Computational Intelligence Based Data Aggregation Technique in Clustered WSN: Prospects and Considerations
Muhammad Umar Farooq Qaisar · 2012
Data aggregation in wireless sensor networks is very important and hot research topic in recent times. Data aggregation is defined as the process of aggregating the data from multiple sensors to eliminate redundant transmission and provide fused information to the base station. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. Data aggregation helps in improving the performance of the wireless sensor network protocols especially the routing protocols which in turn improve the overall performance of the network. Hierarchical networks or Clustering is very important for data aggregation, where the sensor nodes are divided into groups and assigned various roles. Computational Intelligence combines elements of learning, adaptation, evolution and fuzzy logic to solve complex problems. The paradigms of CI include neuro-computing, reinforcement learning, evolutionary computing and fuzzy computing, techniques that use swarm intelligence, artificial immune systems and hybrids of two or more of the above. Paradigms of CI have found practical applications in areas such as product design, robotics, intelligent control, biometrics, and sensor networks. Researchers have successfully used CI techniques to address many challenges in WSNs in various fields including data aggregation. In this paper, the prospects and considerations for a CI based data aggregation technique in clustered networks is discussed and concluded that apart from the conventional data aggregation techniques, there is a need to look for non conventional solutions like CI for making efficient data aggregation techniques.