abstract
- Monitoring ambient air temperature is one of the important operations to ensure resilience and efficiency in large-scale data centers. However, deployment of a data center monitoring system requires recording the location of thousands of sensors which is a labor-intensive task if is done manually. Since Radio-Frequency (RF) based localization solutions in literature are inadequate in the multipath rich environment of data centers, we investigated the possibility of utilizing the measurements of the sensors in localizing themselves. The idea of thermal piloting is to correlate sensor measurements with the expected temperature values at their locations. It can be treated as a classification problem, in which the feature vector is formed by the temperature values at each sensor location across different cooling configurations. The training set is provided by Computational Fluid Dynamic (CFD) simulations. Since classical supervised learning techniques fail to account for the bijective relation between sensor indices and locations, we formulated an extra step based on the Maximum Weighted Bi-partite Matching (MWBM) problem. Experimental results show that the proposed methods can achieve an average localization error of 0.64 meters.