Power-Efficient Clock Synchronization using Two-Way Timing Message Exchanges in Wireless Sensor Networks

A combined hardware-software approach for acoustic sensor network synchronization

Abstract: In this paper we present an approach for synchronizing a wireless acoustic sensor network using a two-stage procedure. First the clock frequency and phase differences between pairs of nodes are estimated employing a two-way message exchange protocol. The estimates are further improved in a Kalman filter with a dedicated observation error model. In the second stage network-wide synchronization is achieved by means of a gossiping algorithm which estimates the average clock frequency and phase of the sensor nodes. These averages are viewed as frequency and phase of a virtual master clock, to which the clocks of the sensor nodes have to be adjusted. The amount of adjustment is computed in a specific control loop. While these steps are done in software, the actual sampling rate correction is carried out in hardware by using an adjustable frequency synthesizer. Experimental results obtained from hardware devices and software simulations of large scale networks are presented. HighlightsAcoustic sensor network synchronization for speaker position estimation.State estimation using Kalman filter and GMM error model.Gossiping algorithm for synchronization of large sensor networks.

An energy-efficient clock synchronization protocol for Wireless Sensor Networks

Abstract: The behavior of Wireless Sensor Networks (WSN) is nowadays widely analyzed. One of the most important issues is related to their energy consumption, as this has a major impact on the network lifetime. Another important application requirement is to ensure data sensing synchronization, which leads to additional energy consumption as a high number of messages is sent and received at each node. Our proposal consists in implementing a combined synchronization protocol based on the IEEE 1588 standard that was designed for wired networks and the PBS (Pairwise Broadcast Synchronization) protocol that was designed for sensor networks, as none of them is able to provide the needed synchronization accuracy for our application on its own. The main goals of our new synchronization protocol are: to ensure the accuracy of local clocks up to a tenth of a microsecond and to provide an important energy saving. Our results obtained using NS-2 (Network Simulator) show that the performance of our solution (IEEE 1588-PBS) matches our application requirements with regard to the synchronization, with a significant improvement in energy saving.

Étude et développement d'une plateforme de communication pour les réseaux de capteurs acoustiques sans fil : application au contrôle-santé des rails par corrélation du bruit ambiant

Abstract: Le Controle-Sante Integre (CSI) reduit les besoins d’inspections humaines grâce a une surveillance automatisee, reduit les couts de maintenance grâce a la detection precoce des anomalies avant qu’elles ne degenerent et ameliore la securite ainsi que la fiabilite des services. L’objectif de cette these est de concevoir une plateforme de communication sans fil pour le CSI des structures ferroviaires. Le principe de controle repose sur la reconstruction des reponses impulsionnelles (fonctions de Green) par correlation de bruit aleatoire se propageant dans le milieu. Durant ces travaux, nous avons eprouve experimentalement la relation entre les reponses actives experimentales et une version post-traitee des fonctions de correlation de bruit dans un contexte ferroviaire. Ainsi, nous avons demontre l’applicabilite des fonctions de correlation pour la detection d’un defaut local sur un rail. Ensuite, nous avons realise une etude experimentale comparative sur la caracterisation d’une transmission ZigBee en termes d’attenuation et de portee dans plusieurs environnements. Dans l’environnement ferroviaire sous test, nous avons demontre l’adequation avec la portee d’une transmission ZigBee mono-saut (dans un rayon de 76m). Une solution de synchronisation des capteurs lors du prelevement du signal basee sur la norme IEEE 802.15.4 a ete proposee et validee par une campagne de mesures. Il a ete demontre que cette approche offre une precision de l’ordre de quelques centaines de nanosecondes. Un prototype-plateforme de communication sans fil base sur la technologie ZigBee/IEEE 802.15.4 a ete mis en place et deploye sur un echantillon de rail. Cette solution a permis de valider les performances de cette plateforme, une fois les donnees recoltees par les transducteurs, ces informations sont transmises par un lien ZigBee vers une station de base ou des algorithmes de detection leurs sont appliques.

