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We consider the problem of recommending resilient and predictive actions foran IoT network in the presence of faulty components, considering the presenceof human operators manipulating the information of the environment the agentsees for containment purposes. The IoT network is formulated as a directedgraph with a known topology whose objective is to maintain a constant andresilient flow between a source and a destination node. The optimal routethrough this network is evaluated via a predictive and resilient Q-learningalgorithm which takes into account historical data about irregular operation,due to faults, as well as the feedback from the human operators that areconsidered to have extra information about the status of the network concerninglocations likely to be targeted by attacks. To showcase our method, we utilizeanonymized data from Arlington County, Virginia, to compute predictive andresilient scheduling policies for a smart water supply system, while avoiding(i) all the locations indicated to be attacked according to human operators(ii) as many as possible neighborhoods detected to have leaks or other faults.This method incorporates both the adaptability of the human and the computationcapability of the machine to achieve optimal implementation containment andrecovery actions in water distribution.

A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks