American Institute of Mathematical Sciences

Advanced
2012, 2(2): 357-375. doi: 10.3934/naco.2012.2.357

Performance analysis of transmission rate control algorithm from readers to a middleware in intelligent transportation systems

Sangkyu Baek 1, , Jinsoo Park 2, and  Bong Dae Choi 3,

1. 

School of Electrical Engineering, Korea University, 74 Inchon-ro, Seongbuk-gu, Seoul, South Korea
2. 

Central R&D Laboratory, Korea Telecom, 151 Taebong-ro, Seocho-gu, Seoul, South Korea
3. 

Department of Mathematics, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon 440-746, South Korea

Received  September 2011 Revised  May 2012 Published  May 2012

Radio Frequency Identification (RFID) systems consisting of tags, readers and a middleware are known as one of the promising technologies to be applied to diverse fields for realizing a ubiquitous society. A typical RFID system where RFID readers collect information from tags on vehicles and send the collected data to a middleware has been used primarily for various distribution and logistics applications. The collected information by the middleware is used for many intelligent transportation systems (ITS) applications such as traffic estimation and real-time navigation. We propose a dynamic transmission rate control algorithm between readers and a middleware with limited buffer capacity environment and present analytical performances. We construct 3-dimensional continuous time Markov chains whose Q-matrix has the form of a quasi-birth and death structure. From the analytical model, we obtain performance measures such as packet loss probability and system throughput. We find the maximum number of readers associated with a middleware while satisfying a constraint on packet loss probability.
Keywords: RFID system, Markov chain, readers, middleware, QBD process..
Mathematics Subject Classification: Primary: 68M20; Secondary: 90B18, 90B2.
Citation: Sangkyu Baek, Jinsoo Park, Bong Dae Choi. Performance analysis of transmission rate control algorithm from readers to a middleware in intelligent transportation systems. Numerical Algebra, Control & Optimization, 2012, 2 (2) : 357-375. doi: 10.3934/naco.2012.2.357
References:
[1]

K. Ali and H. Hassanein, Passive RFID for intelligent transportation systems,, in Proc. 6th IEEE Consumer Communications and Networking Conference (CCNC2009), (2009).   Google Scholar

[2]

S. Baek, Y. Lim, S. Rhee and K. Choi, Method for estimating population OD matrix based on probe vehicles,, KSCE Journal of Civil Engineering, 14 (2010), 231.  doi: 10.1007/s12205-010-0231-4.  Google Scholar

[3]

M. Bhuotani and S. Moradpour, "RFID Field Guide: Deploying Radio Frequency Identification Systems,", Prentice Hall, (2005).   Google Scholar

[4]

M. A. Chowdhurry and A. Sadek, "Fundamentals of Intelligent Transportation Systems Planning,", Artech House, (2003).   Google Scholar

[5]

J. C. Q. Dias, J. M. F. Calado, A. L. Osório and L. F. Morgado, Intelligent transportation system based on RFID and multi-agent approaches,, Pervasive Collaborative Networks, 283 (2008), 533.  doi: 10.1007/978-0-387-84837-2_55.  Google Scholar

[6]

EPCglobal, "The EPCglobal Architecture Framework,", 2007. Available from: \url{http://www.epcglobalinc.org/standards/architecture/architecture_1_2-framework-20070910.pdf}., ().   Google Scholar

[7]

M. Kim, J. Park, J. Oh, H. Chong and Y. Kim, Study on network architecture for traffic inormation collection systems based on RFID technology,, in Proc. 2008 IEEE Asia-Pacific Services Computing Conference, (2008).  doi: 10.1109/APSCC.2008.170.  Google Scholar

[8]

G. Latoche and V. Ramaswami, "Introduction to Matrix Analytic Methods in Stochastic Modeling,", ASA and SIAM, (1999).   Google Scholar

[9]

