A neural approach to synchronization in wireless networks with heterogeneous sources of noise
This addresses synchronization challenges in wireless environments such as sensor networks, though it appears incremental as it builds on existing protocols.
The paper tackles clock synchronization in wireless networks with heterogeneous noise sources like temperature variations and delay asymmetry, proposing a neural approach that achieves robustness without requiring prior assumptions or parameter tuning.
The paper addresses state estimation for clock synchronization in the presence of factors affecting the quality of synchronization. Examples are temperature variations and delay asymmetry. These working conditions make synchronization a challenging problem in many wireless environments, such as Wireless Sensor Networks or WiFi. Dynamic state estimation is investigated as it is essential to overcome non-stationary noises. The two-way timing message exchange synchronization protocol has been taken as a reference. No a-priori assumptions are made on the stochastic environments and no temperature measurement is executed. The algorithms are unequivocally specified offline, without the need of tuning some parameters in dependence of the working conditions. The presented approach reveals to be robust to a large set of temperature variations, different delay distributions and levels of asymmetry in the transmission path.