  Sensor calibration is one of the fundamental challenges in large-scale IoT
networks. In this article, we address the challenge of reference-free
calibration of a densely deployed sensor network. Conventionally, to calibrate
an in-place sensor network (or sensor array), a reference is arbitrarily chosen
with or without prior information on sensor performance. However, an arbitrary
selection of a reference could prove fatal, if an erroneous sensor is
inadvertently chosen. To avert single point of dependence, and to improve
estimator performance, we propose unbiased reference-free algorithms. Although,
our focus is on reference-free solutions, the proposed framework, allows the
incorporation of additional references, if available. We show with the help of
simulations that the proposed solutions achieve the derived statistical lower
bounds asymptotically. In addition, the proposed algorithms show improvements
on real-life datasets, as compared to prevalent algorithms.
