Fuzzy based Reliable Data Collection and Communication in Artificial Intelligence of Things (AIoT) Networks - Joseph, B. Maria; Baseer, K. K. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Enhancing the efficiency of Internet of Things (IoT) operations is the primary objective of Artificial Intelligence for Things. In harsh environments, IoT nodes are prone to failures due to hardware faults, battery depletion and external events etc. By analyzing the correctness and quality of received data, the IoT device’s quality can be assessed. In this paper, Fuzzy based reliable data collection and communication (FRDCC) is proposed. For detecting the data faults, each device applies machine learning based Autoencoder technique. The fault detection module receives the device readings as input which is then used to monitor the data correctness. In order to ensure reliable data collection and transmission, a set of data collectors are determined by applying Fuzzy logic model. The variables queue size, total energy consumption and reliability index are considered as fuzzy inputs and the selection probability is returned as the Fuzzy output. From experimental results, it was shown that the proposed technique has an higher fault detection rate and data correctness with reduced packet drop and recovery delay.