Fault-Tolerant Real-Time Collaborative Network Edge Analytics for Industrial IoT and Cyber Physical Systems with Communication Network Diversity

In developing state of the art applications for Internet of Things (IoT) and Cyber Physical Systems (CPS), most applications software and associated hardware are always developed from ground up. However, this approach always involves huge capital outlay, extend time-to-market for products, and contribute to integration delay, especially in the case of Industrial IoT (IIoT). In this paper, it is shown that already existing, widely-available, and low-cost hardware can participate successfully in collaborative computing for new analytics applications, at edges of IIoT and CPS networks. A well-known, low-cost, embedded digital signal processor (DSP) that is fully integrated in many industries is selected and used as a case study. The selected DSP is made more scalable by applying it to new, novel uses at the edge of an IIoT network. The new application includes using it to design useful waveforms needed for collaborative computing at IIoT network edges. Embedded-C, a programming language that is suitable for programming resource-constrained network edge devices is used to successfully design needed waveforms for the selected DSP. Collaborative computing is achieved by sending the designed waveforms from the network edge, across diverse communication channels, to the fog layer, and then, use it at the fog layer to remove noise in selected IIoT data. Correlation coefficient is positive and high between output of noise removal achieved by waveforms from the low-cost DSP when compared to noise removal achieved by waveforms from systems with more computing resources at the fog layer. This signifies a successful collaborative computing using such legacy, low-cost DSP. For low-cost hardware to participate successfully in distributed, real-time collaborative network edge-computing, impact of communication network disturbances must be examined. Hence, for the selected DSP, impact of network Bit Error Rate (BER) is examined for both wired and wireless networks. It is discovered that wireless channels have lesser BER than powerline communication (PLC) channels that have impulsive noises. Hence, it may be more suitable for real-time fault-tolerant collaborative computing using the selected DSP.