Owing to recent advances in artificial intelligence and internet of things (IoT) technologies, collected big data facilitates high computational performance, while its computational resources and energy cost are large. Moreover, data are often collected but not used. To solve these problems, we propose a framework for a computational model that follows a natural computational system, such as the human brain, and does not rely heavily on electronic computers. In particular, we propose a methodology based on the concept of `computation harvesting', which uses IoT data collected from rich sensors and leaves most of the computational processes to real-world phenomena as collected data. This aspect assumes that large-scale computations can be fast and resilient. Herein, we perform prediction tasks using real-world road traffic data to show the feasibility of computation harvesting. First, we show that the substantial computation in traffic flow is resilient against sensor failure and real-time traffic changes due to several combinations of harvesting from spatiotemporal dynamics to synthesize specific patterns. Next, we show the practicality of this method as a real-time prediction because of its low computational cost. Finally, we show that, compared to conventional methods, our method requires lower resources while providing a comparable performance.

中文翻译：

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道路交通动态中的计算收集

由于人工智能和物联网（IoT）技术的最新进展，收集的大数据促进了高计算性能，而其计算资源和能源成本却很高。而且，数据经常被收集但未被使用。为了解决这些问题，我们提出了一种计算模型的框架，该框架遵循自然的计算系统（例如人脑），并且不严重依赖电子计算机。特别是，我们提出了一种基于“计算收集”概念的方法，该方法使用从丰富的传感器收集的IoT数据，并将大多数计算过程留给现实世界的现象作为收集的数据。该方面假定大规模计算可以是快速而有弹性的。在这里 我们使用现实世界的道路交通数据执行预测任务，以显示计算收集的可行性。首先，我们表明，由于从时空动力学到合成特定模式的多种收割组合，交通流量的大量计算可抵抗传感器故障和实时交通变化。接下来，由于其计算成本低，我们将其显示为实时预测的实用性。最后，我们表明，与传统方法相比，我们的方法在提供可比性能的同时需要较少的资源。我们显示，由于从时空动态到合成特定模式的多种收割组合，交通流量的大量计算可抵抗传感器故障和实时交通变化。接下来，由于其计算成本低，我们将其显示为实时预测的实用性。最后，我们表明，与传统方法相比，我们的方法在提供可比性能的同时需要较少的资源。我们显示，由于从时空动态到合成特定模式的多种收割组合，交通流量的大量计算可抵抗传感器故障和实时交通变化。接下来，由于其计算成本低，我们将其显示为实时预测的实用性。最后，我们表明，与传统方法相比，我们的方法在提供可比性能的同时需要较少的资源。