Fog-enabled Event Processing Based on IoT Resource Models

Complex Event Processing (CEP) systems extract interest situations from event flows based on event detection patterns. However, local event processing for distributed Internet of Things (IoT) has not been discussed yet. Besides, it is complex or impossible to discover such patterns in some applications of IoT. In this article, we design a complex event service to process event flows based on IoT resource models, which does not depend on existing patterns, and deals with both discrete events and continuous variables. To improve the CEP performance, local IoT resources are used for local event processing, and a lazy exchange method is designed to realize the collaborated event processing between network edges and a data center. Our evaluation shows that our solution is feasible and effective.

[1]  R. Baker Kearfott,et al.  Introduction to Interval Analysis , 2009 .

[2]  Maria Paola Bonacina,et al.  PSATO: a Distributed Propositional Prover and its Application to Quasigroup Problems , 1996, J. Symb. Comput..

[3]  Min Zhou,et al.  Parallelizing SMT solving: Lazy decomposition and conciliation , 2018, Artif. Intell..

[4]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[5]  Hilary Putnam,et al.  A Computing Procedure for Quantification Theory , 1960, JACM.

[6]  Giordano Tamburrelli,et al.  Learning from the past: automated rule generation for complex event processing , 2014, DEBS '14.

[7]  Johannes Gehrke,et al.  Cayuga: A General Purpose Event Monitoring System , 2007, CIDR.

[8]  Opher Etzion,et al.  Event Processing in Action , 2010 .

[9]  Rik Eshuis,et al.  Flexible Construction of Executable Service Compositions from Reusable Semantic Knowledge , 2016, TWEB.

[10]  Nikil D. Dutt,et al.  HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT , 2017, ACM Trans. Embed. Comput. Syst..

[11]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[12]  Edmund M. Clarke,et al.  δ-Complete Decision Procedures for Satisfiability over the Reals , 2012, IJCAR.

[13]  Heiner Stuckenschmidt,et al.  Handbook on Ontologies , 2004, Künstliche Intell..

[14]  Sheila A. McIlraith,et al.  Partition-based logical reasoning for first-order and propositional theories , 2005, Artif. Intell..

[15]  Matteo Pradella,et al.  Efficient analysis of event processing applications , 2015, DEBS.

[16]  Robert E. Shostak,et al.  Deciding Combinations of Theories , 1982, JACM.

[17]  Ewald Speckenmeyer,et al.  A fast parallel SAT-solver — efficient workload balancing , 2005, Annals of Mathematics and Artificial Intelligence.

[18]  Greg Nelson,et al.  Simplification by Cooperating Decision Procedures , 1979, TOPL.

[19]  N. Immerman,et al.  Optimizing Expensive Queries in Complex Event Processing , 2014 .

[20]  M. Tamer Özsu,et al.  Diversified Stress Testing of RDF Data Management Systems , 2014, SEMWEB.

[21]  M.B.A. Prashan Madumal,et al.  Adaptive event tree-based hybrid CEP computational model for Fog computing architecture , 2016, 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer).

[22]  Avigdor Gal,et al.  A Model for Reasoning with Uncertain Rules in Event Composition Systems , 2005, UAI.

[23]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[24]  Jai E. Jung,et al.  Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing , 2017, Future Gener. Comput. Syst..

[25]  Yang Zhang,et al.  Declarative Construction of Distributed Event-driven IoT Services Based on IoT Resource Models , 2017 .

[26]  L. Wos,et al.  Paramodulation and Theorem-Proving in First-Order Theories with Equality , 1983 .

[27]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[28]  Daniel Kroening,et al.  Deciding a Combination of Theories , 2008 .

[29]  Donald W. Loveland,et al.  A machine program for theorem-proving , 2011, CACM.

[30]  Cesare Tinelli,et al.  Satisfiability Modulo Theories , 2021, Handbook of Satisfiability.

[31]  Martin Hirzel,et al.  Partition and compose: parallel complex event processing , 2012, DEBS.

[32]  Natasha Sharygina,et al.  Search-Space Partitioning for Parallelizing SMT Solvers , 2015, SAT.

[33]  Avigdor Gal,et al.  Efficient Processing of Uncertain Events in Rule-Based Systems , 2012, IEEE Transactions on Knowledge and Data Engineering.

[34]  Pierre Hansen,et al.  A reliable affine relaxation method for global optimization , 2010, 4OR.

[35]  Minos N. Garofalakis,et al.  Issues in complex event processing: Status and prospects in the Big Data era , 2017, J. Syst. Softw..

[36]  Samuel Madden,et al.  ZStream: a cost-based query processor for adaptively detecting composite events , 2009, SIGMOD Conference.

[37]  Heiner Stuckenschmidt,et al.  Structure-Based Partitioning of Large Ontologies , 2009, Modular Ontologies.

[38]  Jan Cernocký,et al.  Probabilistic and Bottle-Neck Features for LVCSR of Meetings , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[39]  Ugur Çetintemel,et al.  Plan-based complex event detection across distributed sources , 2008, Proc. VLDB Endow..

[40]  Song Guo,et al.  Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System , 2016, IEEE Transactions on Computers.

[41]  Peng Peng,et al.  Processing SPARQL queries over distributed RDF graphs , 2014, The VLDB Journal.

[42]  Cesare Tinelli,et al.  Satisfiability Modulo Theories , 2018, Handbook of Model Checking.

[43]  Nesime Tatbul,et al.  RIP: run-based intra-query parallelism for scalable complex event processing , 2013, DEBS.

[44]  Edmund M. Clarke,et al.  dReal: An SMT Solver for Nonlinear Theories over the Reals , 2013, CADE.

[45]  Neil Immerman,et al.  Efficient pattern matching over event streams , 2008, SIGMOD Conference.

[46]  Paulo Marques,et al.  A Performance Study of Event Processing Systems , 2009, TPCTC.

[47]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[48]  Avigdor Gal,et al.  Complex event processing over uncertain data , 2008, DEBS.

[49]  Thomas Lukasiewicz,et al.  Probabilistic Logic Programming , 1998, ECAI.

[50]  Beat Wüthrich.  Probabilistic Knowledge Bases , 1995, IEEE Trans. Knowl. Data Eng..

[51]  Peter R. Pietzuch,et al.  Distributed complex event processing with query rewriting , 2009, DEBS '09.

[52]  Manolis Koubarakis,et al.  Modeling and Querying Metadata in the Semantic Sensor Web: The Model stRDF and the Query Language stSPARQL , 2010, ESWC.

[53]  Gilles Audemard,et al.  Lazy Clause Exchange Policy for Parallel SAT Solvers , 2014, SAT.

[54]  Xiaoming Zhang,et al.  Complex Event Processing over distributed probabilistic event streams , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[55]  Alan Sill,et al.  Standards at the Edge of the Cloud , 2017, IEEE Cloud Computing.