Abstract
Popularity of wireless sensor networks (WSNs) is increasing day a day where hundreds or thousands of applications are explored. In most of such applications, the need of gathering data periodically about the monitored environment beside the limited, generally irreplaceable, power sensor sources make energy conservation and big data gathering reduction two fundamental challenges in such networks. In this paper, we propose an Adaptive Distributed Data Gathering (ADiDaG) technique for saving energy in periodic WSN applications. ADiDaG works into rounds where each round consists of three phases: data gathering, sampling decision, and transmission. These phases respectively use Map reduce, longest common subsequence similarity and grouping approach in order to search data redundancy and adapt sensor sampling rate at each round. The performance of ADiDaG is evaluated based on both simulation and experimentations where the obtained results show significant energy savings and high accurate data gathering compared to existing approaches.