Optimized Application Driven Scheduling for Clustered WSN

The widespread use of applications through Wireless Sensor Network (WSN) demands an adaptive management system. One of the issues in autonomous WSN is finding the optimal solution for minimizing energy consumption while taking into account the application data rate requirements. This trade-off is done by modifying the size and composition of the MAC superframe. In this paper, an optimized application-aware superframe scheduling for clustered WSN is presented that optimizes the trade-off between power consumption and sensor data acquisition rate, by utilizing a differential evolution algorithm to find the optimal size and composition of the MAC superframe. The approach presented also leverages a cluster network topology in the design of the network. It also considers multi-superframe configurations that have not been previously leveraged in the literature. Simulation comparisons leveraging LEACH-C show that our approach can optimize the 802.15.4 superframe size and composition for particular sensor data rates and improve energy consumption in the network compared to using a standard single superframe.