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.

中文翻译：

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随机能量收集模型下多源更新网络系统中的 AoI 优化


这项工作研究了信息收集无线网络系统中的信息年龄 （AoI） 优化问题，其中时间敏感数据更新从多个信息源收集，每个源都配备了电池并从周围能源（如太阳能、风能等）中收集能量。收集的能量的到来可以建模为随机过程，只有在以下情况下，信息源才能提供其数据更新：1） 电池中有能量，并且 2） 根据传输策略选择该源来传输其数据更新。这项工作分析了每个来源的能量到达模式和传输策略如何共同影响多个来源之间的平均 AoI。据我们所知，这是第一项在稳态随机采样 （SRS） 策略空间中正式发展平均 AoI 的闭式表达式的工作，并在随机能量收集模型下提出了多源系统中具有恒定比率的近似方案。更具体地说，在完美无线信道下，发展了任意有限电池尺寸下 SRS 策略空间下 AoI 的闭式表达式。基于结果，我们提出了最大能量感知权重 （MEAW） 策略，该策略被证明可以在整个策略空间中实现 2 近似。在不确定的无线信道下，我们开发了 Whittle 指数的闭式表达式来解决目标问题。基于该结果，我们提出了能量感知的惠特尔指数策略 （EWIP），并使用 Lyapunov 优化技术证明了其近似性能。 实验结果表明，完美信道设置下的 MEAW 和不确定信道设置下的 EWIP 都接近理论下限，并优于最先进的方案。