通过参数优化提高 IoT-NDN 的能效

Future Internet Pub Date : 2024-02-16 DOI:10.3390/fi16020061
Dennis Papenfuß, Bennet Gerlach, Stefan Fischer, M. A. Hail
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引用次数: 0

摘要

物联网包括通常不被视为计算机的物体、传感器和日常用品。物联网设备受到能源、内存和计算能力的严重限制。在物联网中采用 NDN 是最近解决这些问题的一种方法。要深入了解不同网络参数对能耗的影响,使用超参数优化分析一系列参数似乎是合理的。本文基于 ndnSIM 的超参数设置实验表明,数据包大小对能耗的影响最大,其次是缓存方案、缓存策略,最后是转发策略。这些参数的能量足迹相差好几个数量级。令人惊讶的是,数据包请求序列对缓存参数能量足迹的影响要大于图的大小和拓扑结构。在能源消耗方面,结果表明数据压缩可能比预期更重要,而缓存可能比转发策略更重要。本研究开发的 ndnSIM 框架可用于更有效地模拟 NDN 网络。此外,这项工作还为进一步研究以前未研究过的特定参数组合的效果奠定了宝贵的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
The IoT encompasses objects, sensors, and everyday items not typically considered computers. IoT devices are subject to severe energy, memory, and computation power constraints. Employing NDN for the IoT is a recent approach to accommodate these issues. To gain a deeper insight into how different network parameters affect energy consumption, analyzing a range of parameters using hyperparameter optimization seems reasonable. The experiments from this work’s ndnSIM-based hyperparameter setup indicate that the data packet size has the most significant impact on energy consumption, followed by the caching scheme, caching strategy, and finally, the forwarding strategy. The energy footprint of these parameters is orders of magnitude apart. Surprisingly, the packet request sequence influences the caching parameters’ energy footprint more than the graph size and topology. Regarding energy consumption, the results indicate that data compression may be more relevant than expected, and caching may be more significant than the forwarding strategy. The framework for ndnSIM developed in this work can be used to simulate NDN networks more efficiently. Furthermore, the work presents a valuable basis for further research on the effect of specific parameter combinations not examined before.
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