Offset Tracking of sensor clock using Kalman filter for wireless network synchronization

Wireless Sensors Networks (WSN) are more and more used in structural health monitoring applications since they represent a less expensive and non-invasive way to monitor infrastructures. Most of these applications work by merging or comparing data from several sensors located across the structure. These data often comprise measurements of physicals phenomenons evolving with time, such as acceleration and temperature. To merge or compare time-dependent data from different sensors they need to be synchronized so all the samples are time-stamped with the same time reference. An initial synchronization of the sensors is needed because sensors are independent and therefore can not be all started at the same time. Subsequent re-synchronizations are also needed since the sensors keep track of time using their imperfect local clock. A quartz clock will drift in time due to the sensitivity of the quartz oscillator to its environmental conditions ; thus, synchronization accuracy depends on the quality of the oscillator, environmental conditions, re-synchronization frequency and time reference quality. The required accuracy of the synchronization depends on applications, for instance, one needs millisecond accuracy to analyze vibration data, microsecond for acoustic data and nanosecond to time-stamp electromagnetic propagation. Sensors can be synchronized by exchanging timing information as in the RBS [1], TPSN [2] and FTSP [3] protocols or through an external timing reference such as the PPS signal transmitted by the GPS as in [4]. While the first option might be less power hungry, our work in this paper focus on the second option as it allows for a better synchronization accuracy. This paper presents a smart-sensor able of time-stamping samples as well as measuring its clock offset and frequency from a noisy PPS signal. We implemented it on an FPGA to get high speed counters without software overhead. We then used a Kalman filter to track the offset with more accuracy and to adjust the sample time-stamps of the sensors. This work is aiming at having the highest time accuracy as possible while minimizing its consequence on sensor power-consumption.