Time Synchronization in WSNs: A Maximum-Value-Based Consensus Approach

He, Jianping; Cheng, Peng; Shi, Ling; Chen, Jiming; Sun, Youxian

doi:10.1109/tac.2013.2286893

Cited by 266 publications

(83 citation statements)

References 35 publications

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“…In [19], WSN time synchronization follows two strategies: (i) maximum time synchronization (MTS) to simultaneously synchronize the skew and offset of each node when the communication delay is negligible and (ii) a weighted maximum time synchronization (WMTS) when the communication delay between the nodes is random. In contrast to our work, in which we synchronize a virtual clock, these authors attempt to synchronize the clock skew, in order to obtain acceptable synchronization accuracies.…”

Section: Synchronization Protocols For Wsnmentioning

confidence: 99%

“…The main idea of MTS and WMTS is to drive all clocks to the maximum value among the network. In [19], random communication delays with normal distribution are considered, while we validated our solutions against Gaussian and exponentially distributed delays. This solution can be classified as distributed and asynchronous algorithm, whereas ours can be classified as centralized and asynchronous.…”

Section: Synchronization Protocols For Wsnmentioning

confidence: 99%

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Synchronization of application-driven WSN

Marques

Ricardo

2017

J Wireless Com Network

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The growth of wireless sensor networks (WSN) has resulted in part from requirements for connecting sensors and advances in radio technologies. WSN nodes may be required to save energy and therefore wake up and sleep in a synchronized way. In this paper, we propose an application-driven WSN node synchronization mechanism which, by making use of cross-layer information such as application ID and duty cycle, and by using the exponentially weighted moving average (EWMA) technique, enables nodes to wake up and sleep without losing synchronization. The results obtained confirm that this mechanism maintains the nodes in a mesh network synchronized according to the applications they run, while maintaining a high packet reception ratio.

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Section: Synchronization Protocols For Wsnmentioning

confidence: 99%

Section: Synchronization Protocols For Wsnmentioning

confidence: 99%

Synchronization of application-driven WSN

Marques

Ricardo

2017

J Wireless Com Network

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“…By replacing the series of constraints with one constraint,the problem (10) is simplified with consensus-based technique. Apart from the well-known average consensus theory, [17] proposed the maximum(minimum) consensus theory with lower complexity and faster convergence in network. Combining the average-based consensus scheme proposed in [6], the convergence is expedited by replacing the constraints mentioned before with the Euclidian distance between local estimates and weighted sum of estimates from neighbours in graph G. The jth user solves the local regularization as follows:…”

Section: A Consensus Based Channel Estimation Using Csmentioning

confidence: 99%

L0.5-regularization based distributed channel estimation for industrial wireless sensor network

Yang

Chen

Guan

2015

2015 IEEE/CIC International Conference on Communications in China (ICCC)

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In wireless communication, channel state information (CSI) is essential for receivers to correctly demodulate the signal from transmitters. The performance of wireless sensor network is significantly affected by accuracy of channel estimation, which is obtained by exciting the channel with a probing sequence and decoding the impulse response of receivers. Considering the sparsity of response, compressive sensing theory can be applied to decrease the sampling rates at the receivers, who recover the commonly detected CSI by solving the Lp-regularization. Hence, a distributed multipath channel estimation (DMCE) approach is proposed here, utilizing iterative half thresholding algorithm to solve the L0.5-regularization. Distributed cooperation reduces iteration times by allocating the computation burden to multiple sensors, whose spatial diversity reduces error of estimation from the noisy measurements. Simulation results shows the efficiency, effectiveness and promotion in sparsity than L1-regularization and non-cooperative scheme of our approach.

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“…When all sensors agree on the value of a variable of interest, they are said to have reached consensus. The average consensus has been proven to be an effective tool for performing network-wide distributed computation task ranging from synchronisation and robot rendezvous [51,52] to machine learning and target tracking [53][54][55].…”

Section: Introduction To Average Consensusmentioning

confidence: 99%

Distributed multi‐object localisation by consensus on compressive sampling received signal strength fingerprints

et al. 2015

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Recent growing interest for location-based services has created a demand on object localisation approaches with low cost and high accuracy. In this study, the problem of distributed multi-object localisation using fingerprints of received signal strength (RSS) is addressed by combining average consensus and compressed sensing. First, Bayesian compressed sensing is employed at each agent to recover the sparse index vector from RSS measurements, which are corrupted by noises. It relaxes the requirement on accurate prior position knowledge of beacon nodes and is applicable in non-line-of-sight conditions. Then, average consensus is adopted to compel all agents to reach an agreement on the index vector, and in turn, on the location of objects. By using only one-hop neighbours' information, the proposed distributed localisation method is applicable to large-scale networks. Moreover, the final location of each object is obtainable from each individual agent, which makes the proposed method flexible to the network administration. Experimental results are included to demonstrate the effectiveness of the proposed method.

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Time Synchronization in WSNs: A Maximum-Value-Based Consensus Approach

Cited by 266 publications

References 35 publications

Synchronization of application-driven WSN

Synchronization of application-driven WSN

L0.5-regularization based distributed channel estimation for industrial wireless sensor network

Distributed multi‐object localisation by consensus on compressive sampling received signal strength fingerprints

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