414
Views
0
CrossRef citations to date
0
Altmetric
Articles

Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events

, , &
Pages 77-89 | Received 08 Feb 2022, Accepted 13 Jun 2023, Published online: 24 Jul 2023

References

  • Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M. I. (2003), “An Introduction to MCMC for Machine Learning,” Machine Learning, 50, 5–43. DOI: 10.1023/A:1020281327116.
  • Anselin, L. (1995), “Local indicators of spatial association–LISA,” Geographical Analysis, 27, 93–115. DOI: 10.1111/j.1538-4632.1995.tb00338.x.
  • Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2003), Hierarchical Modeling and Analysis for Spatial Data, London: Chapman and Hall/CRC.
  • Banerjee, S., Wall, M. M., and Carlin, B. P. (2003), “Frailty Modeling for Spatially Correlated Survival Data, with Application to Infant Mortality in Minnesota,” Biostatistics, 4, 123–142. DOI: 10.1093/biostatistics/4.1.123.
  • Barrett, C., Beckman, R., Channakeshava, K., Huang, F., Kumar, V. A., Marathe, A., Marathe, M. V., and Pei, G. (2010), “Cascading Failures in Multiple Infrastructures: From Transportation to Communication Network,” in 2010 5th International Conference on Critical Infrastructure (CRIS), IEEE, pp. 1–8. DOI: 10.1109/CRIS.2010.5617569.
  • Bevilacqua, M., Caamaño-Carrillo, C., and Porcu, E. (2022), “Unifying Compactly Supported and Matérn Covariance Functions in Spatial Statistics,” Journal of Multivariate Analysis, 189, 104949. DOI: 10.1016/j.jmva.2022.104949.
  • Błaszczyszyn, B., Karray, M. K., and Keeler, H. P. (2013), “Using Poisson Processes to Model Lattice Cellular Networks,” in 2013 Proceedings IEEE INFOCOM, pp. 773–781.
  • Brooks, S. P. (1998), “MCMC Convergence Diagnosis via Multivariate Bounds on Log-Concave Densities,” The Annals of Statistics, 26, 398–433. DOI: 10.1214/aos/1030563991.
  • Cai, J., Cigsar, C., and Ye, Z.-S. (2020), “Assessing the Effect of Repair Delays on a Repairable System,” Journal of Quality Technology, 52, 293–303. DOI: 10.1080/00224065.2019.1611347.
  • Chen, X., Jin, Y., Qiang, S., Hu, W., and Jiang, K. (2015), “Analyzing and Modeling Spatio-Temporal Dependence of Cellular Traffic at City Scale,” in 2015 IEEE International Conference on Communications, pp. 3585–3591.
  • Chu, K.-C., and Lin, F. Y.-S. (2006), “Survivability and Performance Optimization of Mobile Wireless Communication Networks in the Event of Base Station Failure,” Computers & Electrical Engineering, 32, 50–64. DOI: 10.1016/j.compeleceng.2006.01.015.
  • Claeskens, G., and Consentino, F. (2008), “Variable Selection with Incomplete Covariate Data,” Biometrics, 64, 1062–1069. DOI: 10.1111/j.1541-0420.2008.01003.x.
  • Coetzee, J. L. (1997), “The Role of NHPP Models in the Practical Analysis of Maintenance Failure Data,” Reliability Engineering & System Safety, 56, 161–168. DOI: 10.1016/S0951-8320(97)00010-0.
  • Cook, R. J., and Lawless, J. (2007), The Statistical Analysis of Recurrent Events, New York: Springer.
  • Cressie, N., and Wikle, C. K. (2015), Statistics for Spatio-Temporal Data, Hoboken: Wiley.
  • Dalalyan, A. S. (2017), “Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities,” Journal of the Royal Statistical Society, Series B, 79, 651–676. DOI: 10.1111/rssb.12183.
  • Doerry, N. (2015), “Naval Power Systems: Integrated Power Systems for the Continuity of the Electrical Power Supply,” IEEE Electrification Magazine, 3, 12–21. DOI: 10.1109/MELE.2015.2413434.
  • Economides, N. (1999), “Real Options and the Costs of the Local Telecommunications Network,” in The New Investment Theory of Real Options and Its Implication for Telecommunications Economics, eds. J. Alleman, and E. Noam, pp. 207–213, New York: Springer.
  • Econotimes (2021), “KT Telecom is Paying $29 to $33 Million for Massive Network Outage,” Online; accessed Nov 2, 2021.
  • Federal Communications Commission (2021), “FCC Reaches $19.5M Settlement of T-Mobile 911 Outage Investigation,” Online; accessed Nov 23, 2021.
  • Fredette, M., and Lawless, J. F. (2007), “Finite-Horizon Prediction of Recurrent Events, with Application to Forecasts of Warranty Claims,” Technometrics, 49, 66–80. DOI: 10.1198/004017006000000390.
  • Gabriel, E., and Diggle, P. J. (2009), “Second-Order Analysis of Inhomogeneous Spatio-Temporal Point Process Data,” Statistica Neerlandica, 63, 43–51. DOI: 10.1111/j.1467-9574.2008.00407.x.
