EDCRA-IoT: Edge-based Data Conflict Resolution Approach for Internet of Things

Abstract

In Internet of Things (IoT), data are collected from various sources (e.g., sensors and databases) to describe a phenomenon under-observation. Due to the lack of knowledge about the measurement environment and the limited accuracy of data sources, the IoT data inevitably appears uncertain, imperfect, and conflicting. This would lead to high data conflict among different data sources, and the final result of data fusion is degraded. This paper presents an edge-based data conflict resolution approach for IoT to tackle these issues while maintaining an accurate and reliable result. In the proposed approach, data conflict is significantly alleviated using two phases. The first is Faulty Data Detection and Correction (FDDC), which runs based on the confidence interval and estimated data. The second is Data Conflict Measure and Fusion (DCMF), which is developed to compute the degree of conflict among different data sources based on Fuzzy measures and calculate each data source’s credibility degree. Then, data fusion is carried out based on the Minimum Mean Square Error (MMSE) criterion accordingly. The simulation results reveal the remarkable effectiveness and efficiency of the proposed approach for solving IoT conflicting data.