面向资源受限物联网设备自持续性的能量感知自适应采样
Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices
AI总结：
提出一种基于FSM和TCP Reno启发策略的能量感知自适应采样算法，在资源受限IoT设备中实现自持续并最大化采样率，经EcoTrack设备及欧洲三城数据验证，3000mAh下每日定位24至3000次。
摘要：
在日益增长的物联网（IoT）领域中，智能电源管理算法与能量收集解决方案相结合对于实现自持续性至关重要。本文提出了一种能量感知的自适应采样率算法，专为资源受限、电池供电的物联网设备中的嵌入式部署而设计。该算法基于有限状态机（FSM），并受传输控制协议（TCP）Reno的加法增加和乘法减少机制的启发，最大化传感器采样率，确保功率自持续性而不会导致电池耗尽。此外，我们利用48天内采集的数据对太阳能电池进行了表征，并使用所建立的模型从开源全球数据集中获取能量数据。为验证我们的方法，我们推出了EcoTrack设备，这是一种多功能设备，具备全球导航卫星系统（GNSS）功能和长期演进机器类型通信（LTE-M）连接，支持MQTT协议用于云数据中继。这种多用途设备可用于例如健康与安全可穿戴设备、远程危险监测系统或全球资产追踪器。在来自三个不同欧洲城市的数据上验证的结果表明，所提出的算法能够在最大化每日采样位置数量的同时实现自持续性。在3000毫安时电池容量的实验中，该算法始终维持每天至少24次定位，并达到高达3000次的峰值。
英文摘要：
In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This paper presents an energy-aware adaptive sampling rate algorithm designed for embedded deployment in resource-constrained, battery-powered IoT devices. The algorithm, based on a finite state machine (FSM) and inspired by Transmission Control Protocol (TCP) Reno's additive increase and multiplicative decrease, maximizes sensor sampling rates, ensuring power self-sustainability without risking battery depletion. Moreover, we characterized our solar cell with data acquired over 48 days and used the model created to obtain energy data from an open-source world-wide dataset. To validate our approach, we introduce the EcoTrack device, a versatile device with global navigation satellite system (GNSS) capabilities and Long-Term Evolution Machine Type Communication (LTE-M) connectivity, supporting MQTT protocol for cloud data relay. This multi-purpose device can be used, for instance, as a health and safety wearable, remote hazard monitoring system, or as a global asset tracker. The results, validated on data from three different European cities, show that the proposed algorithm enables self-sustainability while maximizing sampled locations per day. In experiments conducted with a 3000 mAh battery capacity, the algorithm consistently maintained a minimum of 24 localizations per day and achieved peaks of up to 3000.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
