Volume 10 , Issue 2 , PP: 01–10, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Raden Aur Aachman Azakiyullah 1 * , Aiswan Aumanti 2
Doi: https://doi.org/10.54216/IJWAC.100201
Wireless sensor IoT devices increasingly operate under strict energy, latency, and security constraints while generating high-frequency telemetry that cannot be forwarded continuously to remote clouds. This paper presents an event-selective fog microbatching model for wireless sensor streams in which local novelty scoring, fog-side buffering, risk-preserving retention, and energy-aware scheduling are jointly optimized. Unlike conventional anomaly-detection pipelines, the proposed method treats communication reduction as a primary design objective and binds it mathematically to attack-evidence preservation. A reduced feature-level experimental file following the public Edge-IIoTset label structure and selected network/sensor attributes is used to evaluate traffic selectivity, uplink reduction, fog latency, energy saving, and detection performance. The model assigns each observation window a novelty score, suppresses redundant low-information traffic, and groups retained events into load-aware microbatches at the nearest fog node. The proposed model is extended with stochastic retention bounds, microbatch-delay stability, radio-energy equations, and risk-constrained threshold calibration. Experimental results show that the design reduces uplink load and radio-energy consumption while preserving strong attack discrimination across distributed wireless sensor traffic. The findings support a broader use of fog computing as a selective communication-control layer for dense, security-sensitive wireless sensor IoT deployments.
Wireless sensor IoT , Fog computing , Event-selective transmission , Microbatching , Edge-IIoTset , Anomaly-aware scheduling
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