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 by performing an outlier data detection process before data aggregation is implemented. This paper concerns evaluating multivariate outlier detection (MOD) analysis on aggregated accuracy of data generated by a forest fire environment using OMNeT++ and performing the analysis in MATLAB R2018b. The findings of the study showed that the MOD algorithm conserved approximately 59.5% of aggregated data accuracy, compared with an equivalent algorithm, such as the FTDA algorithm, which conserved 54.25% of aggregated data accuracy for the same event.
Highlights
Wireless sensor networks (WSNs) have been deployed in various fields, including monitoring applications such as forest monitoring, target monitoring, security monitoring and fence monitoring [1]
The findings of the study showed that the multivariate outlier detection (MOD) algorithm conserved approximately 59.5% of aggregated data accuracy, compared with an equivalent algorithm, such as the Fault-Tolerant Data Aggregation (FTDA) algorithm, which conserved 54.25% of aggregated data accuracy for the same event
Tabs. 12 and 13 contain the aggregated data computed by the cluster head (CH) node and the aggregated values of the data sent by the cluster members (CMs) nodes without any false data for both events
Summary
Wireless sensor networks (WSNs) have been deployed in various fields, including monitoring applications such as forest monitoring, target monitoring, security monitoring and fence monitoring [1]. CMs often generate redundant data, where part of the event data might be outliers caused by data redundancy, errors, noise and missing data [2,3,4] To overcome this problem, data aggregation algorithms have been deployed in WSN to remove redundant data and decrease the number of transmissions in the clustered network, but aggregation is performed at the cost of the accuracy of the final aggregated data [1,5]. Accuracy degradation of aggregated data is mainly caused when the CH node receives outlier data. This is important especially in decision-making activity [6] about emergencies such as forest fires
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