Key result
Computational intelligence using SFFS and Naive Bayes classifies channel congestion for real-time availability decisions.
Why the study?
The response time for remote monitoring and control in real-time systems is sensitive and requires analysis of communication system traffic within pre-established time windows.
The proposed computational intelligence approach allows for supporting decisions about channel availability and predicting time window lengths in real-time remote monitoring systems.
Hypothesis-generating for congestion classification in remote monitoring; leaves open prospective validation before clinical use.
The response time for remote monitoring and control in real-time systems is a sensitive issue in device interconnection elements. Therefore, it is necessary to analyze the traffic of the communication system in pre-established time windows. In this paper, a methodology based on computational intelligence is proposed for identifying the availability of a data channel and the variables or characteristics that affect the performance and data transfer, which is made up of four stages: a) integration of a communication system with an acquisition module and a final control structure; b) communication channel characterization by means of traffic variables; and c) relevance analysis from the characterization space using SFFS (sequential forward oating selection); d) Channel congestion classification as Low or High using a classifier based on Naive Bayes algorithm. The experimental setup emulates a real process using an on/off remote control of a DC motor on an Ethernet network. The communication time between the client and server was integrated with the operation and control times, to study the whole response time. This proposed approach allows support decisions about channel availability, to establish predictions about the length of the time window when the availability conditions are unknown.
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Serna-Guarín et al. (2020) studied this question. Computational intelligence methodology (SFFS and Naive Bayes) was evaluated on Channel congestion classification and response time. A computational intelligence methodology using SFFS and Naive Bayes was proposed to classify communication channel congestion and support decisions about channel availability in real-time systems.
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