A Systematic Review of Data Aggregation using Machine Learning Techniques

Abstract

Wireless Sensor Networks (WSNs) are usually employed to address the data transfer using sensors nodes associated with dynamic environment. However, this data transmission gets significantly challenged when huge data need to be transferred via WSN. Numerous researchers had proposed various Data Aggregation (DA) schemes to resolve this issue, however, due to service quality and security factors it persisted to challenge the Quality-of-Service (QoS) delivered using WSN. The present review had discussed the concept and the process involved in DA. Further, the paper mainly surveyed the research published in the last decade for DA in WSN using Machine Learning (ML) approaches. The basic architecture involved in data transfer using nodes in while performing DA is discussed followed by the list of various ML approaches that have been implemented in the recent past for DA. The review is based on the articles published in various authenticated journals. Finally, the analysis is summarized to lay foundation of further research aimed at the enhancement of performance of data transmission via DA in WSN using ML approaches.