Optimizing data acquisition by sensor-channel co-allocation in wireless sensor networks

Wireless sensor networks (WSNs) should handle multiple sensing tasks for various applications. How to improve the quality of the data acquired in such resource constrained environment is a challenging issue. In this paper, we propose a sensor-channel co-allocation model for scheduling the sensing tasks. The proposed model considers the capability, coupling and load balancing constraints for sensing data acquisition, and can guarantee transmission of sensed data in real-time while avoiding data incompleteness in an efficient way. A spatiotemporal metric called sensing-span is proposed to evaluate the tasks' execution cost of achieving desired data quality. We extend computation task scheduling algorithms to support sensor-channel co-allocation problem and a heuristic called Minimum Service Capability Fragment (MSCF) is introduced for task scheduling to minimize the waste of reserved channel capacity. Simulation results show that MSCF can improve the performance of data acquisition in WSNs as compared with other heuristics, when scheduling a large number of concurrent data acquisition tasks. ©2010 IEEE.published_or_final_versionThe 17th IEEE International Conference on High Performance Computing (HiPC 2010), Dona Paula, Argentina, 19-22 December 2010. In Proceedings of the 17th IEEE HiPC, 2010, p. 1-1