In this paper, a fuzzy-logic-based fault detection system is designed for a medical Internet of Nano Things architecture. The goal of this system is to detect the root cause and severity of the faults occurred in the in-body nanonetwork. Since nanomachines have very limited capabilities, the sampled data from the in-body nanonetwork is sent to cloud servers by means of an on-body micro-gateway. The fuzzy fault detection system was designed based on two well-known methods including Mamdani and Takagi–Sugeno-Kang (TSK) fuzzy systems. The performance of the proposed approach is evaluated on a theoretical model of medical in-body nanonetwork from the literature through in silico study. This nanonetwork includes eleven types of nanomachines which cooperate with each other within the arterial wall and interact with low-density lipoprotein (LDL), drug and signaling molecules in order to prevent the formation and development of Atherosclerosis plaques. Any fault in these nanomachines can highly take negative effect on treatment efficiency. The results of computer simulation and comparative study on 37 atherosclerosis patients demonstrate how the proposed approach could successfully detect the root cause and severity of the faults occurred in the nanonetwork.

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