A Novel Framework for Reliable Network Prediction of Small Scale Wireless Sensor Networks (SSWSNs)

• Autorzy: Sandhu J. K. , Verma A. K. , Rana P. S.

Identyfikatory

DOI

10.3233/FI-2018-1685

• Języki publikacji: EN

Abstrakty

EN

In Small Scale Wireless Sensor Networks (SSWSNs), reliability is defined as the capability of a network to perform its intended task under certain conditions for a stated time span. There are many tools for modeling and analyzing the reliability of a network. As the intricacy of various networks is increasing, there is a need for many sophisticated methods for reliability analysis. The term reliability is used as an umbrella term to capture various attributes such as safety, availability, security, and ease of use. The existing methods have many shortcomings which include inadequacy of a novel framework and inefficacy to handle scalable networks. This paper presents a novel framework which predicts the overall reliability of the SSWSNs in terms of performance metrics such as, sent packets, received packets, packets forfeit, packet delivery ratio and throughput. This framework includes various phases starting with scenario generation, construction of a dataset, applying ensemble based machine learning techniques to predict the parameters which cannot be calculated. The ensemble model predicts with an optimum accuracy of 99.9% for data flow, 99.9% for the protocol used and 97.6% for the number of nodes. Finally, to check the robustness of the ensemble model 10-fold cross-validation is used. The dataset used in this work is available as a supplement at http://bit.ly/SSWSN-Reliability.

Słowa kluczowe

EN

ensemble; machine learning; network prediction; reliability; small scale wireless sensor networks

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2018
• Tom: Vol. 160, nr 3
• Strony: 303--341
• Opis fizyczny: Bibliogr. 64 poz., rys., tab., wykr.

Twórcy

autor

Sandhu J. K.

• Research Scholar, CSED, Thapar Institute of Engineering and Technology, Patiala, India

autor

Verma A. K.

• Research Scholar, CSED, Thapar Institute of Engineering and Technology, Patiala, India

autor

Rana P. S.

• Research Scholar, CSED, Thapar Institute of Engineering and Technology, Patiala, India

