Efficient data collection from a multitude of Internet of Things (IoT) devices is crucial for various applications, yet existing solutions often struggle with minimizing access delay and Age of Information (AoI), especially when managing multiple simultaneous transmissions and access strategies. This challenge becomes increasingly critical as IoT deployments continue to expand, demanding robust mechanisms for handling diverse traffic scenarios. In this study, we propose a novel approach leveraging Successive Interference Cancellation (SIC) based on adaptive and fixed parameter schemes to address these limitations. By analyzing both throughput and AoI along with access delay, we demonstrate the effectiveness of our adaptive approach compared to the fixed approach, particularly in scenarios featuring heavy and light traffic. Our findings highlight the pivotal role of adaptive approaches in optimizing data collection processes in IoT ecosystems, with a particular focus on minimizing access delay, AoI, and spectral efficiency.

翻译：从大量物联网设备中高效收集数据对于各类应用至关重要，然而现有方案在最小化接入时延和信息年龄方面往往面临挑战，尤其是在管理多个同时传输和接入策略时。随着物联网部署规模持续扩大，这一挑战变得日益严峻，亟需能够处理多样化流量场景的稳健机制。本研究提出一种基于自适应与固定参数方案的新型方法，利用连续干扰消除技术来解决这些局限性。通过综合分析吞吐量、信息年龄及接入时延，我们证明了自适应方法相较于固定方法的优越性，特别是在重载与轻载流量场景下。我们的研究结果凸显了自适应方法在优化物联网生态系统数据收集过程中的关键作用，尤其侧重于最小化接入时延、信息年龄及提升频谱效率。