An Adaptive Monitoring Service Exploiting Data Correlations in Fog Computing
In smart environments, a big amount of information is generated by sensors and monitoring devices. Moving data from the edge where they are generated to the cloud might introduce delays with the growth of data volume. We propose an adaptive monitoring service, able to dynamically reduce the amount of data moved in a fog environment, exploiting the dependencies among the monitored variables dynamically assessed through correlation analysis. The adaptive monitoring service enables the identification of dependent variables that can be transmitted at a highly reduced rate and the training of prediction models that allow deriving the values of dependent variables from other correlated variables. The approach is demonstrated in a smart city scenario.
This work is supported by European Commission H2020 Programme through the DITAS (Data-intensive applications Improvement by moving daTA and computation in mixed cloud/fog environmentS) Project no. 731945.
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