Bibliografia

• [1] Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. Wireless sensor networks: a survey, Computer networks, 2002;38(4):393-422. doi:10.1016/S1389-1286(01)00302-4.
• [2] Al-Kuwaiti M, Kyriakopoulos N, Hussein S. A comparative analysis of network dependability, fault-tolerance, reliability, security, and survivability, Communications Surveys & Tutorials, IEEE, 2009;11(2):106-124. doi:10.1109/SURV.2009.090208.
• [3] Arlot S, and Celisse A. A survey of cross-validation procedures for model selection, Statistics surveys, 2010;4:40-79. doi:10.1214/09-SS054.
• [4] Baker AM, Hsu FC, Gayzik FS. A method to measure predictive ability of an injury risk curve using an observation-adjusted area under the receiver operating characteristic curve, Journal of Biomechanics, 2018. doi:10.1016/j.jbiomech.2018.02.018.
• [5] Bangash JI, Abdullah AH, Anisi MH, Khan AW. A survey of routing protocols in wireless body sensor networks, Sensors, 2014;14(1):1322-1357. doi:10.3390/s140101322.
• [6] Borra S, and Di Ciaccio A. Improving nonparametric regression methods by bagging and boosting, Computational Statistics & Data Analysis, 2002;38(4):407-420. doi: 10.1016/S0167-9473(01)00068-8.
• [7] Breiman L: Bagging predictors, Machine learning, 1996;24(2):123-140. doi:10.1023/A:1018054314350.
• [8] Bruneo D, Puliafito A, Scarpa M. Dependability analysis of wireless sensor networks with active-sleep cycles and redundant nodes, Proceedings of the First Workshop on Dynamic Aspects in Dependability Models for Fault-Tolerant Systems, ACM, 2010; pp. 25-30. doi:10.1145/1772630.1772637.
• [9] Burden FR, and Winkler DA. Robust QSAR models using Bayesian regularized neural networks, Journal of medicinal chemistry, 1999;42(16):3183-3187. doi:10.1021/jm980697n.
• [10] Candanedo LM, Feldheim V, Deramaix D. Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, 2017;140:81-97. doi:10.1016/j.enbuild.2017.01.083.
• [11] Cardellini V, Casalicchio E, Grassi V, Presti FL, Mirandola R. Towards self-adaptation for dependable service-oriented systems, in: Architecting Dependable Systems VI, Springer, 2009; pp. 24-48. doi:10.1007/978-3-642-10248-6_2.
• [12] Chang YR, Huang CY, Kuo SY. Performance assessment and reliability analysis of dependable and distributed computing systems based on BDD and recursive merge, Applied Mathematics and Computation, 2010; 2171:403-413. doi:10.1016/j.amc.2010.05.075.
• [13] Chen J, Diaz M, Llopis L, Rubio B, Troya JM. A survey on quality of service support in wireless sensor and actor networks: Requirements and challenges in the context of critical infrastructure protection, Journal of Network and Computer Applications, 2011;34(4):1225-1239. doi:10.1016/j.jnca.2011.01.008.
• [14] Cheng K, Lu Z, Wei Y, Shi Y, Zhou Y. Mixed kernel function support vector regression for global sensitivity analysis, Mechanical Systems and Signal Processing, 2017;96:201-214. doi:10.1016/j.ymssp.2017.04.014.
• [15] Cholette ME, Borghesani P, Di Gialleonardo E, Braghin F. Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications, Expert Systems with Applications, 2017;81:39-52. doi:10.1016/j.eswa.2017.03.050.
• [16] Bui EN, Henderson BL, Viergever K. Knowledge discovery from models of soil properties developed through data mining, Ecological Modelling, 2006;1913-4:431-446. doi:10.1016/j.ecolmodel.2005.05.021.
• [17] Dong M, Ota K, Liu A, Guo M. Joint Optimization of Lifetime and Transport Delay under Reliability Constraint Wireless Sensor Networks, IEEE Transactions on Parallel and Distributed Systems, 2016;27(1):225-236. doi:10.1109/TPDS.2015.2388482.
• [18] Elghazel W, Bahi J, Guyeux C, Hakem M, Medjaher K, Zerhouni N. Dependability of wireless sensor networks for industrial prognostics and health management, Computers in Industry, 2015;68:1-15. doi:10.1016/j.compind.2014.10.004.
• [19] Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L. Weka: A machine learning workbench for data mining, in: Data Mining and Knowledge Discovery Handbook, 2005;1305-1314. doi:10.1007/0-387-25465-X_62.
• [20] Grubinger T, Zeileis A, Pfeiffer KP. evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R, Technical report, Working Papers in Economics and Statistics, 2011. URL https://cran.r-project.org/web/packages/evtree/index.html.
