Energy-aware Age Optimization: AoI Analysis in Multi-source Update Network Systems Powered by Energy Harvesting

This work studies the Age-of-Information (AoI) minimization problem in information-gathering network systems, where time-sensitive data updates are collected from multiple information sources, which are equipped with a battery and harvest energy from ambient energy sources. In such systems, an information source can deliver its data update only when 1) there is energy in the battery; and 2) this source is selected to transmit its data update based on the transmission scheduling policy. This work analyzes how the energy arrival pattern of each source and the transmission scheduling policy jointly influence the average AoI among multiple sources and develops the closed-form expression of average AoI by analyzing its theoretical properties. For the unit battery case, the closed-form expression of the average AoI under the Stationary Randomized Sampling (SRS) policy space is derived, and the optimal policy is proposed by analyzing the KKT conditions. For the arbitrary finite battery size, the closed-form expression of AoI under SRS policy space with infinite battery capacity is first analyzed. Then based on the result, we construct a proper weight function in Lyapunov optimization to develop a policy named Max Energy-Aware Weight (MEAW), which is proved to achieve 2-approximation in the full policy space. Experimental results validate the theoretical results and show that MEAW performs close to the theoretical lower bound and outperforms the state-of-the-art schemes.