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

^*Corresponding author for this work

School of Instrumentation and Optoelectronic Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
Pages	516-522
Number of pages	7
DOIs	https://doi.org/10.1109/APSCC.2010.40
State	Published - 2010
Event	2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010 - Hangzhou, China Duration: 6 Dec 2010 → 10 Dec 2010

Publication series

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

Conference

Conference	2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010
Country/Territory	China
City	Hangzhou
Period	6/12/10 → 10/12/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.1109/APSCC.2010.40

Cite this

@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. in Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010., 5708613, Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010, pp. 516-522, 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010, Hangzhou, China, 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 et al.
Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010. 2010. p. 516-522 5708613 (Proceedings - 2010 IEEE Asia-Pacific Services Computing Conference, APSCC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this