Fast distributed multi-hop relative time synchronization protocol and estimators for wireless sensor networks

Djenouri, Djamel; Merabtine, Nassima; Mekahlia, Fatma Zohra; Doudou, Messaoud

doi:10.1016/j.adhoc.2013.06.001

Cited by 38 publications

(10 citation statements)

References 33 publications

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“…Therefore, the cluster members will take advantages of such synchronization traffic to adjust their local clocks. In [22], a distributed receiver to receiver protocol is provided to cope with the need of a fixed reference which is the major drawback of the scheme introduced by RBS [3]. In [23], a fully-distributed synchronization algorithm has been proposed for WSNs under unknown exponential delays.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Consensus-Based Clock Synchronization Protocol for Wireless Sensor Networks

Aissaoua

Aliouat

Bounceur

et al. 2017

Wireless Pers Commun

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The birth of computer networks and distributed systems has led to the appearance of the clock synchronization problem. This issue has gained increasing importance with the emergence of new resource constrained networks such as wireless sensor networks. In this paper, we propose a new distributed clock synchronization algorithm, referred to as Weighted Consensus Clock Synchronization (WCCS), whose objective is to achieve a consensus clock among network nodes. In this distributed approach and in contrast to centralized schemes, each node periodically exchanges the local clock reading with its immediate neighbor nodes. Then, each node employs these time informations to calculate its relative offset and skew with respect to its neighbor nodes using a weighted average consensus based technique. The effectiveness of WCCS is proved through both simulations and an experimental study on TelosB mote using TinyOS.

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Section: Related Workmentioning

confidence: 99%

A Distributed Consensus-Based Clock Synchronization Protocol for Wireless Sensor Networks

Aissaoua

Aliouat

Bounceur

et al. 2017

Wireless Pers Commun

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show abstract

“…An 8-bit 16 MHz ATmega 328p microcontroller that is embedded in the Arduino Nano board is adopted in this study. It has 32 KB of flash memory, 1 KB of SRAM [26], [27], an 8 channel 10-bit analog to digital converter (ADC), 14 digital input/output pins with six that provide PWM, a USB connection, an SPI interface, a serial port, and operates at 5 volts from a battery or power jack. The Arduino can be programmed in a language that is similar to C++ with some modifications and simplifications, and the essential libraries of Arduino have been written in C and C++ [28].…”

Section: A Strain Gauge Sensor Installation On the Crank Armmentioning

confidence: 99%

Development and Validation of a Track Bicycle Instrument for Torque Measurement Using the Zigbee Wireless Sensor Network

Gharghan

Nordin

Ismail

2017

International Journal on Smart Sensing and Intelligent Systems

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Abstract-This study evaluates the consistency between the bicycle torque of the proposed system, and a Schoberer Rad Messtechnik (SRM) system. The torque was measured while a trainer was cycling indoors, and the measured values were compared with those of the SRM system. A Bland-Altman statistical analysis indicated that the measured values agreed with the SRM within 95%. The mean absolute percentage error and root mean square error between the measurements of the proposed system and the SRM system were 8.25%, and 1.86, respectively. The results show that the bicycle torque can be measured accurately and transmitted using ZigBee wireless protocol.

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“…u i is the control input. The coefficient w ij is non-negative element of adjacency matrix associated with the graph G over mobile sensors defined in equation (1). N i is the set of neighbours of sensor v i defined in section 'Graph theory'.…”

Section: Dynamics Of Mobile Sensor Systemsmentioning

confidence: 99%

“…1,2 In these sceneries, deployment, rendezvous and consensus usually are needed in certain tasks of mobile sensor networks. Consensus seeking is a very interesting problem in control of mobile sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed convergence control for consensus of mobile sensor networks with dynamical topologies

Cheng

Dong

et al. 2016

International Journal of Distributed Sensor Networks

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Mobile sensors need to reach consensus in many applications such as unmanned vehicles, multi-agent systems and environmental monitoring. In these scenarios, it is very important to reduce the power consumption as well as make sensors reach consensus as fast as possible. In this article, we propose a novel guaranteed convergence control algorithm to switch topologies of mobile sensors so that we can reduce power consumption in the sensor network and make the mobile sensors reach consensus with guaranteed convergence as well. The topology graphs over the mobile sensors are unconnected, directed, dynamical and switched periodically at any moment, while the joint graphs of dynamical topologies are connected in a bounded time period. The guaranteed convergence rate of consensus is derived on a method based on the variable decomposition. Some illustrative examples are provided to demonstrate the validity and effectiveness of the results.

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Fast distributed multi-hop relative time synchronization protocol and estimators for wireless sensor networks

Cited by 38 publications

References 33 publications

A Distributed Consensus-Based Clock Synchronization Protocol for Wireless Sensor Networks

A Distributed Consensus-Based Clock Synchronization Protocol for Wireless Sensor Networks

Development and Validation of a Track Bicycle Instrument for Torque Measurement Using the Zigbee Wireless Sensor Network

Guaranteed convergence control for consensus of mobile sensor networks with dynamical topologies

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