• [21] Hagenauer J, and Helbich M. A comparative study of machine learning classifiers for modeling travel mode choice, Expert Systems with Applications, 2017;78:273-282. doi:10.1016/j.eswa.2017.01.057.
• [22] Heung B, Hodul M, Schmidt MG. Comparing the use of training data derived from legacy soil pits and soil survey polygons for mapping soil classes, Geoderma, 2017;290:51-68. doi:10.1016/j.geoderma.2016.12.001.
• [23] Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework, Journal of Computational and Graphical statistics, 2006;15(3):651-674. doi:10.1198/106186006X133933.
• [24] Hu K, Rahman A, Bhrugubanda H, Sivaraman V. HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors, IEEE Sensors Journal, 2107;17(11):3517-3525. doi:10.1109/JSEN.2017.2690975.
• [25] Issariyakul T, and Hossain E. Introduction to network simulator NS2, Springer: Signals & Communication, 2012. doi:10.1007/978-1-4614-1406-3.
• [26] Kafi MA, Othman JB, Badache N. A Survey on Reliability Protocols in Wireless Sensor Networks, ACM Computing Surveys (CSUR), 2017;50(2):1-47. doi:10.1145/3064004.
• [27] Karim ME, Petkau J, Gustafson P, Tremlett H, Group TBS. On the application of statistical learning approaches to construct inverse probability weights in marginal structural cox models: Hedging against weight-model misspecification, Communications in Statistics-Simulation and Computation, 2017;46(10):7668-7697. doi:10.1080/03610918.2016.1248574.
• [28] Keerthi SS, and Gilbert EG. Convergence of a generalized SMO algorithm for SVM classifier design, Machine Learning, 2002;46(1-3):351-360. doi:10.1023/A:1012431217818.
• [29] Larranaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armananzas R, Santafe G, Robles APV. Machine learning in bioinformatics, Briefings in bioinformatics, 2006;7(1):86-112. doi:10.1093/bib/bbk007.
• [30] Liaw A, and Wiener M. Classification and Regression by random Forest, R News, 2002;2(3):18-22. URL http://CRAN.R-project.org/doc/Rnews/.
• [31] Limbourg P. Dependability modelling under uncertainty, Springer-Theoretical Computer Science, 2008. doi:10.1007/978-3-540-69287-4.
• [32] Lin HY, Chen YA, Tsai YY, Qu X, Tseng TS, Park JY. TRM: A Powerful Two-Stage Machine Learning Approach for Identifying SNP-SNP Interactions, Annals of human genetics, 2012; 76(1):53-62. doi:10.1111/j.1469-1809.2011.00692.x.
• [33] Lloret J, Shu L, Lacuesta R, Chen M. User-Oriented and Service-Oriented Spontaneous Ad Hoc and Sensor Wireless Networks, Adhoc & Sensor Wireless Networks, 2012;141-2:1-8. URL http://hdl.handle.net/10251/29897.
• [34] Lopez-Benitez N. Dependability analysis of distributed computing systems using stochastic Petri nets, IEEE-Symposium on Reliable Distributed Systems, 1992. doi:10.1109/RELDIS.1992.235139.
• [35] Mahmood MA, Seah WKG, Welch I. Reliability in wireless sensor networks: A survey and challenges ahead, Computer Networks, 2015; 79:166-187. doi:10.1016/j.comnet.2014.12.016.
• [36] Malhotra M, and Trivedi KS. Dependability modeling using Petri-nets, IEEE Transactions on Reliability, 1995;44(3):428-440. doi:10.1109/24.406578.
• [37] Mas JF. Receiver Operating Characteristic (ROC) Analysis, in: Geomatic Approaches for Modeling Land Change Scenarios, 2018 pp. 465-467. doi:10.1007/978-3-319-60801-3_30.
• [38] Mbowe JE, and Oreku GS. Quality of service in wireless sensor networks, Wireless Sensor Network, 2014;62:19-26. doi:10.4236/wsn.2014.62003.
• [39] Munir A, Antoon J, Gordon-Ross A. Modeling and Analysis of Fault Detection and Fault Tolerance in Wireless Sensor Networks, ACM Transactions on Embedded Computing Systems (TECS), 2015;14(1):1-43. doi:10.1145/2680538.
• [40] Platis A, Limnios N, Le-Dua M. Dependability analysis of systems modeled by non-homogeneous Markov chains, Reliability Engineering & System Safety, 1998;61(3):235-249. doi:10.1016/S0951-8320(97)00073-2.
• [41] Pocas I, Goncalves J, Costa PM, Goncalves I, Pereira LS, Cunha M. Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region, International Journal of Applied Earth Observation and Geoinformation, 2017;58:177-190. doi:10.1016/j.jag.2017.02.013.
• [42] Rathore SS, and Kumar S. Towards an ensemble based system for predicting the number of software faults, Expert Systems with Applications, 2017;82:357-382. doi:10.1016/j.eswa.2017.04.014.
