A self-adaption link-quality detection algorithm for data collecting in OSN

Xiao Wang; Yinfeng Wu; Ning Yu; Jiangwen Wan

doi:10.1109/APSCC.2010.40

A self-adaption link-quality detection algorithm for data collecting in OSN

Xiao Wang^*
, Yinfeng Wu
, Ning Yu
, Jiangwen Wan

^*此作品的通讯作者

仪器科学与光电工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

3 引用（Scopus）

摘要

A Self-adaption Link-quality Detection Algorithm (SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self-adaptive Link-quality Detection strategy to measure the realtime link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDA uses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.

源语言	英语
主期刊名	Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
页	516-522
页数	7
DOI	https://doi.org/10.1109/APSCC.2010.40
出版状态	已出版 - 2010
活动	2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010 - Hangzhou, 中国期限: 6 12月 2010 → 10 12月 2010

出版系列

姓名	Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010

会议

会议	2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
国家/地区	中国
市	Hangzhou
时期	6/12/10 → 10/12/10

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/APSCC.2010.40

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{e029d47a5eb143e89340ba33c3c73c32,

title = "A self-adaption link-quality detection algorithm for data collecting in OSN",

abstract = "A Self-adaption Link-quality Detection Algorithm (SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self-adaptive Link-quality Detection strategy to measure the realtime link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDA uses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.",

keywords = "Data collecting, Detection, Message queues management, Opportunistic sensor network, Self-adaption link-quality, The unscented Kalman filter",

author = "Xiao Wang and Yinfeng Wu and Ning Yu and Jiangwen Wan",

year = "2010",

doi = "10.1109/APSCC.2010.40",

language = "英语",

isbn = "9780769543055",

series = "Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010",

pages = "516--522",

booktitle = "Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010",

note = "2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010 ; Conference date: 06-12-2010 Through 10-12-2010",

}

Wang, X, Wu, Y , Yu, N & Wan, J 2010, A self-adaption link-quality detection algorithm for data collecting in OSN. 在 Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010., 5708613, Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010, 页码 516-522, 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010, Hangzhou, 中国, 6/12/10. https://doi.org/10.1109/APSCC.2010.40

A self-adaption link-quality detection algorithm for data collecting in OSN. / Wang, Xiao; Wu, Yinfeng ; Yu, Ning 等.
Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010. 2010. 页码 516-522 5708613 (Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A self-adaption link-quality detection algorithm for data collecting in OSN

AU - Wang, Xiao

AU - Wu, Yinfeng

AU - Yu, Ning

AU - Wan, Jiangwen

PY - 2010

Y1 - 2010

N2 - A Self-adaption Link-quality Detection Algorithm (SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self-adaptive Link-quality Detection strategy to measure the realtime link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDA uses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.

AB - A Self-adaption Link-quality Detection Algorithm (SLDA) is proposed to implement the Data Collecting in Opportunistic Sensor Network. The new scheme adopts Self-adaptive Link-quality Detection strategy to measure the realtime link quality weight factor (LQWF), and combines energy consumption model of mobile nodes to predict optimal transmission path for message forwarding by means of the Unscented Kalman Filter (UKF). On the other hand, SLDA uses a new message queue management that analyzes the lifetime of every message, so all messages are graded by stepping factor which reflects importance degree of messages. Simulation results show that SLDA enhances the predicted accuracy of link decision. It also increases the average delivery ratio and reduces the average transmission delay. Comparing with other typical algorithms, SLDA performs best in OSN, especially in the situation of sparse deployment of mobile nodes.

KW - Data collecting

KW - Detection

KW - Message queues management

KW - Opportunistic sensor network

KW - Self-adaption link-quality

KW - The unscented Kalman filter

UR - https://www.scopus.com/pages/publications/79952425596

U2 - 10.1109/APSCC.2010.40

DO - 10.1109/APSCC.2010.40

M3 - 会议稿件

AN - SCOPUS:79952425596

SN - 9780769543055

T3 - Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010

SP - 516

EP - 522

BT - Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010

T2 - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010

Y2 - 6 December 2010 through 10 December 2010

ER -

A self-adaption link-quality detection algorithm for data collecting in OSN

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此