A Primer on Energy-Efficient Synchronization of WSN Nodes over Correlated Rayleigh Fading Channels

Briff, Pablo; Lutenberg, Ariel; Vega, Leonardo Rey; Vargas, F.; Patwary, Mohammad

doi:10.1109/wcl.2013.110713.130668

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…In general, the outage probability is a function S ij , g ij ,

γ_{0_{j}}

σ_{j}^{2}

at the receiver, a ij and the relative velocity between sensors v ij . For Cramer‐Rao efficient estimators of θ ij , the estimation error incurred by node u j when estimating u i ’s clock offset, denoted as ε ij , is given by

ϵ_{i j} = σ_{normalV}^{2} / {\tilde{m}}_{i j}

[7], where

σ_{normalV}^{2}

is the variance of the measured θ ij . Thus, the total local estimation error on node u i when estimating its neighbours’ clock offsets, denoted as ε i , is

ϵ_{i} ≜ \sum_{j} ϵ_{i j} = \sum_{j} \frac{σ_{V}^{2}}{m_{i j} (1 - P_{{out}_{i j}})} normal∀ j \in V, j \neq i

…”

Section: Model Statementmentioning

confidence: 99%

“…Node u i ’s pairwise synchronisation energy, defined as the local average energy function of node u i when synchronising with u j , is dictated by E ij = S ij m ij δ ij [7], where

δ_{i j} = T_{normalm} / ()(, 1 - P_{{normalout}_{i j}})

represents each message's average delivery time and T m is each message's time duration. In general, node u i shall synchronise with ( N − 1) nodes, consuming a total synchronisation energy, denoted as E i , equal to

E_{i} ≜ \sum_{j} E_{i j} normal∀ j \in V, j \neq i

.…”

Section: Energy Optimisation Against Network‐wide Estimation Errormentioning

confidence: 99%

“…More precisely, node u i sends m ij number of messages to u j , which successfully receives M ij messages, where M ij ≤ m ij . The expectation of M ij , denoted as

{\tilde{m}}_{i j}

, can be obtained as

{\tilde{m}}_{i j} = m_{i j} \cdot ()(, 1 - P_{{normalout}_{i j}})

[7], where

P_{{normalout}_{i j}}

denotes the outage probability of the wireless link. In general, the outage probability is a function S ij , g ij ,

γ_{0_{j}}

σ_{j}^{2}

at the receiver, a ij and the relative velocity between sensors v ij .…”

Section: Model Statementmentioning

confidence: 99%

See 2 more Smart Citations

Generalised trade‐off model for energy‐efficient WSN synchronisation

et al. 2015

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A mathematical framework to obtain a generalised energy-efficient trade-off model for generic wireless sensor networks (WSNs) while attaining sensing node synchronisation at a given network-wide estimation error threshold is presented. The model outputs both a theoretical optimal solution as well as a set of sub-optimal solutions to cater for real-world WSN designs. The robustness of the proposed framework is examined with an example in which randomly deployed sensors are affected by path-loss and uncorrelated Rayleigh fading effects.Introduction: Time synchronisation has become a major feature within application-specific wireless sensor networks (WSNs) to meet application demands such as data fusion, energy management and collision avoidance mechanisms. The need for energy-efficient time synchronisation has attracted intense research focus of recent years. A recent survey published in [1] details the challenges in unattended WSNs and the inevitable need for energy-efficient routing and data processing algorithms, although without mentioning the existence of a network-wide energy-efficient solution with regard to estimation error. Basagni et al.[2] highlight the underlying trade-off between consumed energy and data latency, and propose an energy harvesting technique to alleviate the application constraints. Lenzen et al. [3] have updated the PulseSync Protocol with further considerations of energy efficiency, message latency and estimation error. However, the solutions proposed in [2,3] have not considered optimal solutions for application-specific resource trade-off while attaining a given estimation error. In this Letter, we present a generalised model providing optimal solutions with a wide range of tunability for the energy-efficient synchronisation within WSN nodes, which has not been yet studied to the best our knowledge.

