Abstract
A wireless sensor network (WSN) are normally deployed in harsh environments to collect and deliver data to a remotely located base station. In a sensor network it is very important to know about the position of the sensor node (SN) and data collected by that node, as it has a significant impact on the overall performance of the WSN. Grouping SNs to form clusters has been adopted widely to overcome the scalability problem. It has been proved that for organizing a network into a connected hierarchy, clustering is an effective approach. In this chapter, we address the localization and clustering techniques in WSN, challenges/issues in providing localization and clustering for WSN, and the use of computational techniques for localization and clustering algorithms. We also outline the recent research works on the use of computational intelligence (CI) techniques and future challenges that need to be addressed in providing CI techniques for localization and clustering.
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