Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Wireless sensor networks have been a very important means in forest monitoring applications. A clustered network comprises set of cluster members and one head. The are normally located close to each other, with overlaps among their sensing coverage within the cluster. concurrently detect same event send Cluster Head node. This is where data aggregation deployed remove redundant at cost accuracy, some generated by process might be an outlier. Thus, it conserve aggregated data’s accuracy performing outlier detection before implemented. paper concerns evaluating multivariate (MOD) analysis on fire environment using OMNeT++ MATLAB R2018b. findings study showed that MOD algorithm conserved approximately 59.5% compared equivalent algorithm, such as FTDA which 54.25% for event.