Enhancing Data Collection of Tree-Based Aggregation in Wireless Sensor Networks

We investigate the following fundamental questionhow fast can information be collected from a wireless sensor network organized as tree To address this, we explore and estimate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first take time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Then, we combine scheduling with transmission power control to mitigate the effects of interference, and demonstrate that while power control helps in reducing the schedule length under a single frequency, and scheduling transmissions using multiple frequencies is more efficient. We provide lower bounds on the schedule length when interference is completely removed, and propose algorithms that achieve these bounds. We also calculate the performance of various channel assignment methods and find empirically that for moderate size networks of regarding 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. After that, the data collection rate no longer remains limited by interference but by the topology of the routing tree. Finally, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant development in scheduling performance over different deployment densities.