Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK.
Sensors (Basel). 2018 Oct 23;18(11):3597. doi: 10.3390/s18113597.
The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.
物联网 (IoT) 的未来设想为数以亿计的传感器与物理环境集成。与此同时，在此基础设施上对电池进行充电和更换不仅会导致高昂的维护成本，而且由于需要处理旧电池，还会产生大量有毒废物。最近，已经开发出了无需电池的传感器平台，这些平台使用超级电容器作为储能，有望实现无需维护和永久运行的传感器。虽然之前的工作重点是超级电容器的特性、建模和超级电容器感知调度，但移动性对电容器充电和整体传感器应用性能的影响在很大程度上被忽视了。我们表明，超级电容器的大小对移动系统的性能至关重要，选择最佳值并非易事：小电容器充电速度快，可以使节点在低能量环境中运行，但无法支持通信或重新编程等密集型任务；另一方面，增加电容器的尺寸可以支持能源密集型任务，但如果节点在低能量区域导航，则可能导致节点根本无法启动。本文研究了这个问题，并提出了一种混合存储解决方案，该解决方案使用自适应学习算法来预测可用环境能量的数量，并根据环境动态地在两个电容器之间切换。基于广泛的模拟和原型测量的评估表明，与固定电容器方法相比，在采集的能量和传感器覆盖范围方面，该方法的性能提高了 40%和 80%。
