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A Self-Adaption Link-Quality Detection Algorithm for Data Collecting in OSN

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, SLDAuses 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.