An Efficient Data Aggregation Approach for Underwater Wireless Sensor Networks

The main goal of data aggregation technique is to gather data in energy manner for a long-term network monitoring. In data aggregation technique, the role of an aggregator is to collect sensed data from surrounding environment and transmit the collected data to base station. One part of data aggregation process is applying similarity function in order to minimize redundancy from the raw data and reduce the packet size is being sent to the base station. Our main research focuses on long-term underwater wireless sensor networks (UWSNs), especially in cluster-based UWSNs. In this paper, we evidence the effectiveness of similarity functions on reducing the packet size and minimizing data redundancy of cluster-based UWSNs. We show through the results that Euclidean distance and Cosine distance growth the efficiency of the network in both theory and simulation.