PREDICTING AND CLASSIFYING PACKET TRANSMISSION EFFICIENCY IN BIO-INSPIRED WIRELESS SENSOR NETWORKS

PREDICTING AND CLASSIFYING PACKET TRANSMISSION EFFICIENCY IN BIOINSPIRED WIRELESS SENSOR NETWORKS By AHMAD ZUHAIR ALKAZZAZ, M.S. A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Engineering at Virginia Commonwealth University. Virginia Commonwealth University, 2014 Director: ROSALYN HOBSON HARGRAVES Associate Professor, Electrical and Computer Engineering Biological networks (specifically genetic regulatory networks) are known to be robust to various external perturbations. Bio-inspired wireless sensor networks (WSN) are known to be smart communication structures and have a have high packet transmission efficiency. In earlier work neural network models that correlate the average packet receival rates to the five topological features of the bio-inspired WSN were investigated. These features include the degree index, sink coverage, network density, hub node density, and motif index. In this thesis, an appropriate classification algorithm that works with these five features is investigated. The random forest algorithm is the best classification algorithm compared to other classification methods (APPENDIX B). In addition, a local weighted linear regression algorithm was created to predict the robustness of the network utilizing these five topological features.

[1]  Trevor Hastie,et al.  Additive Logistic Regression : a Statistical , 1998 .

[2]  Miguel Rio,et al.  Internet Traffic Forecasting using Neural Networks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[3]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[4]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[5]  Sajal K. Das,et al.  Principles of genomic robustness inspire fault-tolerant WSN topologies: A network science based case study , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[6]  Dina S. Deif,et al.  Classification of Wireless Sensor Networks Deployment Techniques , 2014, IEEE Communications Surveys & Tutorials.

[7]  G. Fagiolo.  Clustering in complex directed networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Hongjun Dai,et al.  A Multivariate Classification Algorithm for Malicious Node Detection in Large-Scale WSNs , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[9]  Johannes Fürnkranz,et al.  An Evaluation of Grading Classifiers , 2001, IDA.

[10]  patil Mrs.,et al.  Study of Wireless Sensor Network in SCADA System for Power Plant , 2012 .

[11]  Leo Breiman,et al.  Prediction Games and Arcing Algorithms , 1999, Neural Computation.

[12]  Haijia Shi.  Best-first Decision Tree Learning , 2007 .

[13]  S. Salzberg,et al.  INSTANCE-BASED LEARNING : Nearest Neighbour with Generalisation , 1995 .

[14]  S. Mangan,et al.  Structure and function of the feed-forward loop network motif , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Bernhard Pfahringer,et al.  Locally Weighted Naive Bayes , 2002, UAI.

[16]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[17]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[18]  Guy Theraulaz,et al.  Bio-Inspired Models of Network, Information, and Computing Systems , 2012, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

[19]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[20]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[21]  Stefan Kramer,et al.  Ensembles of nested dichotomies for multi-class problems , 2004, ICML.

[22]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[23]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[24]  Masayuki Murata,et al.  Toward bio-inspired network robustness - Step 1. Modularity , 2007, 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems.

[25]  Hiroaki Kitano,et al.  Biological robustness , 2008, Nature Reviews Genetics.

[26]  H. Altay Güvenir,et al.  Classification by Voting Feature Intervals , 1997, ECML.

[27]  Dario Floreano,et al.  GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..

[28]  L. Breiman.  OUT-OF-BAG ESTIMATION , 1996 .

[29]  G. Rutka,et al.  Neural Network Models for Internet Traffic Prediction , 2006 .

[30]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[31]  David W. Aha,et al.  Instance-Based Learning Algorithms , 1991, Machine Learning.

[32]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[34]  Ron Kohavi,et al.  Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid , 1996, KDD.

[35]  M. El-Aaasser,et al.  Energy aware classification for wireless sensor networks routing , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).

[36]  Robert Tibshirani,et al.  Bias, Variance and Prediction Error for Classification Rules , 1996 .

[37]  Jian Li,et al.  Analytical modeling and mitigation techniques for the energy hole problem in sensor networks , 2007, Pervasive Mob. Comput..

[38]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[39]  César Ducruet,et al.  Scale-free and small-world networks in geographical research: A critical examination , 2011 .

[40]  Ron Kohavi,et al.  Wrappers for performance enhancement and oblivious decision graphs , 1995 .

[41]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[42]  Jan M. Rabaey,et al.  Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks , 2002, USENIX Annual Technical Conference, General Track.

[43]  Feng Wang,et al.  Network Traffic Prediction Based on Grey Neural Network Integrated Model , 2008, 2008 International Conference on Computer Science and Software Engineering.

[44]  J. Baras,et al.  Motif-based Topology Design for Efficient Performance by Networks of Mobile Autonomous Vehicles , 2011 .

[45]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[46]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[47]  Asad Mohsin,et al.  Hamilton, New Zealand , 2008 .

[48]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[49]  Raymond J. Mooney,et al.  Constructing Diverse Classifier Ensembles using Artificial Training Examples , 2003, IJCAI.

[50]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[51]  Geoffrey I. Webb,et al.  MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.

[52]  H. Kitano.  Towards a theory of biological robustness , 2007, Molecular systems biology.

[53]  Geoffrey I. Webb,et al.  Lazy Learning of Bayesian Rules , 2000, Machine Learning.

[54]  Ian H. Witten,et al.  Stacking Bagged and Dagged Models , 1997, ICML.

[55]  Marco Aiello,et al.  A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Network , 2012, 2012 IEEE 15th International Conference on Computational Science and Engineering.

[56]  Eibe Frank,et al.  Logistic Model Trees , 2003, ECML.

[57]  Liang Ding,et al.  Collaborative Peer-to-Peer Training and Target Classification in Wireless Sensor Networks , 2007, Future Generation Communication and Networking (FGCN 2007).

[58]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[59]  Alexander K. Seewald,et al.  How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness , 2002, International Conference on Machine Learning.

[60]  Nelson Piedra,et al.  STUDY OF THE APPLICATION OF NEURAL NETWORKS IN INTERNET TRAFFIC ENGINEERING , 2008 .

[61]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[62]  David H. Wolpert,et al.  An Efficient Method To Estimate Bagging's Generalization Error , 1999, Machine Learning.

[63]  M. Bikdash,et al.  Measuring nodal contribution to global network robustness , 2011, 2011 Proceedings of IEEE Southeastcon.

[64]  Cohen,et al.  Resilience of the internet to random breakdowns , 2000, Physical review letters.

[65]  Sajal K. Das,et al.  Empirical prediction of packet transmission efficiency in bio-inspired Wireless Sensor Networks , 2012, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).

[66]  Geoff Holmes,et al.  Multiclass Alternating Decision Trees , 2002, ECML.

[67]  S. Pongor,et al.  Multiple weak hits confuse complex systems: a transcriptional regulatory network as an example. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[68]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[69]  Cong Wang,et al.  An Internet Traffic Forecasting Model Adopting Radical Based on Function Neural Network Optimized by Genetic Algorithm , 2008, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008).

[70]  Yong Wang,et al.  Using Model Trees for Classification , 1998, Machine Learning.

[71]  Wen-Hui Chen,et al.  Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units , 2012 .

[72]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[73]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[74]  Richard M. Murray,et al.  Networks with the Smallest Average Distance and the Largest Average Clustering , 2010, 1007.4031.