In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm.This work was supported in part by the National Natural Science Foundation of China under Grant 61503308 and Grant 61472331, in part by the Natural Science Foundation Project of Chongqing CSTC 2015jcyjA40043, and in part by Fundamental Research Funds for the Central Universities under Grant SWU114036. This publication was made possible by NPRP grant #4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation)