Multi-hop time synchronization for Underwater Acoustic Networks

Abstract: Time synchronization plays an important role in UANs, which need fine-grained coordination among nodes. Precise time synchronization is also needed for a variety of other coordinate tasks in UANs such as data fusion, TDMA scheduling, localization, power-saving and so on. Recently, time synchronization protocol for point-to-point with high propagation delay has been proposed, while time synchronization for multi-hop UANs is still an open issue. In this paper, we propose a multi-hop time synchronization scheme for Underwater Acoustic Networks (MSUAN). MSUAN includes three stages. In the first stage, we assign each node a level by establishing a path to the reference node. During the second phase, nodes realize synchronization by receiving synchronization packets both from it's parent node and neighboring nodes. The third stage re-synchronizes nodes because the clock may drift away without synchronization for long time. Mathematical analysis of MSUAN's synchronization error shows that the more time-stamps we get, the more accurate skew we will get. At the end, we use OPNET to simulate MSUAN, and the simulation result under pyramid topology shows that even the hop accumulates to 8, for MSUAN, the average offset error is maintained at about 58us and the average skew error is below 2ppm.

Least Squares-Based Estimation of Relative Clock Offset and Frequency in Sensor Networks with High Latency

Abstract: Accurate clock synchronization is essential in the operation of Sensor Networks. Algorithms for clock synchronization generally rely on estimators of the relative offset and frequency of two different clocks. Sensor Networks have limited power resources so it is important that these estimators not be too computationally intensive. For this reason a number of least squares-based estimators have been proposed, however they are not appropriate for sensor networks with significant propagation latency. For these applications, the Time Synchronization for High Latency (TSHL) protocol was developed. We seek to improve the accuracy of TSHL and thus propose, study, and demonstrate the effectiveness of several alternative least squares-based estimators.

Wireless sensor networks: a survey

Abstract: This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

Timing-sync protocol for sensor networks

Abstract: Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are time-stamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks.In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20ms. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.

The flooding time synchronization protocol

Abstract: Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.

