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The scale of systems employed in industrial environments demands a largenumber of sensors to facilitate meticulous monitoring and functioning. Theserequirements could potentially lead to inefficient system designs. The datacoming from various sensors are often correlated due to the underlyingrelations in the system parameters that the sensors monitor. In theory, itshould be possible to emulate the output of certain sensors based on othersensors. Tapping into such possibilities holds tremendous advantages in termsof reducing system design complexity. In order to identify the subset ofsensors whose readings can be emulated, the sensors must be grouped intoclusters. Complex systems generally have a large quantity of sensors thatcollect and store data over prolonged periods of time. This leads to theaccumulation of massive amounts of data. In this paper we propose an end-to-endalgorithmic solution, to realise virtual sensors in such systems. Thisalgorithm splits the dataset into blocks and clusters each of themindividually. It then fuses these clustering solutions to obtain a globalsolution using an Ant Colony inspired technique, FAC2T. Having grouped thesensors into clusters, we select representative sensors from each cluster.These sensors are retained in the system while the other sensors readings areemulated by applying supervised learning algorithms.

Ant Colony Inspired Machine Learning Algorithm for Identifying and Emulating Virtual Sensors