Self-adaption link-quality detection algorithm for data collecting in OSN

In order to implement the data collecting in opportunistic sensor network (OSN), a self-adaption link-quality detection algorithm (SLDA) was proposed to enhance the prediction accuracy of the link and the data transmission rate. The new scheme adopted self-adaptive link-quality detection strategy to measure the real-time link quality weight factor, then it was combined with energy consumption model of mobile nodes to quantitatively predict optimal transmission link for message forwarding by means of the unscented Kalman filter (UKF). Simulation results show that SLDA increases the average delivery ratio and reduces the average delay in OSN, and performs well in the situation of sparse deployment of mobile nodes.