Fog-Assisted Trust and Anomaly-Aware Scheduling for Wireless
Sensor IoT Devices
Arash Salehpour1,* Tamara Zhukabayeva2
1 Department Cybersecurity, University, Istanbul, Türkiye
2 Eurasian National University, Kazakhstan
Emails: arashsalehpour@halic.edu.tr · Zhukabayeva_tk@enu.kz
Received: February 23, 2026 Revised: March 01, 2026 Accepted: May 03, 2026 ⋆ Corresponding author
ABSTRACT
Wireless sensor Internet of Things (IoT) devices increasingly generate time-sensitive traffic that cannot be efficiently
inspected only in a remote cloud. Fog computing reduces the distance between sensing devices and decision
logic, but fog nodes must jointly manage latency, queue pressure, wireless channel variability, energy use and
security risk. This paper presents FogSense-TSA, a trust-aware and anomaly-aware scheduling model for wireless
sensor IoT traffic in fog computing environments. The model integrates traffic intensity, wireless link behaviour,
fog-resource state and temporal trust into a local decision process that determines whether a device-window should
be accepted, quarantined at the fog layer or escalated to cloud inspection. The empirical analysis is conducted using
a reduced analysis-ready file aligned with a recent public IoT device-identification and anomaly-detection setting.
The proposed formulation introduces three algorithmic components: online trust-risk scheduling, load-aware fog
placement and adaptive threshold calibration. Mathematical analysis is provided for evidence aggregation, trust
stability, latency decomposition, energy cost, constrained placement and computational complexity. The results
show that fog placement substantially reduces service latency relative to cloud-only routing while preserving high
anomaly-discrimination capability. The strongest predictors are trust score, flow intensity, jitter, fog CPU load,
payload entropy and queue pressure, indicating that fog-layer security should be coupled with wireless access and
resource conditions rather than treated as a separate classifier. The study provides a reproducible and interpretable
basis for designing lightweight security and scheduling modules for wireless sensor IoT deployments.
Keywords: Wireless sensor networks Internet of Things Fog computing Anomaly detection Trust-aware scheduling
Wireless IoT security