The recent advances in Wireless Sensor Network (WSN) have gained world-wide attention because of its minimized size, low cost and un- tethered nature. These sensor nodes has sensing unit to collect the data from physical environment, processing unit to process the data and communicating component to communicate sensory data to base station over wireless medium. WSN placement is very specific to applications such as environment monitoring, habitat monitoring, military applications, health monitoring, object tracking and smart grids. In most of these applications numerous sensors nodes are re- motely deployed and they are set to operate autonomously. WSN designers face many issues that arise due to limited battery, harsh environment, communica- tion failures, memory and bandwidth. Among this, limited power source is the primary issue that needs high priority attention. However, the open challenges in WSN such as network lifetime, connectivity, coverage are all proved as NP complete problems. For this reason several swarm intelligence (SI) techniques have been used to address WSN issues to bring efficient optimization solution. Compared to other communication and topology control protocol, the usage of SI techniques is very minimal among WSN researchers. This depicts that the WSN designers and developers are not fully aware of SI techniques. On the other hand soft computing experts are not completely aware of the WSN issues and the possibility of exploiting SI algorithms in WSN. This survey summaries the research challenges in WSN, introduces several SI techniques and tries to bridge the gap between WSN and SI researchers. It also describes the applica- tion of SI in WSN design and deployment, clustering, scheduling, localization, data aggregation and QoS Management. © 2015 Academic Publications, Ltd.