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Telecommunication Systems
Erschienen in: Telecommunication Systems 2/2022

22.08.2022

Measuring synchronization precision in mobile sensor networks

verfasst von: F. C. S. Eiras, W. L. Zucchi

Erschienen in: Telecommunication Systems | Ausgabe 2/2022

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Abstract

Many applications involving the use of drones in sensor networks require precise synchronization from the involved sensors. However, not many papers evaluate the precision of the synchronism that can be obtained by means of exchanging packets in mobile sensor networks. This lack can be explained by the difficulties encountered in modeling the swift movements of the drones and the large volumes of these elements in a monitored area. Measuring phase noise also requires techniques that are quite different from those commonly used to analyze data networks. This paper suggests a simulation model based on discrete events, deployed in a Matlab Simulink® tool, which combines calculating loss probability in a mobile sensor network with measuring phase error between the sensor clock and the reference clock. Phase error is evaluated by Maximum Time Interval Error (MTIE) and Allan Deviation (ADEV) statistics. Results show that synchronism precision is strongly connected to the probability of message loss and that, with fewer losses, precision in the order of tens of nano-seconds can be obtained.
Vorheriger Artikel A model for a practical evaluation of a DASH-based rate adaptive algorithm over HTTP
Nächster Artikel Spectrally efficient IR-UWB pulse designs based on linear combinations of Gaussian Derivatives

