Title :
A Self-Adaption Link-Quality Detection Algorithm for Data Collecting in OSN
Author :
Xiao, Wang ; Yinfeng, Wu ; Ning, Yu ; Jiangwen, Wan
Author_Institution :
Sch. of Instrum. Sci. & Optoelectron. Eng., Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
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 real time 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 :
Kalman filters; energy consumption; mobile radio; queueing theory; radio links; telecommunication network management; wireless sensor networks; data collecting; energy consumption model; link quality weight factor; message forwarding; message queue management; mobile node deployment; opportunistic sensor network; self-adaption link-quality detection algorithm; unscented Kalman filter; Covariance matrix; Delay; Energy consumption; Mobile communication; Mobile computing; Monitoring; Predictive models; Data Collecting; Message Queues Management; Opportunistic Sensor Network; Self-adaption Link-quality Detection; the Unscented Kalman Filter;
Conference_Titel :
Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-9396-8
DOI :
10.1109/APSCC.2010.40
