A method of managing communications in a Supervisory Control and Data Acquisition (SCADA) system, the SCADA system comprising a SCADA server configured for monitoring telemetry data from a plurality of remote industrial assets and providing supervisory control, the method comprising: establishing communication between the industrial assets and the SCADA server of the SCADA system via a private data radio network, wherein the private data radio network comprises a plurality of data radios, and wherein one of the data radios comprises an access point; executing, by the access point, reinforced machine learning on messages transmitted between the data radios and the access point, wherein the machine learning includes: processing header data of a training set of the messages from the access point to the data radios to learn which of the header data correlates to soliciting a response from the data radios; learning response time delays of the data radios for retrieving the telemetry data from the at least one of the industrial assets associated therewith in response to the training set of the messages; and inspecting header data of the messages transmitted by the access point to predict a set of solicitation messages from which the data radios are being solicited for responses and corresponding response time delays; and generating, by the access point, a centrally coordinated access protocol for communicating the responses to the predicted solicitation messages from the data radios to the access point based on which of the data radios (118( are being solicited for responses and the predicted response time delays corresponding thereto, wherein generating the coordinated access protocol comprises efficiently allocating time on shared wireless channels between the data radios and the access point. establishing communication between the industrial assets and the SCADA server of the SCADA system via a private data radio network, wherein the private data radio network comprises a plurality of data radios, and wherein one of the data radios comprises an access point; executing, by the access point, reinforced machine learning on messages transmitted between the data radios and the access point, wherein the machine learning includes: processing header data of a training set of the messages from the access point to the data radios to learn which of the header data correlates to soliciting a response from the data radios; learning response time delays of the data radios for retrieving the telemetry data from the at least one of the industrial assets associated therewith in response to the training set of the messages; and processing header data of a training set of the messages from the access point to the data radios to learn which of the header data correlates to soliciting a response from the data radios; learning response time delays of the data radios for retrieving the telemetry data from the at least one of the industrial assets associated therewith in response to the training set of the messages; and inspecting header data of the messages transmitted by the access point to predict a set of solicitation messages from which the data radios are being solicited for responses and corresponding response time delays; and generating, by the access point, a centrally coordinated access protocol for communicating the responses to the predicted solicitation messages from the data radios to the access point based on which of the data radios (118( are being solicited for responses and the predicted response time delays corresponding thereto, wherein generating the coordinated access protocol comprises efficiently allocating time on shared wireless channels between the data radios and the access point.