Continuous approximation of stochastic models for wireless sensor networks

Mahmoud Talebi; Jan Friso Groote; Jean-Paul Linnartz

doi:10.1109/SCVT.2015.7374240

Continuous approximation of stochastic models for wireless sensor networks

Mahmoud Talebi, Jan Friso Groote, Jean-Paul Linnartz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

5 Downloads (Pure)

Abstract

Stochastic analysis of wireless sensor networks becomes exceedingly hard as the number of nodes in a network grows large. In this paper we intend to address this issue by proposing a method of modeling large networks by dynamical systems rather than explicit Markov models, called Mean-Field Approximation. We verify the suitability of Mean-Field Approximation by analyzing ALOHA, by both studying a discrete model and a system of differential equations and then by comparing these models. We then extend the modeling technique in order to express characteristics of a network running a CSMA/CA protocol.

Original language	English
Title of host publication	2015 IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 24 November 2015, Luxembourg City
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1109/SCVT.2015.7374240
Publication status	Published - 24 Nov 2015

Keywords

Analytical models
Approximation methods
Markov processes
Mathematical model
Protocols
Receivers
Wireless sensor networks

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10.1109/SCVT.2015.7374240

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title = "Continuous approximation of stochastic models for wireless sensor networks",

abstract = "Stochastic analysis of wireless sensor networks becomes exceedingly hard as the number of nodes in a network grows large. In this paper we intend to address this issue by proposing a method of modeling large networks by dynamical systems rather than explicit Markov models, called Mean-Field Approximation. We verify the suitability of Mean-Field Approximation by analyzing ALOHA, by both studying a discrete model and a system of differential equations and then by comparing these models. We then extend the modeling technique in order to express characteristics of a network running a CSMA/CA protocol.",

keywords = "Analytical models, Approximation methods, Markov processes, Mathematical model, Protocols, Receivers, Wireless sensor networks",

author = "Mahmoud Talebi and Groote, {Jan Friso} and Jean-Paul Linnartz",

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Continuous approximation of stochastic models for wireless sensor networks. / Talebi, Mahmoud; Groote, Jan Friso ; Linnartz, Jean-Paul.
2015 IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 24 November 2015, Luxembourg City. Piscataway: Institute of Electrical and Electronics Engineers, 2015. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Groote, Jan Friso

AU - Linnartz, Jean-Paul

PY - 2015/11/24

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N2 - Stochastic analysis of wireless sensor networks becomes exceedingly hard as the number of nodes in a network grows large. In this paper we intend to address this issue by proposing a method of modeling large networks by dynamical systems rather than explicit Markov models, called Mean-Field Approximation. We verify the suitability of Mean-Field Approximation by analyzing ALOHA, by both studying a discrete model and a system of differential equations and then by comparing these models. We then extend the modeling technique in order to express characteristics of a network running a CSMA/CA protocol.

AB - Stochastic analysis of wireless sensor networks becomes exceedingly hard as the number of nodes in a network grows large. In this paper we intend to address this issue by proposing a method of modeling large networks by dynamical systems rather than explicit Markov models, called Mean-Field Approximation. We verify the suitability of Mean-Field Approximation by analyzing ALOHA, by both studying a discrete model and a system of differential equations and then by comparing these models. We then extend the modeling technique in order to express characteristics of a network running a CSMA/CA protocol.

KW - Analytical models

KW - Approximation methods

KW - Markov processes

KW - Mathematical model

KW - Protocols

KW - Receivers

KW - Wireless sensor networks

U2 - 10.1109/SCVT.2015.7374240

DO - 10.1109/SCVT.2015.7374240

M3 - Conference contribution

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EP - 6

BT - 2015 IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT), 24 November 2015, Luxembourg City

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Continuous approximation of stochastic models for wireless sensor networks

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Keywords

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