Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems under a unified profiling and prediction platform. Through automated, time-synchronized instrumentation, the framework captures fine-grained energy dynamics across both node and gateway layers. We developed a suite of tools that generate energy datasets for IoT ecosystems, addressing the scarcity of such data and enabling AI-based predictive and adaptive energy optimization. Validated within a network-level IoT testbed, the approach demonstrates robust performance under real operating conditions.

翻译：能源效率已成为可持续物联网网络演进中的关键约束因素。本研究超越了基于仿真或设备中心的研究范式，提出了基于实测的网络级智能能耗分析方法。所提出的系统实现了分布式物联网基础设施中能量流的端到端可视化，将低功耗蓝牙与可见光通信模式、环境感知子系统及电子墨水显示子系统集成于统一的性能分析与预测平台之下。通过自动化时间同步的仪器化部署，该框架能够捕获节点层与网关层的细粒度能量动态特征。我们开发了一套工具集，可为物联网生态系统生成能耗数据集，解决了此类数据的稀缺性问题，并支持基于人工智能的预测性与自适应能耗优化。该方法在网络级物联网测试平台中进行了验证，在实际运行条件下展现出鲁棒的性能表现。