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Application of delta function to probabilistic modeling of communication delays in wireless networks – introduction and mathematical basis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the mathematical basis of a new method for building probabilistic models of communication delays in wireless networks in the case if the sent data are not correct and have to be retransmitted. The method is based on using a delta function sequence to describe delays in retransmissions between a transmitter and a receiver [1, 2] under assumption that the access time of the transmitter is taken as random and described by a probability density function. The retransmissions are caused by passive or active external disturbances influencing the communication channel established in the wireless medium [3, 4, 5]. Theoretical considerations are illustrated by examples using both measured and simulated data.
Wydawca
Rocznik
Strony
426--429
Opis fizyczny
Bibliogr. 34 poz., rys., wzory
Twórcy
autor
  • Silesian University of Technology, 10 Akademicka St., Gliwice
autor
  • Silesian University of Technology, 10 Akademicka St., Gliwice
Bibliografia
  • [1] Jakubiec J., Krupanek B.: Model of communication delays in wireless networks. Problems and progress in metrology. PPM'15, Kościelisko, 07-10 June 2015, pp. 45-48, 2015.
  • [2] Krupanek B., Bogacz R.: Applications of wireless transmission model in indoor environment. IMEKO XXI World Congress, Prague, August, 30 September 4, 2015.
  • [3] Krupanek B., Bogacz R.: Investigations of transmission delays in ZigBee networks. Prz. Elektrot., nr 1, pp. 70-75, 2014.
  • [4] Krupanek B., Bogacz R.: Modelling of communication delays in wireless networks. Advances measurement tools in technical diagnostics for systems' reliability and safety. 13th IMEKO TC10 Workshop on Technical Diagnostics, Warsaw, Poland, June 26-27, 2014. Proceedings, pp. 21-26, 2014.
  • [5] Jakubiec J., Krupanek B.: Probabilistyczny model opóźnień transmisji w jednorodnym systemie bezprzewodowym poddawanym zaburzeniom. Prz. Elektrot., nr 11, s. 20-22, 2014.
  • [6] Yuan L., Zhu Y.: Modeling and Simulating Wireless Sensor Transportation Monitoring Network. The Sixth World Congress on Intelligent Control and Automation, Vol. 2, 2006, pp. 8640-8644.
  • [7] Chaudhary D. D., Waghmare L. M.: Quality of service analysis in wireless sensor network by controlling end-to-end delay.7th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2012, pp. 703-708.
  • [8] Xiong X., Tan J., Lin X.: Study on Communication Architecture Design of Wide-Area Measurement System. IEEE Transactions on Power Delivery, Vol. 28, Issue 3, 2013, pp. 1542-1547.
  • [9] Topór-Kamiński T., Krupanek B., Homa J.: Delays Models of Measurement and Control Data Transmission Network. Advanced Technologies for Intelligent Systems of National Border Security, Studies in Computational Intelligence, 440, pp. 257-279.
  • [10] Miczulski W., Powroźnik P.: A new elastic scheduling task model in the node of a control and measurement system. Metrol. Meas. Syst., Vol. XX (2013), No. 1, pp. 87-98.
  • [11] Chitre, M., Motani, M., Shahabudeen, S.: Throughput of Networks With Large Propagation Delays. IEEE Journal of Oceanic Engineering, Vol. 37, Issue 4, 2012, pp. 645-658.
  • [12] Bobbio A.: System Modelling with Petri Nets. Instituto Elettrotecnico Nazionale Galileo Ferraris, System Reliability Assessment, Kluwer p. c., 1990, p. 102-143.
  • [13] Yang S., Kuipers, F.A.: Traffic uncertainty models in network planning. IEEE Communications Magazine, Vol. 52, Issue 2, 2014, pp. 172-177.
  • [14] Nistor M., Lucani D. E., Vinhoza T. T. V., Costa R. A.: On the Delay Distribution of Random Linear Network Coding. IEEE Journal on Selected Areas in Communications, Vol. 29, Issue 5, 2011, pp. 1084-1093.
  • [15] LiuY., Ni L., Chuanping H.: A Generalized Probabilistic Topology Control for Wireless Sensor Networks. IEEE Journal on Selected Areas in Communications, Vol. 30, Issue 9, 2012, pp. 2776-2780.