• [43] Rezaee AA, and Golparvar SE. Conditional Inference Tree Modelling of Competing Motivators of the Positioning of Concessive Clauses: The Case of a Non-native Corpus, Journal of Quantitative Linguistics, 2017;242-3:89-106. doi:10.1080/09296174.2016.1265799.
• [44] Riedmiller M, and Braun H. A direct adaptive method for faster backpropagation learning: The RPROP algorithm, IEEE International Conference on Neural Networks, 1993. doi:10.1109/ICNN.1993.298623.
• [45] Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC bioinformatics, 2011; 12:1-8. doi:10.1186/1471-2105-12-77.
• [46] Rosset V, Paulo MA, Cespedes JG, Nascimento MCV. Enhancing the reliability on data delivery and energy efficiency by combining swarm intelligence and community detection in large-scale WSNs, Expert Systems with Applications, 2017;78:89-102. doi:10.1016/j.eswa.2017.02.008.
• [47] Rzeszotko J, and Nguyen SH. Machine learning for traffic prediction, Fundamenta Informaticae, 2012;119(3-4):407-420. doi:10.3233/FI-2012-745.
• [48] Sarkar A, and Murugan TS. Routing protocols for wireless sensor networks: What the literature says?, Alexandria Engineering Journal, 2016;55(4):3173-3183. doi:10.1016/j.aej.2016.08.003.
• [49] Silva I, Guedes LA, Portugal P, Vasques F. Reliability and availability evaluation of wireless sensor networks for industrial applications, Sensors, 2012; 12(1):806-838. doi:10.3390/s120100806.
• [50] Smusz S, Kurczab R, Bojarski AJ. A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds, Chemometrics and Intelligent Laboratory Systems, 2013;128:89-100. doi:10.1016/j.chemolab.2013.08.003.
• [51] Swain RR, and Khilar PM. Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks, Wireless Personal Communications, 2017;95(3):2507-2548. doi:10.1007/s11277-016-3931-3.
• [52] Tian X, Liu J, Wang X. Streamline architecture of network simulator to facilitate teaching of computer networking, Proceedings of the ACM Turing 50th Celebration Conference-China, 2017. doi:10.1145/3063955.3063957.
• [53] Ticknor JL. A Bayesian regularized artificial neural network for stock market forecasting, Expert Systems with Applications, 2013;40(14):5501-5506. doi:10.1016/j.eswa.2013.04.013.
• [54] Trivedi KS. Probability and Statistics with Reliability, Queuing and Computer Science Applications, Wiley Quality and Reliability, 2016. doi:10.1002/9781119285441.
• [55] Turgeman L, May JH, Sciulli R. Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission, Expert Systems with Applications, 2017;78:376-385. doi:10.1016/j.eswa.2017.02.023.
• [56] Weng B, Ahmed MA, Megahed FM. Stock market one-day ahead movement prediction using disparate data sources, Expert Systems with Applications, 2017;79:153-163. doi:10.1016/j.eswa.2017.02.041.
• [57] Weng CE, and Lai TW. An energy-efficient routing algorithm based on relative identification and direction for wireless sensor networks, Wireless personal communications, 2013; 69(1):253-268. doi:10.1007/s11277-012-0571-0.
• [58] Xu L, Ziv H, Alspaugh TA, Richardson DJ. An architectural pattern for non-functional dependability requirements, Journal of Systems and Software, 2006;79(10):1370-1378. doi:10.1016/j.jss.2006.02.061.
• [59] Xu X, Liang W, Wark T. Data quality maximization in sensor networks with a mobile sink, IEEE International Conference on Distributed Computing in Sensor Systems, 2011. doi:10.1109/DCOSS.2011.5982160.
• [60] Yang JJ, and Zuo B. Performance of Smart Sensor Detectors for Stop-Bar Detection at Signalized Intersections, Journal of Transportation Engineering, Part A: Systems, 2017;143(6):1-9. doi:10.1061/JTEPBS.0000045.
• [61] Yang L, Liu S, Tsoka S, Papageorgiou LG. A regression tree approach using mathematical programming, Expert Systems with Applications, 2017;78:347-357. doi:10.1016/j.eswa.2017.02.013.
• [62] Yao CC, Yu PT, Hung RW. Extractive support vector algorithm on support vector machines for image restoration, Fundamenta Informaticae, 2009;90(1-2):171-190. doi:10.3233/FI-2009-0012.
• [63] Yu FR, Sun B, Krishnamurthy V, Ali S. Application layer QoS optimization for multimedia transmission over cognitive radio networks, Wireless Networks, 2011;17(2):371-383. doi:10.1007/s11276-010-0285-8.
• [64] Zonouz AE, Xing L, Vokkarane VM, Sun Y. Hybrid wireless sensor networks: a reliability, cost and energy-aware approach, IET Wireless Sensor Systems, 2016;6(2):42-48. doi:10.1049/iet-wss.2014.0131.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).