Jang-Sub Kim

Clock Synchronization in Wireless Sensor Networks: An Overview

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs.

Energy-Efficient Estimation of Clock Offset for Inactive Nodes in Wireless Sensor Networks

For a meaningful processing of the information sensed by a wireless sensor network (WSN), the clocks of the individual nodes need to be matched through some well-defined procedures. Extending the idea of having silent nodes in a WSN overhear the two-way timing message communication between two active (master and slave) nodes, this paper derives the maximum-likelihood estimator (MLE) for the clock offsets of the listening nodes located within the communication range of the active nodes by assuming an exponential link delay modeling, hence synchronizing with the reference node at a very low cost. A vital advantage for adopting such an approach is that the performance of sender-receiver protocols can be compared with receiver-receiver protocols on equal footings, because their main critical aspect was associated with the high-communication overhead induced by the point-to-point nature of communication links relative to broadcast communications. The MLE is also shown to be the minimum variance unbiased estimator (MVUE) of the clock offset when the mean of exponential link delays is known. Since it is attractive to know in advance the extent to which an estimator can perform through its lower bound, the Chapman-Robbins bound and the Barankin bound for the clock offset estimator are also derived. It is shown that for an exponential link delay model, the mean square error of the clock offset estimator is inversely proportional to the square of the number of observations, and hence its performance is on a similar scale, albeit slightly lesser, as compared to the usual sender-receiver clock offset estimator. In addition, a novel method referred to as the Gaussian mixture Kalman particle filter (GMKPF) is proposed herein to estimate the clock offsets of the listening nodes in a WSN. GMKPF represents a better and flexible alternative to the MLE for the clock offset estimation problem due to its improved performance and applicability in arbitrary and generalized non-Gaussian random delay models.

A robust estimation scheme for clock phase offsets in wireless sensor networks in the presence of non-Gaussian random delays

To cope with the Gaussian or non-Gaussian nature of the random network delays, a novel method, referred to as the Gaussian mixture Kalman particle filter (GMKPF), is proposed herein to estimate the clock offset in wireless sensor networks. GMKPF represents a better and more flexible alternative to the symmetric Gaussian maximum likelihood (SGML), and symmetric exponential maximum likelihood (SEML) estimators for clock offset estimation in non-Gaussian or non-exponential random delay models. The computer simulations illustrate that GMKPF yields much more accurate results relative to SGML and SEML when the network delays are modeled in terms of a single non-Gaussian/non-exponential distribution or as a mixture of several distributions.

Improved Particle Filtering-Based Estimation of the Number of Competing Stations in IEEE 802.11 Networks

This letter proposes a new method to estimate the number of competing stations in IEEE 802.11 networks. Due to the nonlinear/non-Gaussian nature of measurement model, a nonlinear filtering algorithm, called the Gaussian mixture sigma point particle filter (GMSPPF), is proposed herein to estimate the number of competing stations. Since GMSPPF represents a better alternative to the conventional extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and unscented particle filter (UPF) for nonlinear/non-Gaussian (or Gaussian) tracking problems, we apply this filter for IEEE 802.11 WLANs. GMSPPF provides a more viable means for tracking in any conditions the number of competing stations in IEEE 802.11 WLANs relative to EKF, UKF, PF, and UPF. Further, GMSPPF presents both high accuracy as well as prompt reactivity to changes in the network occupancy status. For the more accurate method (GMSPPF), the combined access mode is shown to maximize the system throughput by switching between the basic access mode and the RTS/CTS access mode.

Handoff Triggering and Network Selection Algorithms for Load-Balancing Handoff in CDMA-WLAN Integrated Networks

This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection). The handoff trigger is decided based on the received signal strength (RSS). To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service), and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.

A novel vertical handoff strategy for integrated IEEE 802.11 WLAN/CDMA networks

The integration of WLANs and CDMA networks has recently evolved into hot issue. We propose a vertical handoff algorithm between WLAN and CDMA network. In this algorithm, a vertical handoff is classified into handoff decision making and MIP (mobile IP) registration process. Then, a handoff initiation is decided by the received signal strength (RSS). To reduce the unnecessary handoff probability, we consider a hand-off initiation model based on the criteria of RSS. As a mechanism of network selection, we also propose a context based network selection and the corresponding algorithms between WLAN to CDMA network, based on wireless channel assignment. We focus on a handoff initiation criteria which uses both the RSS and the network selection method which uses context information such as dropping probability, blocking probability, GOS (grade of service), velocity. As a decision making criterion, velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign available channels to the mobile stations. The proposed scheme is validated by computer simulation.

