Clock synchronization between any two nodes is generally accomplished through message exchanges. Due to the presence of non-deterministic and possibly unbounded message delays, messages can be delayed arbitrarily, which makes the clock synchronization very difficult. The most commonly proposed nondeterministic network delay distributions are the Gaussian, exponential, Gamma, and Wei-bull pdfs, see e.g. In general, it is difficult, if not impossible, to assess which distribution model may be fit to capture the network delay distributions in a given WSN. This is due to the fact that various factors may impact the distribution of network delays differently. The Gaussian pdf and the exponential pdf have also been proposed to model the network delays in WSNs. Here, the ML estimators for clock offset estimation in the presence of Gaussian and exponential network delay distributions will be referred to as the Gaussian ML (GML) and exponential ML (EML), respectively. The simulation results in Figure 6.4 showed that GML and EML are quite sensitive to the network delay distributions. Therefore, one important problem to cope with is the design of clock offset estimation schemes that are robust with respect to the distribution of the unknown network delays.

This chapter deals with the development of clock offset estimators for WSNs that are robust with respect to the possible asymmetries and the unknown or possibly time-varying distributions of the network delays in the uplink and downlink of message exchanges. The two-way message exchange mechanism used in the NTP and TPSN is adopted here as the clock synchronization approach between two nodes of the WSN.