Analytics in IOT
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The document provides an overview of analytics in the Internet of Things (IoT) space. It defines key concepts like IoT, Industrial IoT (IIoT), and Analytics of Things (AoT). It discusses how IoT analytics is different and provides case studies on preventive maintenance, freezer failure detection, and analytics for a solar PV plant. It highlights the importance of data science for analyzing the huge volumes of data generated by IoT devices and the analytics techniques used, including performance analytics, trend analysis, and machine learning algorithms.
Analytics in IOT
- 1. ANALYTICS IN IOT SPACE By :- • Mitesh Gupta
- 2. AGENDA What is IoT? What is IIoT What is AoT How IoT Analytics is Different? Case Studies Data Science (necessity and importance) Analytics Way Forward Technology Stack for Analytics Questions & Answers
- 3. WHAT IS…
- 4. BASIC DEFINITION • Forbes : This term refers to devices that collect & transmit data via the internet. • SAP : More than 50 billions objects will connect to internet and this connection is called as IoT. This “things” talk to each other, collect streaming data and insights. • Cisco : The IoT links smart objects to the Internet. It can enable an exchange of data never available before, and bring users information in a more secure way. • Wiki : The Internet of Things (IoT) is the network of physical objects/devices that enables these objects to collect and exchange data.
- 5. THINGS IN INTERNET OF THINGS • Objects • Machines • Appliances • Building • Vehicle • People and many more…
- 6. WHAT IS INTERNET OF THINGS! 6 Application Server Things Web Service s
- 7. IOT ECOSYSTEM Remot e Internet Network Data Storage Analytics IoT Devices Analysis Command Gateway
- 8. M2M • Machine to machine refers to direct communication between devices using any communication channel, including wired and wireless. • Machine to machine communication can include industrial instrumentation, enabling a sensor or meter to communicate the data it records.
- 9. M2M VS IOT
- 10. IMPORTANCE • Connect with things • Monitoring of things • Search for things • Manage things • Control things
- 11. GARTNER: EMERGING TECHNOLOGY HYPE CYCLE
- 12. MARKET DRIVERS & BARRIERS Four Market Drivers •Expanded internet connectivity •High mobile adoption •Low-cost sensors •Large IoT investments Four Barriers •Security concerns •Privacy concerns •Implementation problems •Technological fragmentation
- 13. SIZE IN MARKET $6 TRILLION INVESTED $6 trillion will be invested on IoT solutions over the next five years 24 BILLION There will be 24 billion IoT devices installed by 2020 $13 TRILLION ROI Total investments over the next five years will generate $13 trillion by 2025
- 14. ENTITIES USING IOT ECOSYSTEM Consumers 5B Devices Installed By 2020 Governments 7.7B Devices Installed By 2020 Businesses 11.2B Devices Installed By 2020 $3B Spent (2015-2020) $2.1B Spent (2015-2020) $900M Spent (2015-2020) $7.6B ROI (2015-2025) $1.4B ROI (2015-2025) $4.7B ROI (2015-2025)
- 15. VERTICALS UTILIZE IOT ECOSYSTEM Transportation Manufacturing Connected Homes Agriculture Oil & Gas, Mining Utilities Infrastructure Health Care Many more….
- 16. INDUSTRIAL INTERNET OF THINGS (IIOT) • A universe of intelligent industrial products, processes and services that communicate with each other and with people over a global network - Accenture • The Industrial Internet of Things (IIoT) is the next wave of innovation impacting the way the world connects and optimizes machines. The IIoT, through the use of sensors, advanced analytics and intelligent decision making, will profoundly transform the way field assets connect and communicate with the enterprise – EDN Networks • The Industrial Internet of Things is an evolution of existing technologies that enables end users to improve processes, drive productivity, and maintain an edge in our increasingly competitive global economy - Kepware
- 17. IOT & IIOT Source – Control Engineering
- 18. IIOT FRAMEWORK Source - EDN Networks
- 19. KEY CHALLENGES IN IIOT • Settling on device capabilities • Security • Bridging the gaps that divide us (people)
- 20. IIOT & ITS IMPACT 20 Workforce Advanced Analytics Intelligent Machine
- 21. INTRODUCTION OF AOT Connected Devices Embedded Analytics Smart Devices Analytics enables to make predictions and send alerts/notifications from streaming data(real time sensor data) using automated analytics platform Analytics decode and transform the continuous flow of M2M data into value-added information
- 22. WHAT MAKES IOT ANALYTICS DIFFERENT
- 23. CASE STUDY • Preventive Maintenance • Freezer Failure (Proactive Failure Detection) • Solar PV Plant (Real Time Analytics)
- 25. 1. PREVENTIVE MAINTENANCE (CONT.)
- 26. 1. PROACTIVE FAILURE DETECTION
- 27. 1. PROACTIVE FAILURE DETECTION (CONT.)
- 28. 1. PROACTIVE FAILURE DETECTION (CONT.)
- 29. 1. PROACTIVE FAILURE DETECTION (CONT.)
- 30. 1. PROACTIVE FAILURE DETECTION (CONT.)
- 31. SOLAR UTILITY EPC COMPANY 31 Increasing Challenges How do you create a Competitive Advantage ? Tariff rates O&M expenses Remote locations
- 32. 32 LETS TRACK A 20 MW SOLAR PLANT 3 2 4 1 5 6 7 9 8 Remote Monitoring & Control, Diagnostics and Analytics are very CRITICAL for Solar plants!
- 33. QUANTUM OF DATA : 20MW SOLAR PV PLANT CONFIDENTIAL & PROPRIETARY DOCUMENT Sr. No. Device Type Total Devices Frequency Daily (B) Monthly (B) 1 Combiner Box 208 1 Hz 61,10,20,800 18,33,06,24,000 2 INVERTER 26 1 Hz 58,81,07,520 17,64,32,25,600 3 TRAFO 13 1 Hz 17,59,680 5,27,90,400 4 MFM 17 1 Hz 1,91,43,360 57,43,00,800 5 VCB 15 1 Hz 2,16,000 64,80,000 6 PLANT 1 1 Hz 12,24,000 3,67,20,000 7 ZONE 8 1 Hz 11,75,040 3,52,51,200 8 WS 1 1 Hz 5,63,040 1,68,91,200 9 FAN 26 1 Hz 37,440 11,23,200 10 UPS 8 1 Hz 45,04,320 13,51,29,600 11 LDB 8 1 Hz 46,080 13,82,400 12 ACDB 8 1 Hz 11,520 3,45,600 13 SURGEARRESTOR 208 1 Hz 2,99,520 89,85,600 14 TEMPSENSOR 6 1 Hz 1,46,880 44,06,400 Total (Bytes) 36,84,76,56,000 Total (GigaBytes) 34.3171
- 34. HOW TO DIGEST THIS HUMONGOUS DATA FOR DATA INSIGHTS CONFIDENTIAL & PROPRIETARY DOCUMENT • Performance Analytics • Real Time Benchmarking • Trend Analysis • Adaptive Machine Learning Algorithms Performance Analytics Trend Analysis Machine Learning Real Time Benchmarking
- 35. PERFORMANCE ANALYTICS CONFIDENTIAL & PROPRIETARY DOCUMENT CB-01 CB-02 CB-03 CB-04 CB-05 CB-06 CB-07 CB-08 CB-09 CB-10 CB-11 CB-12 CB-13 CB-14 CB-15 CB-16 CB-17 CB-18 CB-19 CB-20 CB-21 CB-22 CB-23 CB-24 CB-25 CB-26 CB-27 CB-28 CB-29 CB-30 CB-31 CB-32 CB-33 CB-34 CB-35 CB-36 CB-37 CB-38 CB-39 CB-40 CB-41 CB-42 CB-43 CB-44 CB-45 CB-46 CB-47 CB-48 CB-49 CB-50 CB-51 CB-52 CB-53 CB-54 CB-55 CB-56 CB-57 CB-58 CB-59 CB-60 CB-61 CB-62 CB-63 CB-64 CB-65 CB-66 CB-67 CB-68 CB-69 CB-70 CB-71 CB-72 CB-73 CB-74 CB-75 CB-76 CB-77 CB-78 CB-79 CB-80 CB-81 CB-82 CB-83 CB-84 CB-85 CB-86 CB-87 CB-88 CB-89 CB-90 CB-91 CB-92 CB-93 CB-94 CB-95 CB-96 CB-97 CB-98 CB-99 CB-100 CB-01 CB-02 CB-03 CB-04 CB-05 CB-06 CB-07 CB-08 CB-09 CB-10 CB-11 CB-12 CB-13 CB-14 CB-15 CB-16 CB-17 CB-18 CB-19 CB-20 CB-21 CB-22 CB-23 CB-24 CB-25 CB-26 CB-27 CB-28 CB-29 CB-30 CB-31 CB-32 CB-33 CB-34 CB-35 CB-36 CB-37 CB-38 CB-39 CB-40 CB-41 CB-42 CB-43 CB-44 CB-45 CB-46 CB-47 CB-48 CB-49 CB-50 CB-51 CB-52 CB-53 CB-54 CB-55 CB-56 CB-57 CB-58 CB-59 CB-60 CB-61 CB-62 CB-63 CB-64 CB-65 CB-66 CB-67 CB-68 CB-69 CB-70 CB-71 CB-72 CB-73 CB-74 CB-75 CB-76 CB-77 CB-78 CB-79 CB-80 CB-81 CB-82 CB-83 CB-84 CB-85 CB-86 CB-87 CB-88 CB-89 CB-90 CB-91 CB-92 CB-93 CB-94 CB-95 CB-96 CB-97 CB-98 CB-99 CB-100 CB-01 CB-02 CB-03 CB-04 CB-05 CB-06 CB-07 CB-08 CB-09 CB-10 CB-11 CB-12 CB-13 CB-14 CB-15 CB-16 CB-17 CB-18 CB-19 CB-20 CB-21 CB-22 CB-23 CB-24 CB-25 CB-26 CB-27 CB-28 CB-29 CB-30 CB-31 CB-32 CB-33 CB-34 CB-35 CB-36 CB-37 CB-38 CB-39 CB-40 CB-41 CB-42 CB-43 CB-44 CB-45 CB-46 CB-47 CB-48 CB-49 CB-50 CB-51 CB-52 CB-53 CB-54 CB-55 CB-56 CB-57 CB-58 CB-59 CB-60 CB-61 CB-62 CB-63 CB-64 CB-65 CB-66 CB-67 CB-68 CB-69 CB-70 CB-71 CB-72 CB-73 CB-74 CB-75 CB-76 CB-77 CB-78 CB-79 CB-80 CB-81 CB-82 CB-83 CB-84 CB-85 CB-86 CB-87 CB-88 CB-89 CB-90 CB-91 CB-92 CB-93 CB-94 CB-95 CB-96 CB-97 CB-98 CB-99 CB-100 CB-01 CB-02 CB-03 CB-04 CB-05 CB-06 CB-07 CB-08 CB-09 CB-10 CB-11 CB-12 CB-13 CB-14 CB-15 CB-16 CB-17 CB-18 CB-19 CB-20 CB-21 CB-22 CB-23 CB-24 CB-25 CB-26 CB-27 CB-28 CB-29 CB-30 CB-31 CB-32 CB-33 CB-34 CB-35 CB-36 CB-37 CB-38 CB-39 CB-40 CB-41 CB-42 CB-43 CB-44 CB-45 CB-46 CB-47 CB-48 CB-49 CB-50 CB-51 CB-52 CB-53 CB-54 CB-55 CB-56 CB-57 CB-58 CB-59 CB-60 CB-61 CB-62 CB-63 CB-64 CB-65 CB-66 CB-67 CB-68 CB-69 CB-70 CB-71 CB-72 CB-73 CB-74 CB-75 CB-76 CB-77 CB-78 CB-79 CB-80 CB-81 CB-82 CB-83 CB-84 CB-85 CB-86 CB-87 CB-88 CB-89 CB-90 CB-91 CB-92 CB-93 CB-94 CB-95 CB-96 CB-97 CB-98 CB-99 CB-100
