Clock Estimation for Long-Term Synchronization in Wireless Sensor Networks with Exponential Delays

Although the existing time synchronization protocols in wireless sensor networks (WSNs) are efficient for short periods, many applications require long-term synchronization among the nodes, for example, coordinated sleep and wakeup modes, and synchronized sampling. In such applications, experiments have shown that even clock skew correction cannot maintain long-term clock synchronization and a quadratic model of clock variations can better capture the dynamics of the actual clock model involved, hence increasing the resynchronization period and conserving significant energy. This paper derives the maximum likelihood (ML) estimator for all the clock parameters in a two-way timing exchange model with exponential delays. The same estimation procedure can be applied to one-way timing exchange models with little modification

Joint synchronization of clock phase offset, skew and drift in reference broadcast synchronization (RBS) protocol

Time-synchronization in wireless ad-hoc sensor networks is a crucial piece of infrastructure. Thus, it is a fundamental design problem to have a good clock syn- chronization amongst the nodes of wireless ad-hoc sensor networks. Motivated by this fact, in this thesis, the joint maximum likelihood (JML) estimator for relative clock phase offset and skew under the exponential noise model for the reference broadcast synchronization protocol is formulated and found via a direct algorithm. The Gibbs Sampler is also proposed for joint estimation of relative clock phase offset and skew, and shown to provide superior performance compared to the JML-estimator. Lower and upper bounds for the mean-square errors (MSE) of the JML-estimator and the Gibbs Sampler are introduced in terms of the MSE of the uniform minimum variance unbiased estimator and the conventional best linear unbiased estimator, respectively. The suitability of the Gibbs Sampler for estimating additional unknown parameters is shown by applying it to the problem in which synchronization of clock drift is also needed

Cooperative Synchronization in Wireless Networks

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE Transaction on Signal Processin