  • [16] Angrisani L., Capriglione D., Ferrigno L., Miele G.: An Internet Protocol Packet Delay Variation Estimator for Reliable Quality Assessment of Video-Streaming Services. IEEE Transactions on Instrumentation and Measurement, Vol. 62, Issue 5, 2013, pp. 914-923.
  • [17] Bao D., De VitoL., Rapuano S.:A Histogram-Based Segmentation Method for Wideband Spectrum Sensing in Cognitive Radios. IEEE Transactions on Instrumentation and Measurement, Vol. 62, Issue 7, 2013, pp. 1900-1908.
  • [18] Priebe S., Kurner T.: Stochastic Modeling of THz Indoor Radio Channels. IEEE Transactions on Wireless Communications, Vol. 12, Issue 9, 2013, pp. 4445-4455.
  • [19] Quer G., Meenakshisundaram H., Tamma B. R., Manoj B. S.: Using Bayesian Networks for Cognitive Control of Multi-hop Wireless Networks. Military Communications Conference MILCOM, 2010, pp. 201–206.
  • [20] Zhang Z.: Theory and Applications of Network Error Correction Coding. Proceedings of the IEEE, Vol. 99, Issue 3, 2011, pp. 406-420.
  • [21] Jakubiec J.: A New Conception of Measurement Uncertainty Calculation. Acta Physica Polonica A. Vol. 124 (2013), No. 3, pp. 436-444.
  • [22] Krupanek B.: Modeling of transmission delays caused by disturbances in the wireless networks in IEEE 802.15.4 standard (in Polish), PhD thesis, Gliwice 2012.
  • [23] Akyildiz I. F., Sankarasubramaniam Y., Cayirci E.: A survey on sensor networks. IEEE Communication Magazine, 2002, 40(8), pp. 102-114.
  • [24] Gallo D., Landi C., PasquinoN.: Multi-sensor network for urban electromagnetic field monitoring. IEEE Transactions on Instrumentation and Measurement, 2009, 58(9), pp. 3315-3322.
  • [25] Moustpha A. I., Selmic R. R.: Wireless sensor network modeling using modified recurrent neural networks: application to fault detection. IEEE Transactions on Instrumentation and Measurement, 2008, 57(5), pp. 981-988.
  • [26] Oliver R., Fohler G.: Probabilistic estimation of end-to-end path latency in wireless sensor networks. IEEE 6th International Conference on Mobile Adhoc and Sensor Systems MASS '09, 2009, pp. 423–431.
  • [27] Sun S., Deng Z.: Multi-sensor optimal information fusion Kalman filter. Automatica, 2004, 40(6), pp. 1017-1023.
  • [28] Ribeiro A., Schizas I., Roumeliotis S., Giannakis G.: Kalman filtering in wireless sensor networks. IEEE Control Systems, 2010, 30(2), pp. 66-86.
  • [29] Cui P., Zhang H., Lam J., Ma L.: Real-time Kalman filtering based on distributed measurements. International Journal of Robust and Nonlinear Control, 2013, 23(14), pp. 1597-1608.
  • [30] Krupanek B.: Simulations of experimental set up based on ZigBee standard using Opnet. International PhD Workshop OWD 2009, Wisła, 17-20.10.2009, pp. 262-267.
  • [31] Topór-Kamiński T., Grygiel M.: Probabilistic Modeling of Delays in Data Transmission System with Wireless Network Interfaces Employing Random String Functions. Acta Physica Polonica A. Vol. 124 (2013), No. 3, pp. 578-585.
  • [32] Eady F.: Hands-On ZigBee: Implementing 802.15.4 with Microcontrollers. Elsevier Inc, 2007.
  • [33] Porter F.: Testing Consistency of Two Histograms, California Institute of Technology Lauritsen Laboratory for High Energy Physics, Pasadena, 2008.
  • [34] Gupta S. C.: Delta Function. IEEE Transactions on Education, vol. 7, iss. 1. 1964, pp. 16-22.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b8a980e5-42ce-4440-8824-092f980c1edc
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