  • Gelfand, A. E., Diggle, P., Guttorp, P., and Fuentes, M. (2010), Handbook of Spatial Statistics, Boca Raton, FL: CRC Press.
  • Genz, A., and Malik, A. (1983), “An Imbedded Family of Fully Symmetric Numerical Integration Rules,” SIAM Journal on Numerical Analysis, 20, 580–588. DOI: 10.1137/0720038.
  • González, J. A., Rodríguez-Cortés, F. J., Cronie, O., and Mateu, J. (2016), “Spatio-Temporal Point Process Statistics: A Review,” Spatial Statistics, 18, 505–544. DOI: 10.1016/j.spasta.2016.10.002.
  • Guo, R., and Love, C. (1994), “Simulating Nonhomogeneous Poisson Processes with Proportional Intensities,” Naval Research Logistics, 41, 507–522. DOI: 10.1002/1520-6750(199406)41:4<507::AID-NAV3220410404>3.0.CO;2-H.
  • Kaufman, C. G., Schervish, M. J., and Nychka, D. W. (2008), “Covariance Tapering for Likelihood-based Estimation in Large Spatial Data Sets,” Journal of the American Statistical Association, 103, 1545–1555. DOI: 10.1198/016214508000000959.
  • Krishnamoorthy, K., and Peng, J. (2011), “Improved Closed-Form Prediction Intervals for Binomial and Poisson Distributions,” Journal of Statistical Planning and Inference, 141, 1709–1718. DOI: 10.1016/j.jspi.2010.11.021.
  • Kwasinski, A., and Tang, A. K. (2011), “Telecommunications Performance in the M = 9.0 Off-shore East Coast of Japan Earthquake and Tsunami, March 11, 2011,” in Proceedings of the International Symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, March 1–4, 2012, Tokyo, Japan, pp. 1–4.
  • Lawless, J. F., and Nadeau, C. (1995), “Some Simple Robust Methods for the Analysis of Recurrent Events,” Technometrics, 37, 158–168. DOI: 10.1080/00401706.1995.10484300.
  • Le Gat, Y., and Eisenbeis, P. (2000), “Using Maintenance Records to Forecast Failures in Water Networks,” Urban Water, 2, 173–181. DOI: 10.1016/S1462-0758(00)00057-1.
  • Levine, R. A., and Casella, G. (2001), “Implementations of the Monte Carlo EM Algorithm,” Journal of Computational and Graphical Statistics, 10, 422–439. DOI: 10.1198/106186001317115045.
  • Lewis, P., and Shedler, G. (1976), “Simulation of Nonhomogeneous Poisson Processes with Log Linear Rate Function,” Biometrika, 63, 501–505. DOI: 10.1093/biomet/63.3.501.
  • Lewis, P. W., and Shedler, G. S. (1979), “Simulation of Nonhomogeneous Poisson Processes by Thinning,” Naval Research Logistics Quarterly, 26, 403–413. DOI: 10.1002/nav.3800260304.
  • Li, H., Calder, C. A., and Cressie, N. (2007), “Beyond Moran’s I: Testing for Spatial Dependence based on the Spatial Autoregressive Model,” Geographical Analysis, 39, 357–375. DOI: 10.1111/j.1538-4632.2007.00708.x.
  • Li, J., Hong, Y., Thapa, R., and Burkhart, H. E. (2015), “Survival Analysis of Loblolly Pine Trees with Spatially Correlated Random Effects,” Journal of the American Statistical Association, 110, 486–502. DOI: 10.1080/01621459.2014.995793.
  • Link, W. A., and Eaton, M. J. (2012), “On Thinning of Chains in MCMC,” Methods in Ecology and Evolution, 3, 112–115. DOI: 10.1111/j.2041-210X.2011.00131.x.
  • Louis, T. A. (1982), “Finding the Observed Information Matrix When using the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 44, 226–233. DOI: 10.1111/j.2517-6161.1982.tb01203.x.
  • Massey, W. A., Parker, G. A., and Whitt, W. (1996), “Estimating the Parameters of a Nonhomogeneous Poisson Process with Linear Rate,” Telecommunication Systems, 5, 361–388. DOI: 10.1007/BF02112523.
  • Meyn, S. P., and Tweedie, R. L. (2012), Markov Chains and Stochastic Stability, London: Springer.
  • Minasny, B., and McBratney, A. B. (2005), “The Matérn Function as a General Model for Soil Variograms,” Geoderma, 128, 192–207. DOI: 10.1016/j.geoderma.2005.04.003.
  • Nelson, W. B. (2003), Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications, Philadelphia: SIAM.
  • Nielsen, J., and Dean, C. B. (2008), “Adaptive Functional Mixed NHPP Models for the Analysis of Recurrent Event Panel Data,” Computational Statistics & Data Analysis, 52, 3670–3685. DOI: 10.1016/j.csda.2007.12.003.
  • Nobre, A. A., Schmidt, A. M., and Lopes, H. F. (2005), “Spatio-Temporal Models for Mapping the Incidence of Malaria in Pará,” Environmetrics, 16, 291–304. DOI: 10.1002/env.704.
