This paper describes approaches to gathering measurement data about moving objects in networks with low bandwidth. The first approach uses Fog computing conception and suggests moving assessing the quality of the measurement data into measuring points. The second approach uses prediction of telemetry quality by mining models. In addition, the paper presents implementation of these approaches based on actor model. As a result, it became possible not only to load balancing among edge and cloud nodes, but also to significantly reduce the network traffic, which in turn brings the possibility of decreasing the requirements for communication channels bandwidth and of using wireless networks for gathering measurement data about moving objects.

This article describes dynamic management’s approach of measurement data streams from moving objects. It allows reducing network traffic and distributing computing all around the measurement acquisition environment. For this purpose, as integration technology of measuring devices, conception of a fog computing is being used. In order to make decisions for switching streams, machine learning methods are being implemented. Experiments proved network traffic’s great reduction of transmissible measurements.