基于变长帧的无线传感器网络时间同步算法
计算机工程（2014）
Abstract
为降低无线传感器网络同步过程中的能量消耗,加快实现全网络时间同步的收敛速度,提出基于变长帧的时间同步算法。利用精短同步帧结构对时间信息进行压缩,采用短帧、完整帧交替转发保证同步精度,使被同步节点自主判断接收信息的完整性,并通过请求重传机制应对同步过程中的完整帧丢失问题。分析结果表明,实现全网同步的收敛时间与同步开销成正比,变长帧时间同步算法通过去除同步时间数据中的冗余信息,可有效缩短该收敛时间。传感器节点同步实验结果表明,该算法适用于多种时间同步模型,在保证传感网同步精准度的前提下,可减少同步数据通信量,提升能量效率。
MoreTranslated text
Key words
Wireless Sensor Network(WSN),time synchronization,length of synchronization frame,data traffic,energy consumption,convergence time
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined