Monitoring large IoT networks is challenging, especially when devices operate in remote environments and faults cannot be easily observed. This paper introduces a semantics-aware active fault detection approach, where a monitor intelligently decides when to probe a remote sensor to obtain fresh status information. Instead of sending probes periodically or reactively, the system evaluates the importance of initiating a probe by considering two key factors:
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Freshness of information using the Age of Information (AoI) metric
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Uncertainty in the device’s health status, represented through entropy
By jointly assessing these factors, the monitor sends probes only when they are meaningful reducing unnecessary network traffic and improving overall maintenance efficiency.
The authors model the problem as a Partially Observable Markov Decision Process (POMDP) and analytically demonstrate that the optimal probing policy follows a threshold structure. They further introduce a lightweight stochastic approximation algorithm to estimate this optimal threshold in practice.
Numerical results show that this semantics-aware strategy significantly outperforms conventional delay-based probing policies, achieving lower cost and fewer redundant probes, especially in networks with intermittent link or device faults. The study highlights the promise of semantics-oriented communication for future autonomous IoT maintenance systems.
Semantics-Aware_Active_Fault_Detection_in_Status_Updating_Systems