Adaptive Power-Saving Techniques for WSN based on Incremental Data Trend Analysis

• Main Authors: Guan Rong Lin, 林冠榮
• Other Authors: Arbee L. P. Chen
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/53358060482883067863

碩士 === 國立清華大學 === 資訊工程學系 === 95 === Wireless sensor networks have received considerable attentions in recent years and played an important role in monitoring applications. Sensor nodes usually have limited supply of energy. Therefore, one of major design considerations for sensor network applications is to conserve the energy for sensor nodes. In most sensor applications, communication is considered as the factor requiring the largest amount of energy. Therefore, existing approaches for conserving energy mainly focus on reducing the communication of sensor networks. However, as the applications of sensor networks continue to expand, we find that in some sensor applications sensing operations dominate the use of the energy, making existing approaches are not good for use. Therefore, in this study, we propose a novel energy-conserving approach for sensor networks. Our approach builds on the observation that the values of the collected sensor data exhibit periodical patterns over time. We exploit the periodical patterns to construct prediction models for sensor data and use the constructed models to approximately answer queries over sensor networks. In addition, we provide theoretical analyses for the use of the proposed approach, and show that a tight bound of the accuracy of reported value is guaranteed. Finally, we conduct a comprehensive experiment to validate the proposed approach. The experiment results show that our approach significantly reduces the sensing cost as well as the communication cost.