In a wireless network, synchronization accuracy can be significantly affected by impairments affecting the physical layer. It is well known that path delay asymmetry adversely affects timing protocols based on two-way message exchanges but, unfortunately, bounds to link asymmetry are hard to ensure when timing messages are exchanged through wireless links. It would then be desirable to be able to flag the corresponding timestamps as unreliable. The paper discusses the application of a timestamp validation approach in the synchronization of the nodes in a wireless network employing physical-level timestamping in a IEEE 802.15.4 Chirp Spread Spectrum. Because a Kalman filter is employed as a servo for the local clock, differences between the timestamp received through the network and the predicted local time can be compared to statistical thresholds referred to the Kalman filter innovation process. Thus, a detector decision rule can be successfully based on well-defined statistical criteria, as shown by experimental data presented in the paper.
Robust synchronization for IEEE 802.15.4 CSS Wireless Sensor Networks
GIORGI, GIADA;NARDUZZI, CLAUDIO;
2013
Abstract
In a wireless network, synchronization accuracy can be significantly affected by impairments affecting the physical layer. It is well known that path delay asymmetry adversely affects timing protocols based on two-way message exchanges but, unfortunately, bounds to link asymmetry are hard to ensure when timing messages are exchanged through wireless links. It would then be desirable to be able to flag the corresponding timestamps as unreliable. The paper discusses the application of a timestamp validation approach in the synchronization of the nodes in a wireless network employing physical-level timestamping in a IEEE 802.15.4 Chirp Spread Spectrum. Because a Kalman filter is employed as a servo for the local clock, differences between the timestamp received through the network and the predicted local time can be compared to statistical thresholds referred to the Kalman filter innovation process. Thus, a detector decision rule can be successfully based on well-defined statistical criteria, as shown by experimental data presented in the paper.Pubblicazioni consigliate
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