An IOHMM-Based Framework to Investigate Drift in Effectiveness of IoT-Based Systems

IoT-based systems, when interacting with the physical environment through actuators, are complex systems difficult to model. Formal verification techniques carried out at design-time being often ineffective in this context, these systems have to be quantitatively evaluated for effectiveness at run-time, i.e., for the extent to which they behave as expected. This evaluation is achieved by confronting a model of the effects they should legitimately produce in different contexts to those observed in the field. However, this quantitative evaluation is not informative on the drifts in effectiveness, it does not help designers investigate their possible causes, increasing the time needed to resolve them. To address this problem, and assuming that models of legitimate behavior can be described by means of Input-Output Hidden Markov Models (IOHMMs), a novel generic unsupervised clustering-based IOHMM structure and parameters learning algorithm is developed. This algorithm is first used to learn a model of legitimate behavior. Then, a model of the observed behavior is learned from observations gathered in the field. A second algorithm builds a dissimilarity graph that makes clear structural and parametric differences between both models, thus providing guidance to designers to help them investigate possible causes of drift in effectiveness. The approach is validated on a real world dataset collected in a smart home.

[1]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Alexander Schliep.  A Bayesian Approach to Learning Hidden Markov Model Topology with Applications to Biological Sequence Analysis , 2001 .

[3]  K. Wiesner,et al.  What is a complex system? , 2012, European Journal for Philosophy of Science.

[4]  Hani Hagras,et al.  Toward Human-Understandable, Explainable AI , 2018, Computer.

[5]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[6]  Gonçalo Marques,et al.  Ambient Assisted Living and Internet of Things , 2019, Harnessing the Internet of Everything (IoE) for Accelerated Innovation Opportunities.

[7]  Ali Hassan Sodhro,et al.  An adaptive QoS computation for medical data processing in intelligent healthcare applications , 2019, Neural Computing and Applications.

[8]  Muhammad Fahim,et al.  Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review , 2019, IEEE Access.

[9]  Jia Li,et al.  Aggregated Wasserstein Distance and State Registration for Hidden Markov Models , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Junmin Wang,et al.  A Stochastic Driver Pedal Behavior Model Incorporating Road Information , 2017, IEEE Transactions on Human-Machine Systems.

[11]  Yoshua Bengio,et al.  An Input Output HMM Architecture , 1994, NIPS.

[12]  Thomas Brendan Murphy,et al.  Computational aspects of fitting mixture models via the expectation-maximization algorithm , 2012, Comput. Stat. Data Anal..

[13]  Christophe Simon,et al.  Estimating IOHMM Parameters to Compute Remaining Useful Life of System , 2019 .

[14]  Adnan Al-Anbuky,et al.  Editorial: Smart Cyber-Physical Systems: Toward Pervasive Intelligence systems , 2020, Future Gener. Comput. Syst..

[15]  Hazem Nounou,et al.  Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems , 2020 .

[16]  Yuan Di,et al.  Enhanced System Health Assessment using Adaptive Self-Learning Techniques , 2018 .

[17]  Padhraic Smyth,et al.  Markov monitoring with unknown states , 1994, IEEE J. Sel. Areas Commun..

[18]  Jean-Yves Tigli,et al.  Probabilistic Models Toward Controlling Smart-* Environments , 2017, IEEE Access.

[19]  Imre Horváth,et al.  Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems , 2020, Sensors.

[20]  Barbara Hammer,et al.  Incremental learning algorithms and applications , 2016, ESANN.

[21]  Charles Nicholas,et al.  Cluster Quality Analysis Using Silhouette Score , 2020, 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA).

[22]  Seungchul Lee,et al.  Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model , 2010 .

[23]  Shin Nakajima,et al.  Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model , 2015, Simul..

[24]  Philippe Weber,et al.  Fault diagnosis and prognosis by using Input-Output Hidden Markov Models applied to a diesel generator , 2017, 2017 25th Mediterranean Conference on Control and Automation (MED).

[25]  Alessandro Farinelli,et al.  HMMs for Anomaly Detection in Autonomous Robots , 2020, AAMAS.

[26]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[27]  Nhan Le Thanh,et al.  Overview and Challenges of Ambient Systems, Towards a Constructivist Approach to their Modelling , 2020, ArXiv.

[28]  Jean-Marie Bonnin,et al.  Quality of Experience Measurements for Video Streaming over Wireless Networks , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[29]  Joelle Pineau,et al.  PAC-Learning of Markov Models with Hidden State , 2006, ECML.

[30]  Gerardo Rubino,et al.  Quantifying the Quality of Audio and Video Transmissions over the Internet: The PSQA Approach , 2006 .

[31]  Gérald Rocher,et al.  A Possibilistic I/O Hidden Semi-Markov Model For Assessing Cyber-Physical Systems Effectiveness , 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[32]  Tian Zhang,et al.  BIRCH: A New Data Clustering Algorithm and Its Applications , 1997, Data Mining and Knowledge Discovery.

[33]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[34]  Douglas A. Reynolds.  Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.

[35]  Stéphane Lavirotte,et al.  Effectiveness Assessment of Cyber-Physical Systems , 2019, Int. J. Approx. Reason..

[36]  Richard Nock,et al.  Disentangled behavioural representations , 2019, NeurIPS.

[37]  Yoshua Bengio,et al.  Input-output HMMs for sequence processing , 1996, IEEE Trans. Neural Networks.

[38]  Nir Friedman,et al.  Learning Belief Networks in the Presence of Missing Values and Hidden Variables , 1997, ICML.

[39]  Plamen Angelov,et al.  Anomaly detection based on eccentricity analysis , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).

[40]  Christian Laugier,et al.  Incremental Learning of Statistical Motion Patterns With Growing Hidden Markov Models , 2007, IEEE Transactions on Intelligent Transportation Systems.

[41]  Lin Ma,et al.  Model-based fault diagnosis and prognosis of dynamic systems: a review , 2019 .

[42]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[43]  Thierry Artières,et al.  Learning Model Structure from Data: an Application to on-Line Handwriting , 2009, Progress in Computer Vision and Image Analysis.

[44]  Philippe Weber,et al.  Benefits of Bayesian Network Models , 2016 .

[45]  Jianbo Yu,et al.  Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring , 2017 .

[46]  André Platzer,et al.  Cyber-Physical Systems: Overview , 2018 .

[47]  Matteo Dell'Amico,et al.  FISHDBC: Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance , 2019, ArXiv.

[48]  Plamen P. Angelov,et al.  An evolving approach to unsupervised and Real-Time fault detection in industrial processes , 2016, Expert Syst. Appl..

[49]  Tucker R. Balch,et al.  Learning executable agent behaviors from observation , 2006, AAMAS '06.

[50]  Helge J. Ritter,et al.  An instantaneous topological mapping model for correlated stimuli , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[51]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[52]  Ricardo J. G. B. Campello,et al.  Density-Based Clustering Based on Hierarchical Density Estimates , 2013, PAKDD.

[53]  Simin Nadjm-Tehrani,et al.  Adaptive real-time anomaly detection with incremental clustering , 2007, Inf. Secur. Tech. Rep..

[54]  Hava T. Siegelmann,et al.  On the Computational Power of Neural Nets , 1995, J. Comput. Syst. Sci..

[55]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .