  Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each
group sending its observations on a particular phenomenon to a central
computing platform over a multiple access channel (MAC). The central platform
incorporates a decision fusion center (DFC) that arrives at global decisions
regarding each set of phenomena by combining the received local sensor
decisions. Owing to the diverse nature of the sensors and heterogeneous nature
of the information they report, it becomes extremely challenging for the DFC to
denoise the signals and arrive at multiple reliable global decisions regarding
multiple phenomena. The industrial environment represents a specific indoor
scenario devoid of windows and filled with different noisy electrical and
measuring units. In that case, the MAC is modelled as a large-scale shadowed
and slowly-faded channel corrupted with a combination of Gaussian and impulsive
noise. The primary contribution of this paper is to propose a flexible, robust
and highly noise-resilient multi-signal transmission framework based on Wavelet
packet division multiplexing (WPDM). The local sensor observations from each
group of sensors are waveform coded onto wavelet packet basis functions before
reporting them over the MAC. We assume a multi-antenna DFC where the
waveform-coded sensor observations can be separated by a bank of linear filters
or a correlator receiver, owing to the orthogonality of the received waveforms.
At the DFC we formulate and compare fusion rules for fusing received multiple
sensor decisions, to arrive at reliable conclusions regarding multiple
phenomena. Simulation results show that WPDM-aided wireless sensor network
(WSN) for IIoT environments offer higher immunity to noise by more than 10
times over performance without WPDM in terms of probability of false detection.
