  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.