Robust Clock Synchronization in Wireless Sensor Networks Through Noise Density Estimation

Assuming that the network delays are normally distributed and the network nodes are subject to clock phase offset errors, the maximum likelihood estimator (MLE) and the Kalman filter (KF) have been recently proposed with the goal of maximizing the clock synchronization accuracy in wireless sensor networks (WSNs). However, because the network delays may assume any distribution and the performance of MLE and KF is quite sensitive to the distribution of network delays, designing clock synchronization algorithms that are robust to arbitrary network delay distributions appears as an important problem. Adopting a Bayesian framework, this paper proposes a novel clock synchronization algorithm, called the Iterative Gaussian mixture Kalman particle filter (IGMKPF), which combines the Gaussian mixture Kalman particle filter (GMKPF) with an iterative noise density estimation procedure to achieve robust performance in the presence of unknown network delay distributions. The Kullback-Leibler divergence is used as a measure to assess the departure of estimated observation noise density from its true expression. The posterior Cramér-Rao bound (PCRB) and the mean-square error (MSE) of IGMKPF are evaluated via computer simulations. It is shown that IGMKPF exhibits improved performance and robustness relative to MLE. The prior information plays an important role in IGMKPF. A MLE-based method for obtaining reliable prior information for clock phase offsets is presented and shown to ensure the convergence of IGMKPF.

A State-Space Approach to Multiuser Parameters Estimation Using Central Difference for CDMA Systems

In this paper, it is shown that a state-space model applies to the code-division multiple-access (CDMA) channel, and Central Difference Filter (CDF) produces channel estimates with the minimum mean-square error (MMSE). This result may be used as compare to Extended Kalman Filter (EKF) which used as channel estimator in CDMA system. The main purpose of this paper is to compare robustness of channel estimator for realistic rapidly time-varying Rayleigh fading channels. To overcome the highly nonlinear nature of time delay estimation and also improve the accuracy, consistency and efficiency of channel estimation, an iterative nonlinear filtering algorithm, called the CDF has been applied in the field of CDMA System. The proposed channel estimator has a more near-far resistant property than the conventional Extended Kalman Filter (EKF). Thus, it is believed that the proposed estimator can replace well-known filters, such as the EKF. The Cramer-Rao lower bound (CRLB) is derived for the estimator, and simulation result show that it is nearly near-far resistant and clearly outperforms the EKF.

Adaptive multiuser receiver with joint channel and time delay estimation of CDMA signals based on the square-root unscented filter

The paper discusses an adaptive multiuser receiver for CDMA systems in which the scaled unscented filter (SUF) and the square root unscented filter (SURF) are used for joint estimation and tracking of the code delays and multipath coefficients of the received CDMA signals. The proposed channel estimators are more near-far resistant than the conventional extended Kalman filter (EKF) and present lower complexity than the conventional particle filter (PF) based methods. To present meaningful performance measures, the modified Cramer-Rao lower bound (CRLB) and computational complexity metrics are derived for the proposed and existing channel estimators. Computer simulation results demonstrate the superior performance of the proposed channel estimators. The proposed estimators are also shown to exhibit lower complexity relative to the PF.

A robust clock synchronization algorithm for wireless sensor networks

Recently, the maximum likelihood estimator (MLE) and Cramer-Rao Lower Bound (CRLB) were proposed with the goal of maximizing and assessing the synchronization accuracy in wireless sensor networks (WSNs). Because the network delays may assume any distribution and the performance of MLE is quite sensitive to the distribution of network delays, designing clock synchronization algorithms that are robust to unknown network delay distributions appears as an important problem. By adopting a Bayesian framework, this paper proposes a novel clock synchronization algorithm, called Iterative Gaussian Mixture Kalman Particle Filter (IGMKPF), which is shown to achieve good and robust performance in the presence of unknown network delay distributions. The Posterior Cramer-Rao Bound (PCRB) and the Mean-Square Error (MSE) of IGMKPF are evaluated and shown to exhibit improved performance and robustness relative to MLE.