A Distance-Based Data Aggregation Technique for Periodic Sensor Networks

Monitoring phenomena and environments is an emergent and required field in our today systems and applications. Hence, wireless sensor networks (WSNs) have attracted considerable attention from the research community as an efficient way to explore various kinds of environments. Sensor networks applications can be useful in different domains (terrestrial, underwater, space exploration, etc.). However, one of the major constraints in such networks is the energy consumption that increases when data transmission increases. Consequently, optimizing data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors and extend network lifetime. In this article, we propose an efficient data transmission protocol that consists in two phases of data aggregation. Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor. In the second phase, it uses distance-based functions to find similarity between sets of collected data. The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads (CHs) in a clustering-based scheme network. To evaluate the performance of the proposed protocol, experiments on real sensor data from both terrestrial and underwater networks have been conducted. Compared to other existing techniques, simulation and real experimentations show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data.