MIS-IoT: Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning

Internet of Things world is getting bigger everyday with new developments in all fronts. The new IoT world requires better handling of big data and better usage with more intelligence integrated in all phases. Here we present MIS-IoT (Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning) framework, which is "modular" and therefore open for new extensions, "intelligent" by providing machine learning and deep learning methods on "big data" coming from IoT objects, "server-based" in a service-oriented way by offering services via standart Web protocols. We present an overview of the design and implementation details of MIS-IoT along with a case study evaluation of the system, showing the intelligence capabilities in anomaly detection over real-time weather data.

[1]  Arkady B. Zaslavsky,et al.  Semantic-Driven Configuration of Internet of Things Middleware , 2013, 2013 Ninth International Conference on Semantics, Knowledge and Grids.

[2]  Erdogan Dogdu,et al.  An extended IoT framework with semantics, big data, and analytics , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[3]  Lovekesh Vig,et al.  Anomaly detection in ECG time signals via deep long short-term memory networks , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[4]  Jeyhun Karimov,et al.  k-Means Performance Improvements with Centroid Calculation Heuristics Both for Serial and Parallel Environments , 2015, 2015 IEEE International Congress on Big Data.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Sasu Tarkoma,et al.  A gap analysis of Internet-of-Things platforms , 2015, Comput. Commun..

[7]  A. Murat Ozbayoglu,et al.  Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach , 2018, Appl. Soft Comput..

[8]  Erdogan Dogdu,et al.  Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[9]  Mahdi Ben Alaya,et al.  Toward semantic interoperability in oneM2M architecture , 2015, IEEE Communications Magazine.

[10]  Lovekesh Vig,et al.  Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.

[11]  Nathalie Japkowicz,et al.  Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[12]  Joerg Swetina,et al.  Toward a standardized common M2M service layer platform: Introduction to oneM2M , 2014, IEEE Wireless Communications.

[13]  Robert J. Meijer,et al.  Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[14]  Erdogan Dogdu,et al.  Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey , 2018, IEEE Internet of Things Journal.

[15]  Chi Harold Liu,et al.  The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey , 2015, IEEE Transactions on Emerging Topics in Computing.

[16]  Jeyhun Karimov,et al.  High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[17]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[18]  Jeyhun Karimov,et al.  Clustering Quality Improvement of k-means Using a Hybrid Evolutionary Model , 2015, Complex Adaptive Systems.

[19]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[20]  Min Chen,et al.  A Survey on Internet of Things From Industrial Market Perspective , 2015, IEEE Access.