S. Moon and B. Yang, Effective monitoring of the concrete pouring operation in an RFID-based environment,, Journal of Computing in Civil Engineering, 24 (2010), 108.  doi: 10.1061/(ASCE)CP.1943-5487.0000004.  Google Scholar

[10]

C. Oberli, M. Torres-Territ and D. Landau, Performance evaluation of UHF RFID technologies for real-time passenger recognition in intelligent public transportation systems,, IEEE Transactions on Intelligent Transportation Systems, 11 (2010), 748.  doi: 10.1109/TITS.2010.2048429.  Google Scholar

show all references

References:
[1]

K. Ali and H. Hassanein, Passive RFID for intelligent transportation systems,, in Proc. 6th IEEE Consumer Communications and Networking Conference (CCNC2009), (2009).   Google Scholar

[2]

S. Baek, Y. Lim, S. Rhee and K. Choi, Method for estimating population OD matrix based on probe vehicles,, KSCE Journal of Civil Engineering, 14 (2010), 231.  doi: 10.1007/s12205-010-0231-4.  Google Scholar

[3]

M. Bhuotani and S. Moradpour, "RFID Field Guide: Deploying Radio Frequency Identification Systems,", Prentice Hall, (2005).   Google Scholar

[4]

M. A. Chowdhurry and A. Sadek, "Fundamentals of Intelligent Transportation Systems Planning,", Artech House, (2003).   Google Scholar

[5]

J. C. Q. Dias, J. M. F. Calado, A. L. Osório and L. F. Morgado, Intelligent transportation system based on RFID and multi-agent approaches,, Pervasive Collaborative Networks, 283 (2008), 533.  doi: 10.1007/978-0-387-84837-2_55.  Google Scholar

[6]

EPCglobal, "The EPCglobal Architecture Framework,", 2007. Available from: \url{http://www.epcglobalinc.org/standards/architecture/architecture_1_2-framework-20070910.pdf}., ().   Google Scholar

[7]

M. Kim, J. Park, J. Oh, H. Chong and Y. Kim, Study on network architecture for traffic inormation collection systems based on RFID technology,, in Proc. 2008 IEEE Asia-Pacific Services Computing Conference, (2008).  doi: 10.1109/APSCC.2008.170.  Google Scholar

[8]

G. Latoche and V. Ramaswami, "Introduction to Matrix Analytic Methods in Stochastic Modeling,", ASA and SIAM, (1999).   Google Scholar

[9]

S. Moon and B. Yang, Effective monitoring of the concrete pouring operation in an RFID-based environment,, Journal of Computing in Civil Engineering, 24 (2010), 108.  doi: 10.1061/(ASCE)CP.1943-5487.0000004.  Google Scholar

[10]

C. Oberli, M. Torres-Territ and D. Landau, Performance evaluation of UHF RFID technologies for real-time passenger recognition in intelligent public transportation systems,, IEEE Transactions on Intelligent Transportation Systems, 11 (2010), 748.  doi: 10.1109/TITS.2010.2048429.  Google Scholar

[1]

Samuel N. Cohen, Lukasz Szpruch. On Markovian solutions to Markov Chain BSDEs. Numerical Algebra, Control & Optimization, 2012, 2 (2) : 257-269. doi: 10.3934/naco.2012.2.257
[2]

Daoyi Xu, Yumei Huang, Zhiguo Yang. Existence theorems for periodic Markov process and stochastic functional differential equations. Discrete & Continuous Dynamical Systems - A, 2009, 24 (3) : 1005-1023. doi: 10.3934/dcds.2009.24.1005
[3]

Jingzhi Tie, Qing Zhang. An optimal mean-reversion trading rule under a Markov chain model. Mathematical Control & Related Fields, 2016, 6 (3) : 467-488. doi: 10.3934/mcrf.2016012
[4]

Ralf Banisch, Carsten Hartmann. A sparse Markov chain approximation of LQ-type stochastic control problems. Mathematical Control & Related Fields, 2016, 6 (3) : 363-389. doi: 10.3934/mcrf.2016007
[5]

