Sensing workload scheduling in hierarchical sensor networks for data fusion applications
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
| Hi-index | 0.00 |
Wireless Sensor Networks (WSNs) are severely constrained in computation and communication capabilities due to the cost and size of available sensors. On the other hand, autonomic computing (AC) offers a promising solution to manage large-scale computing systems without human intervention. Realizing the similarity between WSNs and AC applications, this paper proposes an autonomic sensor network framework to enable self-managing wireless sensor network systems for collaborative information processing. In particular, a preliminary power-aware self-configuring and self-optimizing sensor selection scheme is developed to improve the performance and extend the lifetime of sensor networks. The simulation results confirm that the proposed power-aware scheme prolongs the network lifetime and balances the energy in sensor nodes.