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Literatur
1.
Zervopoulos, A., et al. (2020). Wireless sensor network synchronization for precision agriculture applications. Agriculture, 10(3), 1–20.CrossRef
2.
De Rango, F., Potrino, G., Tropea, M., Santamaria, A. F., & Fazio, P. (2019). Scalable and ligthway bio-inspired coordination protocol for FANET in precision agriculture applications. Computers & Electrical Engineering, 74, 305–318.CrossRef
3.
Bekmezci, İ, Sahingoz, O. K., & Temel, Ş. (2013). Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3), 1254–1270.CrossRef
4.
Federal Aviation Administration. (2016). Summary of small unmanned aircaraft rule (part 107). Washington DC.
5.
DECEA. (2015). “ICA 100-40 - Sistemas de Aeronaves Remotamente Pilotadas e o Acesso ao Espaço Aéreo Brasileiro,” p. 76.
6.
Sahingoz, O. K. (2013). Networking models in flying ad-hoc networks (FANETs): Concepts and challenges. Journal of Intelligent and Robotic Systems, 74(1–2), 513–527.
7.
Ely, T. A., Burt, E. A., Prestage, J. D., Seubert, J. M., & Tjoelker, R. L. (2018). Using the deep space atomic clock for navigation and science. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 65(6), 950–961.CrossRef
8.
Sommer, P., Wattenhofer, R. (2009). Gradient Clock synchronization in wireless sensor networks. In International conference on information processing in sensor networks, 2009. IPSN 2009. pp. 37–48.
9.
Cho, H. C. H., Jung, J. J. J., Cho, B. C. B., Jin, Y. J. Y., Lee, S.-W. L. S.-W., Baek, B. Y. (2009) Precision time synchronization using IEEE 1588 for wireless sensor networks. In 2009 international conference on computational science and engineering, vol. 2.
10.
del Río, J., Toma, D., Shariat-Panahi, S., Mànuel, A., & Ramos, H. G. (2012). Precision timing in ocean sensor systems. Measurement Science and Technology, 23(2), 025801.CrossRef
11.
Mills, D. L. (1991). Internet time synchronization: The network time protocol. IEEE Transactions on Communications, 39(10), 1482–1493.CrossRef
12.
Novick, A. N., Lombardi, M. A. (2015). Practical limitations of NTP time transfer. In 2015 joint conference of the IEEE international frequency control symposium & the European frequency and time forum, FCS 2015 - Proc., no. April, pp. 570–574.
13.
IEEE. (2008). IEEE Std 1588–2008, IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems, vol. 2008.
14.
Elson, J., Girod, L., & Estrin, D. (2002). Fine-grained network time synchronization using reference broadcasts. ACM SIGOPS Operating Systems Review, 36, 147.CrossRef
15.
Ganeriwal, S., Kumar, R., Srivastava,M. B. (2003). Timing-sync protocol for sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems - SenSys ’03, pp. 138–149.
16.
Maróti, M., Kusy,B., Simon, G., Lédeczi, Á. (2004). The flooding time synchronization protocol. In ACM International Conference on Embedded Networked Sensor Systems, pp. 39–49.
17.
Wobschall, D., Ma, Y. (2010) Synchronization of wireless sensor networks using a modified IEEE 1588 protocol. In 2010 IEEE international symposium on precision clock synchronization for measurement, control and communication, pp. 67–70.
18.
Aleksander, N., Kamil, K., Krzysztof, D., & Roman, K. (2010). Differential navigation for UAV platforms with mobile reference station. In H. Fujita & J. Sasaki (Eds.), Selected topics in applied computer science (pp. 465–471). WSEAS Press.
19.
Wang, Y., Wu, S., Chen, Z., Gao, X., & Chen, G. (2017). Coverage problem with uncertain properties in wireless sensor networks: A survey. Computer Networks, 123, 200–232.CrossRef
20.
Chakraborty, S., Goyal, N. K., Mahapatra, S., & Soh, S. (2020). A Monte-Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes. Reliability Engineering and System Safety, 193, 14.CrossRef
21.
Wang, B., Lim, H. B., & Ma, D. (2009). A survey of movement strategies for improving network coverage in wireless sensor networks. Computer Communications, 32(13–14), 1427–1436.CrossRef
22.
Senouci, M. R., Mellouk, A., Asnoune, K., & YazidBouhidel, F. (2015). Movement-assisted sensor deployment algorithms: A survey and taxonomy. IEEE Communications Surveys Tutorials, 17, 2493–2510.CrossRef
23.
Deif, D. S., & Gadallah, Y. (2014). Classification of wireless sensor networks deployment techniques. IEEE Communications Surveys & Tutorials, 16(2), 834–855.CrossRef
24.
Farsi, M., Elhosseini, M. A., Badawy, M., Arafat Ali, H., & Zain Eldin, H. (2019). Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access, 7, 28940–28954.CrossRef
25.
Elhabyan, R., Shi, W., & St-Hilaire, M. (2019). Coverage protocols for wireless sensor networks: Review and future directions. Journal of Communications and Networks, 21(1), 45–60.CrossRef
26.
Kulkarni, R. V., Member, S., & Kumar, G. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2), 262–267.CrossRef
27.
Aziz, N. A. B. A., Mohemmed, A. W., Alias, M. Y. (2009) A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram. In 2009 International conference on networking, sensing and control, pp. 602–607.
28.
Aurenhammer, F. (1991). Voronoi diagrams - a survey of a fundamental geometric data structure. ACM Computing Surveys, 23(3), 345–405.CrossRef
29.
Alia, O. M., & Al-Ajouri, A. (2017). Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm. IEEE Sensors Journal, 17(3), 882–896.CrossRef
30.
Eiras, F. C. S., Zucchi, W. L. (2019). A statistical method for area coverage estimation and loss probability analysis on mobile sensor. In 6th international electronic conference on sensors and applications, pp. 1–21.
31.
32.
ITU-T G.810. (1996). ITU-T G.810 Definitions and terminology for synchronization networks.
33.
Riley, W. J. (1994). Handbook of frequency stability analysis, vol. 31, no. 1. NIST Special Publication 1065.
34.
ITU-T G.8261. (2008). ITU-T G.8261/Y.1361 timing and synchronization aspects in packet networks.
35.
ITU-T G.8262. (2010). ITU-T G.8262/Y.1362 - timing characteristics of a synchronous ethernet equipment slave clock.
36.
Dobrogowski, A., & Kasznia, M. (2010). Methods of real-time calculation of allan deviation and time deviation. Advances in Electronics and Telecommunications, 1(2), 42–46.
37.
MathWorks. (2011). SIMULINK academic version, R2011.
38.
Metadaten
Titel
Measuring synchronization precision in mobile sensor networks
verfasst von
F. C. S. Eiras
W. L. Zucchi
Publikationsdatum
22.08.2022
Verlag
Springer US
Erschienen in
Telecommunication Systems / Ausgabe 2/2022
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-022-00944-9

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