Volume 10 , Issue 2 , PP: 18–26, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Aygul Z. Ibatova 1 * , Baumuratova Dilaram 2
Doi: https://doi.org/10.54216/IJWAC.100203
Wireless sensor IoT networks are moving from simple measurement pipelines toward distributed systems where sensing, interpretation, filtering, and coordination are divided across devices, fog nodes, and cloud services. This review examines that transition through the lens of tiny intelligence, with special attention to how small models, local event filters, federated updates, service placement, and privacy controls reshape fog-assisted wireless sensor deployments. The paper does not treat fog computing as a generic latency layer. Instead, it studies fog as a governance and orchestration layer that decides which data should stay at the device, which events should be aggregated nearby, and which models require cloud-level supervision. A structured comparison of prior studies is provided across architecture, TinyML, federated learning, placement, security, benchmarking, and lifecycle coverage. The synthesis shows that the literature has matured in modelling fog resources and building lightweight inference functions, but remains fragmented in lifecycle management, cross-layer wireless awareness, privacy accounting, and reproducible evaluation. The review concludes with a research agenda for sensor-to-fog intelligence pipelines that are adaptive, auditable, energy-aware, and suitable for long-lived cyber-physical deployments.
Fog computing , Wireless sensor networks , Internet of Things , TinyML , Edge intelligence , Federated learning , Resource-aware orchestration
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