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“…In general, the outage probability is a function S ij , g ij ,

γ_{0_{j}}

σ_{j}^{2}

ϵ_{i j} = σ_{normalV}^{2} / {\tilde{m}}_{i j}

[7], where

σ_{normalV}^{2}

is the variance of the measured θ ij . Thus, the total local estimation error on node u i when estimating its neighbours’ clock offsets, denoted as ε i , is

ϵ_{i} ≜ \sum_{j} ϵ_{i j} = \sum_{j} \frac{σ_{V}^{2}}{m_{i j} (1 - P_{{out}_{i j}})} normal∀ j \in V, j \neq i

…”

Section: Model Statementmentioning

confidence: 99%

“…Node u i ’s pairwise synchronisation energy, defined as the local average energy function of node u i when synchronising with u j , is dictated by E ij = S ij m ij δ ij [7], where

δ_{i j} = T_{normalm} / ()(, 1 - P_{{normalout}_{i j}})

E_{i} ≜ \sum_{j} E_{i j} normal∀ j \in V, j \neq i

.…”

Section: Energy Optimisation Against Network‐wide Estimation Errormentioning

confidence: 99%

“…More precisely, node u i sends m ij number of messages to u j , which successfully receives M ij messages, where M ij ≤ m ij . The expectation of M ij , denoted as

{\tilde{m}}_{i j}

, can be obtained as

{\tilde{m}}_{i j} = m_{i j} \cdot ()(, 1 - P_{{normalout}_{i j}})

[7], where

P_{{normalout}_{i j}}

denotes the outage probability of the wireless link. In general, the outage probability is a function S ij , g ij ,

γ_{0_{j}}

σ_{j}^{2}

at the receiver, a ij and the relative velocity between sensors v ij .…”

Section: Model Statementmentioning

confidence: 99%

See 1 more Smart Citation

Generalised trade‐off model for energy‐efficient WSN synchronisation

et al. 2015

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show abstract

“…Mao et al applied the game theory to solve the network security problem of wireless network, and then a novel intrusion detection framework for WSNs was presented [15,16]. Briff et al proposed a lower bound on the energy required for synchronizing nodes in a WSN by using statistical estimation techniques [17]. Lu et al described the system architecture and design methodology of an ASCI-based sensor network device to meet some attributes for a class of applications [18].…”

Section: Introductionmentioning

confidence: 99%

Study on Wireless Network Communication in Stage Hydraulic Monitoring System Based on Internet of Things

Yue

Hui

Dong

et al. 2015

Discrete Dynamics in Nature and Society

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A novel stage hydraulic monitoring system based on Internet of Things (IoT) is proposed in this paper. Compared with the traditional wired system, the proposed system is a flexible working method and can save the cost. Furthermore, it has the low power consumption, high safety, and large scale network. The real-time pressure and flow data can be collected by using the nodes in ZigBee network. The fault detection and diagnosis process was used in this study, which was facilitated by measuring pressure of flow. When the monitored data exceeds the normal range, some failure may occur in the stage hydraulic system. If any failure occurs in the circuit, the maintainers can be informed immediately, which can greatly improve maintenance efficiency, ensuring the failure to be eliminated in time. Meanwhile, we can take advantage of wireless sensor network (WSN) to connect the multiple loops and then monitor the loops by using ZigBee technology, which greatly improves the efficiency of monitoring.

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Response time optimization with enhanced fault-tolerant wireless sensor network design for on-board rapid transit applications

Raghunath

Rengarajan²

2017

Cluster Comput

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A Primer on Energy-Efficient Synchronization of WSN Nodes over Correlated Rayleigh Fading Channels

Cited by 8 publications

References 16 publications

Generalised trade‐off model for energy‐efficient WSN synchronisation

Generalised trade‐off model for energy‐efficient WSN synchronisation

Study on Wireless Network Communication in Stage Hydraulic Monitoring System Based on Internet of Things

Response time optimization with enhanced fault-tolerant wireless sensor network design for on-board rapid transit applications

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