Research Article
The Performance Optimization of Energy-Efficient Scheduling Algorithm for Cluster-based Wireless Sensor Networks
@INPROCEEDINGS{10.4108/eai.19-8-2015.2261138, author={Hsing-Wen Wang and Pin-Jui Chen}, title={The Performance Optimization of Energy-Efficient Scheduling Algorithm for Cluster-based Wireless Sensor Networks}, proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, year={2015}, month={9}, keywords={scheduling policy; power saving; wireless sensor networks; clustering}, doi={10.4108/eai.19-8-2015.2261138} }
- Hsing-Wen Wang
Pin-Jui Chen
Year: 2015
The Performance Optimization of Energy-Efficient Scheduling Algorithm for Cluster-based Wireless Sensor Networks
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2261138
Abstract
The use of wireless sensor networks is very broad, and its application can be extended to a number of areas. In general, after the wireless sensor networks have been set up, there is no maintenance needed. The life cycle of the network will be limited only by the battery power supply of the sensors. Once the entire network energy consumption causes loss of balance in the system, the energy hole problem will occur, and even cause paralysis of the part of the system. In the cluster architecture, the burden of the cluster heads is very large, so it is bound to become the point with the largest energy consumption. Therefore, we will focus on creating an energy-saving method for the cluster heads. For the new schedule algorithm in our approach, we used the "polling” approach, so that the cluster heads can have effective and absolutely correct data reception and transmission. In addition, we also introduced the “sleeping” mechanism to ensure that the cluster head can save power through the most effective methods of data reception. Through two main mechanisms, not only will the overall operation function more efficiently, but also achieve the purpose of substantially prolonging the life expectancy of the whole network.