  • Pham, H., and Zhang, X. (2003), “NHPP Software Reliability and Cost Models with Testing Coverage,” European Journal of Operational Research, 145, 443–454. DOI: 10.1016/S0377-2217(02)00181-9.
  • Qiu, C., Zhang, Y., Feng, Z., Zhang, P., and Cui, S. (2018), “Spatio-Temporal Wireless Traffic Prediction with Recurrent Neural Network,” IEEE Wireless Communications Letters, 7, 554–557. DOI: 10.1109/LWC.2018.2795605.
  • Roberts, G. O., and Rosenthal, J. S. (2004), “General State Space Markov Chains and MCMC Algorithms,” Probability Surveys, 1, 20–71. DOI: 10.1214/154957804100000024.
  • Shan, Q., Hong, Y., and Meeker, W. Q. (2020), “Seasonal Warranty Prediction based on Recurrent Event Data,” The Annals of Applied Statistics, 14, 929–955. DOI: 10.1214/20-AOAS1333.
  • Sherman, M. (2011), Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties, Chichester: Wiley.
  • Shrestha, D. L., and Solomatine, D. P. (2006), “Machine Learning Approaches for Estimation of Prediction Interval for the Model Output,” Neural Networks, 19, 225–235. DOI: 10.1016/j.neunet.2006.01.012.
  • Snow, A. P., Varshney, U., and Malloy, A. D. (2000), “Reliability and Survivability of Wireless and Mobile Networks,” Computer, 33, 49–55. DOI: 10.1109/2.869370.
  • Soyer, R., and Tarimcilar, M. M. (2008), “Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach,” Management Science, 54, 266–278. DOI: 10.1287/mnsc.1070.0776.
  • Taghipour, S., and Banjevic, D. (2011), “Periodic Inspection Optimization Models for a Repairable System Subject to Hidden Failures,” IEEE Transactions on Reliability, 60, 275–285. DOI: 10.1109/TR.2010.2103596.
  • Taghipour, S., Banjevic, D., and Jardine, A. K. (2011), “Reliability Analysis of Maintenance Data for Complex Medical Devices,” Quality and Reliability Engineering International, 27, 71–84. DOI: 10.1002/qre.1084.
  • Tiefelsdorf, M., and Boots, B. (1995), “The Exact Distribution of Moran’s I,” Environment and Planning A, 27, 985–999. DOI: 10.1068/a270985.
  • Townsend, A., and Moss, M. (2005), “Preparing Cities for Crisis Communications: Telecommunications Infrastructure in Disasters,” New York University Centre for Catastrophe Preparedness and Response.
  • Verbrugge, S., Pasqualini, S., Westphal, F.-J., Jäger, M., Iselt, A., Kirstädter, A., Chahine, R., Colle, D., Pickavet, M., and Demeester, P. (2005), “Modeling Operational Expenditures for Telecom Operators,” in Proceedings of Conference on Optical Network Design and Modeling, pp. 455–466.
  • Wang, H., Ding, J., Li, Y., Hui, P., Yuan, J., and Jin, D. (2015), “Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-Scale Cellular Data Networks,” in Proceedings of the 7th International Workshop on Hot Topics in Planet-scale Mobile Computing and Online Social Networking, pp. 19–24. DOI: 10.1145/2757513.2757518.
  • Wang, X., Zhou, Z., Xiao, F., Xing, K., Yang, Z., Liu, Y., and Peng, C. (2018), “Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis,” IEEE Transactions on Mobile Computing, 18, 2190–2202. DOI: 10.1109/TMC.2018.2870135.
  • Wendland, H. (1995), “Piecewise Polynomial, Positive Definite and Compactly Supported Radial Functions of Minimal Degree,” Advances in Computational Mathematics, 4, 389–396. DOI: 10.1007/BF02123482.
  • ——- (1998), “Error Estimates for Interpolation by Compactly Supported Radial Basis Functions of Minimal Degree,” Journal of Approximation Theory, 93, 258–272.
  • Yan, J., Cowles, M. K., Wang, S., and Armstrong, M. P. (2007), “Parallelizing MCMC for Bayesian Spatiotemporal Geostatistical Models,” Statistics and Computing, 17, 323–335. DOI: 10.1007/s11222-007-9022-2.
  • Yang, Q., Zhang, N., and Hong, Y. (2013), “Reliability Analysis of Repairable Systems with Dependent Component Failures Under Partially Perfect Repair,” IEEE Transactions on Reliability, 62, 490–498.
  • Yin, L., and Trivedi, K. S. (1999), “Confidence Interval Estimation of NHPP-based Software Reliability Models,” in Proceedings 10th International Symposium on Software Reliability Engineering, pp. 6–11.
  • Zeephongsekul, P., Jayasinghe, C. L., Fiondella, L., and Nagaraju, V. (2016), “Maximum-Likelihood Estimation of Parameters of NHPP Software Reliability Models Using Expectation Conditional Maximization Algorithm,” IEEE Transactions on Reliability, 65, 1571–1583. DOI: 10.1109/TR.2016.2570557.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.