Kun Fan, Yang Shen, Tak Kuen Siu, Rongming Wang. On a Markov chain approximation method for option pricing with regime switching. Journal of Industrial & Management Optimization, 2016, 12 (2) : 529-541. doi: 10.3934/jimo.2016.12.529
[6]

Lin Xu, Rongming Wang. Upper bounds for ruin probabilities in an autoregressive risk model with a Markov chain interest rate. Journal of Industrial & Management Optimization, 2006, 2 (2) : 165-175. doi: 10.3934/jimo.2006.2.165
[7]

Kazuhiko Kuraya, Hiroyuki Masuyama, Shoji Kasahara. Load distribution performance of super-node based peer-to-peer communication networks: A nonstationary Markov chain approach. Numerical Algebra, Control & Optimization, 2011, 1 (4) : 593-610. doi: 10.3934/naco.2011.1.593
[8]

Olli-Pekka Tossavainen, Daniel B. Work. Markov Chain Monte Carlo based inverse modeling of traffic flows using GPS data. Networks & Heterogeneous Media, 2013, 8 (3) : 803-824. doi: 10.3934/nhm.2013.8.803
[9]

Badal Joshi. A detailed balanced reaction network is sufficient but not necessary for its Markov chain to be detailed balanced. Discrete & Continuous Dynamical Systems - B, 2015, 20 (4) : 1077-1105. doi: 10.3934/dcdsb.2015.20.1077
[10]

Ralf Banisch, Carsten Hartmann. Addendum to "A sparse Markov chain approximation of LQ-type stochastic control problems". Mathematical Control & Related Fields, 2017, 7 (4) : 623-623. doi: 10.3934/mcrf.2017023
[11]

Xi Zhu, Meixia Li, Chunfa Li. Consensus in discrete-time multi-agent systems with uncertain topologies and random delays governed by a Markov chain. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 0-0. doi: 10.3934/dcdsb.2020111
[12]

Jing Liu, Xiaodong Liu, Sining Zheng, Yanping Lin. Positive steady state of a food chain system with diffusion. Conference Publications, 2007, 2007 (Special) : 667-676. doi: 10.3934/proc.2007.2007.667
[13]

Soliman A. A. Hamdallah, Sanyi Tang. Stability and bifurcation analysis of Filippov food chain system with food chain control strategy. Discrete & Continuous Dynamical Systems - B, 2020, 25 (5) : 1631-1647. doi: 10.3934/dcdsb.2019244
[14]

Boling Guo, Haiyang Huang. Smooth solution of the generalized system of ferro-magnetic chain. Discrete & Continuous Dynamical Systems - A, 1999, 5 (4) : 729-740. doi: 10.3934/dcds.1999.5.729
[15]

Jie Xu, Lanjun Dang. An efficient RFID anonymous batch authentication protocol based on group signature. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1489-1500. doi: 10.3934/dcdss.2019102
[16]

Ellina Grigorieva, Evgenii Khailov. A nonlinear controlled system of differential equations describing the process of production and sales of a consumer good. Conference Publications, 2003, 2003 (Special) : 359-364. doi: 10.3934/proc.2003.2003.359
[17]

Tien-Yu Lin. Effect of warranty and quantity discounts on a deteriorating production system with a Markovian production process and allowable shortages. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2020013
[18]

Dariush Mohamadi Zanjirani, Majid Esmaelian. An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1235-1259. doi: 10.3934/jimo.2018202
[19]

Pin-Shou Ting. The EPQ model with deteriorating items under two levels of trade credit in a supply chain system. Journal of Industrial & Management Optimization, 2015, 11 (2) : 479-492. doi: 10.3934/jimo.2015.11.479
[20]

Kun-Jen Chung, Pin-Shou Ting. The inventory model under supplier's partial trade credit policy in a supply chain system. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1175-1183. doi: 10.3934/jimo.2015.11.1175

 Impact Factor: 

Tools

Metrics

  • PDF downloads (16)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences