Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection
Anfeng Liu, Xiao Liu, Tianyi Wei, Laurence Tianruo Yang, Seungmin Rho, Anand Paul · ACM Transactions on Embedded Computing Systems · 2017
A multi-representative re-fusion (MRRF) approximate data collection approach is proposed in which multiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.