Tiny Intelligence in Fog-Assisted Wireless Sensor IoT Networks:
A Review of Deployment Patterns, Resource Trade-offs, and
Open Challenges
Aygul Z. Ibatova1,* Baumuratova Dilaram2
1 Tyumen Industrial University, Russia
2 Astana International University, Kazakhstan
Emails: aigoul@rambler.ru · Baumuratova.d@gmail.com
Received: February 05, 2026 Revised: March 09, 2026 Accepted: May 12, 2026 ⋆ Corresponding author
ABSTRACT
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.
Keywords: Fog computing Wireless sensor networks Internet of Things TinyML Edge intelligence Federated
learning Resource-aware orchestration