor nodes can sense the data from the environments and send them back to a server via time-division multiple access. The age of information (AOI) is considered in the design objective to ensure the freshness of information in solar-powered wireless communications. To this end, a Q-learning (QL) approach is proposed to schedule the data transmission of multiple clients based on solar, channel, battery, and buffer conditions. Real experiments are conducted to validate the effectiveness of the proposed system and compare the age of data performance with the conventional round-robin scheduling.