Age-of-Information Minimization for Data Aggregation in Energy-Harvesting IoTs

Energy Harvesting (EH) technology has emerged to prolong the lifetime of Internet of Things (IoT) devices. However, in EH-IoTs, the reliance on external energy sources introduces challenges in maintaining up-to-date information. To quantify data freshness in such systems, researchers have introduced the Age-of-Information (AoI) metric, which measures the time elapsed since the generation of the most up-to-date information received by the user. Consequently, the problem of AoI minimization has been studied extensively in EH-IoTs to ensure timely data delivery. While data aggregation is a fundamental task for IoTs, existing works on AoI minimization in EH-IoTs have only considered scenarios where sensory data is updated by individual source nodes. The problem has not been investigated for data aggregation, in which the sensory data is aggregated from multiple source nodes. In this paper, we study the problem of AoI minimization for Data Aggregation in EHIoTs. To address this problem, we propose an energy-adaptive node scheduling algorithm consisting of both offline scheduling and online adjustment. Extensive simulations and testbed experiments verify the high performance of our algorithm in terms of AoI minimization and energy efficiency.