A fuzzy agent approach for smart data extraction in big data environments

The era of big data has brought new challenges in data processing ad management. Existing analytical tools are now close to facing ongoing challenges thus providing satisfactory results at a reasonable cost. However, the velocity at which new data are flooded and the noise generated from such a large volume leads to various new challenges. The present research combines two artificial intelligence fields the represented by multi-agent technologies and fuzzy logic inference systems in order to extract the needed smart data from big noisy ones. A multi-fuzzy agent-based large-scale wireless sensor network has been used to demonstrate the effectiveness of the proposed approach. It handles sensors as autonomous fuzzy agents to measure the relevance of the collected data and eliminate the irrelevant ones. The results of the simulation exhibit a high quality of the data with a decrease in the sensors energy consumption, leading to a longer lifetime of the network.