Adaptive Data Aggregation with Mobile Agents and Evolutionary Computing based Clustering in Sparse Wireless Sensor Networks

Abstract

The Information processing based on Data mining in WSN is at its starting stage, when compared to traditional machine learning in WSN. In order to solve a particular problem in WSN the researchers now a day were mainly focused on applying machine learning techniques. The Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences will result to a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary in order to overcome these differences. The improvement or optimizing of the performance of the entire network in terms of energy conservation and network lifetime will be one of the major objectives in wireless sensor network. This paper will survey the Data Mining in WSN applications from two perspectives, namely the network associated issue and application associated issue. In the network associated issue, different machine learning algorithms applied in WSNs were used in order to improve network performance will be discussed. In application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.