FAULT-TOLERANT DATA PROCESSING ARCHITECTURES FOR DISTRIBUTED INDUSTRIAL INTERNET OF THINGS SYSTEMS

Industrial Internet of Things (IIoT) infrastructures connect field devices, industrial gateways, enterprise platforms, and remote services through heterogeneous networks whose performance cannot be assumed to remain stable. A processing architecture may therefore preserve data delivery while losing the timeliness, interpretability, or consistency needed for operational decisions. This conceptual study examines fault tolerance as a coordinated property of data acquisition, processing placement, state management, recovery, and operator oversight. Architectural alternatives are analyzed with particular attention to edge isolation, event replay, duplication, partial failure, and bounded autonomy during disconnection. Two comparative tables distinguish failure responses from the validation evidence required for deployment. The study proposes a failure-domain-oriented approach that separates safety-related functions from analytical services, assigns explicit freshness requirements to data products, and treats degraded operation as a designed condition rather than an exceptional incident. Conclusions are analytical and do not represent experimental benchmarking.