Network time distribution relies on timestamping, which is employed by local nodes to assign a time value to incoming timing packets. Clearly, any error affecting the timestamp has a direct impact on the final synchronization accuracy of the system. In wireless sensor networks (WSNs), impairments affecting the wireless physical layer are the primary cause of such timestamping errors, and it would be advantageous to flag suspect timestamp values before feeding them into a synchronization algorithm. This paper discusses a new strategy for validating timestamps in a WSN based on IEEE 802.15.4 chirp spread spectrum. The considered system employs physical-level timestamping, which generates one timestamp for each symbol belonging to a packet. Such set of timestamps is processed using a Kalman filter (KF) that compares them with predicted values, employing statistical thresholds referred to the KF innovation process. Experimental results obtained over a long observation period shows that even in a noisy environment with several interfering communication sources, the algorithm is able to detect untrusted timestamps.

Timestamp Validation Strategy for Wireless Sensor Networks Based on IEEE 802.15.4 CSS

GIORGI, GIADA;NARDUZZI, CLAUDIO;
2014

Abstract

Network time distribution relies on timestamping, which is employed by local nodes to assign a time value to incoming timing packets. Clearly, any error affecting the timestamp has a direct impact on the final synchronization accuracy of the system. In wireless sensor networks (WSNs), impairments affecting the wireless physical layer are the primary cause of such timestamping errors, and it would be advantageous to flag suspect timestamp values before feeding them into a synchronization algorithm. This paper discusses a new strategy for validating timestamps in a WSN based on IEEE 802.15.4 chirp spread spectrum. The considered system employs physical-level timestamping, which generates one timestamp for each symbol belonging to a packet. Such set of timestamps is processed using a Kalman filter (KF) that compares them with predicted values, employing statistical thresholds referred to the KF innovation process. Experimental results obtained over a long observation period shows that even in a noisy environment with several interfering communication sources, the algorithm is able to detect untrusted timestamps.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2891903
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