Probability and Random Processes for Electrical Engineering

Abstract: 1. Probability Models in Electrical and Computer Engineering. Mathematical models as tools in analysis and design. Deterministic models. Probability models. Statistical regularity. Properties of relative frequency. The axiomatic approach to a theory of probability. Building a probability model. A detailed example: a packet voice transmission system. Other examples. Communication over unreliable channels. Processing of random signals. Resource sharing systems. Reliability of systems. Overview of book. Summary. Problems. 2. Basic Concepts of Probability Theory. Specifying random experiments. The sample space. Events. Set operations. The axioms of probability. Discrete sample spaces. Continuous sample spaces. Computing probabilities using counting methods. Sampling with replacement and with ordering. Sampling without replacement and with ordering. Permutations of n distinct objects. Sampling without replacement and without ordering. Sampling with replacement and without ordering. Conditional probability. Bayes' Rule. Independence of events. Sequential experiments. Sequences of independent experiments. The binomial probability law. The multinomial probability law. The geometric probability law. Sequences of dependent experiments. A computer method for synthesizing randomness: random number generators. Summary. Problems. 3. Random Variables. The notion of a random variable. The cumulative distribution function. The three types of random variables. The probability density function. Conditional cdf's and pdf's. Some important random variables. Discrete random variables. Continuous random variables. Functions of a random variable. The expected value of random variables. The expected value of X. The expected value of Y = g(X). Variance of X. The Markov and Chebyshev inequalities. Testing the fit of a distribution to data. Transform methods. The characteristic function. The probability generating function. The laplace transform of the pdf. Basic reliability calculations. The failure rate function. Reliability of systems. Computer methods for generating random variables. The transformation method. The rejection method. Generation of functions of a random variable. Generating mixtures of random variables. Entropy. The entropy of a random variable. Entropy as a measure of information. The method of a maximum entropy. Summary. Problems. 4. Multiple Random Variables. Vector random variables. Events and probabilities. Independence. Pairs of random variables. Pairs of discrete random variables. The joint cdf of X and Y. The joint pdf of two jointly continuous random variables. Random variables that differ in type. Independence of two random variables. Conditional probability and conditional expectation. Conditional probability. Conditional expectation. Multiple random variables. Joint distributions. Independence. Functions of several random variables. One function of several random variables. Transformation of random vectors. pdf of linear transformations. pdf of general transformations. Expected value of functions of random variables. The correlation and covariance of two random variables. Joint characteristic function. Jointly Gaussian random variables. n jointly Gaussian random variables. Linear transformation of Gaussian random variables. Joint characteristic function of Gaussian random variables. Mean square estimation. Linear prediction. Generating correlated vector random variables. Generating vectors of random variables with specified covariances. Generating vectors of jointly Gaussian random variables. Summary. Problems. 5. Sums of Random Variables and Long-Term Averages. Sums of random variables. Mean and variance of sums of random variables. pdf of sums of independent random variables. Sum of a random number of random variables. The sample mean and the laws of large numbers. The central limit theorem. Gaussian approximation for binomial probabilities. Proof of the central limit theorem. Confidence intervals. Case 1: Xj's Gaussian unknown mean and known variance. Case 2: Xj's Gaussian mean and variance unknown. Case 3: Xj's Non-Gaussian mean and variance unknown. Convergence of sequences of random variables. Long-term arrival rates and associated averages. Long-term time averages. A computer method for evaluating the distribution of a random variable using the discrete Fourier transform. Discrete random variables. Continuous random variables. Summary. Problems. Appendix: subroutine FFT(A,M,N). 6. Random Processes. Definition of a random process. Specifying of a random process. Joint distributions of time samples. The mean, autocorrelation, and autocovariance functions. Gaussian random processes. Multiple random processes. Examples of discrete-time random processes. iid random processes. Sum processes the binomial counting and random walk processes. Examples of continuous-time random processes. Poisson process. Random telegraph signal and other processes derived from the Poisson Process. Wiener process and Brownian motion. Stationary random processes. Wide-sense stationary random processes. Wide-sense stationary Gaussian random processes. Cylostationary random processes. Continuity, derivative, and integrals of random processes. Mean square continuity. Mean square derivatives. Mean square integrals. Response of a linear system to random input. Time averages of random processes and ergodic theorems. Fourier series and Karhunen-Loeve expansion. Karhunen-Loeve expansion. Summary. Problems. 7. Analysis and Processing of Random Signals. Power spectral density. Continuous-time random processes. Discrete-time random processes. Power spectral density as a time average. Response of linear systems to random signals. Continuous-time systems. Discrete-time systems. Amplitude modulation by random signals. Optimum linear systems. The orthogonality condition. Prediction. Estimation using the entire realization of the observed process. Estimation using causal filters. The Kalman filter. Estimating the power spectral density. Variance of periodogram estimate. Smoothing of periodogram estimate. Summary. Problems. 8. Markov Chains. Markov processes. Discrete-time Markov chains. The n-step transition probabilities. The state probabilities. Steady state probabilities. Continuous-time Markov chains. State occupancy times. Transition rates and time-dependent state probabilities. Steady state probabilities and global balance equations. Classes of states, recurrence properties, and limiting probabilities. Classes of states. Recurrence properties. Limiting probabilities. Limiting probabilities for continuous-time Markov chains. Time-reversed Markov chains. Time-reversible Markov chains. Time-reversible continuous-time Markov chains. Summary. Problems. 9. Introduction to Queueing Theory. The elements of a queueing system. Little's formula. The M/M/I queue. Distribution of number in the system. Delay distribution in M/M/I system and arriving customer's distribution. The M/M/I system with finite capacity. Multi-server systems: M/M/c, M/M/c/c, and M/M/infinity. Distribution of number in the M/M/c system. Waiting time distribution for M/M/c. The M/M/c/c queueing system. The M/M/infinity queueing system. Finite-source queueing systems. Arriving customer's distribution. M/G/I queueing systems. The residual service time. Mean delay in M/G/I systems. Mean delay in M/G/I systems with priority service discipline. M/G/I analysis using embedded Markov chains. The embedded Markov chains. The number of customers in an M/G/I system. Delay and waiting time distribution in an M/G/I system. Burke's theorem: Departures from M/M/c systems Proof of Burke's theorem using time reversibility. Networks of queues: Jackson's theorem. Open networks of queues. Proof of Jackson's theorem. Closed networks of queues. Mean value analysis. Proof of the arrival theorem. Summary. Problems. Appendix A. Mathematical Tables. Appendix B. Tables of Fourier Transformation. Appendix C. Computer Programs for Generating Random Variables. Answers to Selected Problems. Index.