Multi-Step Reasoning for IoT Devices

Internet of Things (IoT) devices are growing constantly in numbers, being forecasted to reach 27 billions in 2025. With such a large number of connected devices, the energy consumption concerns are a major priority for the upcoming years. Cloud / edge / fog computing are critically associated with IoT devices as enablers for data communication and coordination among devices. In this paper, we look at the distribution of semantic reasoning between different IoT devices and define a new class of reasoning, multi-step reasoning that can be associated at the level of the edge or fog node in the context of an IoT cloud / edge / fog computing topology. We conduct an experiment based on synthetic datasets to evaluate the performance of multi-step reasoning in terms of power consumption and other metrics. Overall we found that multi-step reasoning can help in reducing computation time and energy consumption on IoT devices in presence of larger datasets.物联网（Internet of Things, IoT）设备数量持续攀升，据预测至2025年将达270亿台。面对如此庞大的联网设备体量，能源消耗问题已成为未来数年的核心研究与优化优先级。云/边/雾计算作为物联网设备间数据通信与协同协作的关键赋能技术，与物联网设备深度绑定。本文聚焦语义推理在不同物联网设备间的分配机制，定义了一类新型推理模式——多步推理，该模式可部署于物联网云-边-雾计算拓扑架构中的边缘节点或雾节点层级。我们基于合成数据集开展实验，从功耗及其他性能指标维度评估多步推理的综合表现。整体实验结果表明，在处理大规模数据集场景下，多步推理可有效降低物联网设备的计算耗时与能源消耗。