AoI Optimization in Multi-Source Update Network Systems Under Stochastic Energy Harvesting Model

This work studies the Age-of-Information (AoI) optimization problem in the information-gathering wireless network systems, where time-sensitive data updates are collected from multiple information sources, and each source is equipped with a battery and harvests energy from ambient energy, such as solar, wind, etc. The arrival of the harvested energy can be modeled as the stochastic process, and 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 policy. This work analyzes how the energy arrival pattern of each source and the transmission policy jointly influence the average AoI among multiple sources. To the best of our knowledge, this is the first work that formally develops the closed-form expression of average AoI in the Stationary Randomized Sampling (SRS) policy space and proposes approximation schemes with constant ratios in multi-source systems under a stochastic energy harvesting model. More specifically, under the perfect wireless channel, the closed-form expression of AoI under the SRS policy space with arbitrary finite battery size is developed. Based on the result, we propose the Max Energy-Aware Weight (MEAW) policy, which is proven to achieve 2-approximation in the full policy space. Under the uncertain wireless channel, we develop the closed-form expression of Whittle's index to address the target problem. Based on the result, we propose the Energy-aware Whittle's index policy (EWIP) and prove its approximate performance by using the Lyapunov optimization techniques. Experimental results show that MEAW under the perfect channel setting and EWIP under the uncertain channel setting both perform close to the theoretical lower bound and outperform the state-of-the-art schemes.