University of Kashan Soft Computing Journal 2322-3707 Articles in Press 2022 09 21 Detection and Correction of Conflicting Data Based on Edge Computing in Internet of Things Detection and Correction of Conflicting Data Based on Edge Computing in Internet of Things 112800 10.22052/scj.2022.246016.1042 FA Yasser Hosseini Department of Computer Engineering, Islamic Azad University, Miandoab Branch, Miandoab, Iran Seyyed Yasser Hashemi Department of Computer Engineering, Islamic Azad University, Miandoab Branch, Miandoab, Iran Journal Article 2022 02 09 Edge computing is presented as a new model with a focus on near-resource data processing and processing to address Internet of Things(IoT) needs and localize computing needs, increase power for emergency response time, increase scalability and reduce energy costs, and control privacy and data protection. One of the main challenges in using edge computing is the quality of data obtained from multiple sources. Different sources in the vast and heterogeneous IoT environment receive and send inconsistent and conflicting data from the same phenomenon. This creates a strong need to identify and correct the collected data. In this research, a two-step approach for identifying and correcting the data of each source and then identifying the conflicts between the data of different data sources and merging the sources and the final correction of the data is presented. In the first step, the identification and correction of defective information are performed based on the confidence interval and estimated data. The second step is to measure the conflict and fusion of data, which is created to calculate the degree of conflict in different data sources based on fuzzy measures and calculate the degree of validity of each data source. The proposed approach provides good results on the various types of data conflicts. Based on the simulation results with accuracy, sensitivity, specificity, and F-score criteria, the proposed approach has a good performance and in all conflicts, accuracy, and sensitivity show more than 75%, specificity more than 72%, and F-score criterion more than 73%. Edge computing is presented as a new model with a focus on near-resource data processing and processing to address Internet of Things(IoT) needs and localize computing needs, increase power for emergency response time, increase scalability and reduce energy costs, and control privacy and data protection. One of the main challenges in using edge computing is the quality of data obtained from multiple sources. Different sources in the vast and heterogeneous IoT environment receive and send inconsistent and conflicting data from the same phenomenon. This creates a strong need to identify and correct the collected data. In this research, a two-step approach for identifying and correcting the data of each source and then identifying the conflicts between the data of different data sources and merging the sources and the final correction of the data is presented. In the first step, the identification and correction of defective information are performed based on the confidence interval and estimated data. The second step is to measure the conflict and fusion of data, which is created to calculate the degree of conflict in different data sources based on fuzzy measures and calculate the degree of validity of each data source. The proposed approach provides good results on the various types of data conflicts. Based on the simulation results with accuracy, sensitivity, specificity, and F-score criteria, the proposed approach has a good performance and in all conflicts, accuracy, and sensitivity show more than 75%, specificity more than 72%, and F-score criterion more than 73%.