Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F121

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 825–834 (2017)

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Abstract. During the last decade, several Internet of Things (IoT) applications has been developed to facilitate machine-to-human and machine-to-machine communication with the physical world by integrating both digital and physical entities through the internet. However, multiple important challenges need to be addressed in order to take the full advantage of these applications. One of the most important of these challenges concerns the management of IoT data, practically the data generated in dynamic and volatile environments and then provided in the form of streaming datasets. To enable reliable IoT applications in such scenario, it is crucial to develop methods that are able to automatically resolve any possible data conflict between diverse information sources in the case where the data is coming in a streaming fashion. In this paper, an incremental evidential conflict resolution method that is able to overcome this problem is introduced. The efficiency and effectiveness of the proposed method have been tested and evaluated through extensive experiments on synthetic datasets. The obtained results have shown that our method achieves a nice performance over different tradeoffs dimensions.

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