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    ARTICLE

    Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

    Ahmad A. A. Alkhatib*, Qusai Abed-Al

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

    Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

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