This paper proposes a chip correlation indicator （CCI）-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss. On the basis of analyzing all related factors, it can be c...This paper proposes a chip correlation indicator （CCI）-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss. On the basis of analyzing all related factors, it can be concluded that signal-to-noise rate （SNR） is the main factor causing the non-perceived packet loss. In this paper, the relationship model between CCI and non-perceived packet loss rate （NPLR） is established from related models such as SNR versus packet success rate （PSR）, CCI versus SNR and CCI-NPLR. Due to the large fluctuating range of the raw CCI, Kalman filter is introduced to do de-noising of the raw CCI. The cubic model and the least squares method are employed to fit the relationship between CCI and SNR. In the experiments, many groups of comparison have been conducted and the results show that the proposed mechanism can achieve more accurate measurement of the non-perceived packet loss than existing approaches. Moreover, it has the advantage of decreasing extra energy consumption caused by sending large number of probe packets.展开更多
基金supported by the National Natural Science Foundation of China (61262020)Aeronautical Science Foundation of China (2010ZC56008)Nanchang Hangkong University Postgraduate Innovation Foundation (YC2011030)
文摘This paper proposes a chip correlation indicator （CCI）-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss. On the basis of analyzing all related factors, it can be concluded that signal-to-noise rate （SNR） is the main factor causing the non-perceived packet loss. In this paper, the relationship model between CCI and non-perceived packet loss rate （NPLR） is established from related models such as SNR versus packet success rate （PSR）, CCI versus SNR and CCI-NPLR. Due to the large fluctuating range of the raw CCI, Kalman filter is introduced to do de-noising of the raw CCI. The cubic model and the least squares method are employed to fit the relationship between CCI and SNR. In the experiments, many groups of comparison have been conducted and the results show that the proposed mechanism can achieve more accurate measurement of the non-perceived packet loss than existing approaches. Moreover, it has the advantage of decreasing extra energy consumption caused by sending large number of probe packets.