- 36. REAL TIME BENCHMARKING CONFIDENTIAL & PROPRIETARY DOCUMENT
- 37. TREND ANALYSIS CONFIDENTIAL & PROPRIETARY DOCUMENT
- 38. TREND ANALYSIS CONFIDENTIAL & PROPRIETARY DOCUMENT
- 39. ADAPTIVE MACHINE LEARNING ALGORITHMS CONFIDENTIAL & PROPRIETARY DOCUMENT
- 40. CHALLENGES WITH ANALYTICS IN IOT • Data is growing bigger • Data in motion • Geographically distribution of data • New functionality and tools (capability perspective)
- 41. TOP 10 MARKET PLAYERS IN IOT
- 42. We are Engineer’s and we can really connect better to machines rather than human beings…!! IoT It will change our lives
- 43. DATA SCIENCE WHY THE WORLD IS LOOKING AT IT?
- 44. SOME STATS ON DATA SCIENCE: • 25% of organizations now have a data scientist on staff. • By increasing the usability of data by just 10%, the average Fortune 100 company could expect an increase of $2 billion dollars (source: Fathom) • 1,40,000 to 1,90,000 people with deep analytic skills as well as 1.5 million managers and analysts will be needed by 2018 to fill jobs in Big Data in US (source: McKinsey) • 86% of people are willing to pay more for a great customer experience with a brand (source: Lunch Pail) • By 2020 the IoT Will Include 26 Billion Units, Creating New Challenges for All Aspects of the Data Center (source : Gartner)
- 46. WHAT IS ANALYTICS? Maturity of Analytics Capabilities CompetitiveAdvantage Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Predictive Modelling Optimisation What Happened? – Descriptive Analytics Why did it happen? – Diagnostic Analytics What will happen? – Predictive Analytics What is the best that could happen? Prescriptive Analytics Sense & Respond Predict & Act Generic Predictive Analytics Source: SAP
- 47. MACHINE LEARNING • Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. - Wikipedia • Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. - whatis.techtarget.com
- 48. TYPE OF PROBLEM CATEGORIES Supervised Learning Unsupervised Learning
- 49. COMMON PREDICTIVE ANALYTICS METHOD • Regression: Predicting output variable using its cause-effect relationship with input variables. OLS Regression, GLM, Random forests, ANN etc. • Classification: Predicting the item class. Decision Tree, Logistic Regression, ANN, SVM, Naïve Bayes classifier etc. • Time Series Forecasting: Predicting future time events given past history. AR, MA, ARIMA, Triple Exponential Smoothing, Holt- Winters etc.
- 50. COMMON PREDICTIVE ANALYTICS METHOD (CONTD.) • Association rule mining: Mining items occurring together Apriori Algorithm. • Clustering: Finding natural groups or clusters in the data. K-means, Hierarchical, Spectral, Density based EM algorithm Clustering etc. • Text mining: Model and structure the information content of textual sources. Sentiment Analysis, NLP
- 51. BUSINESS APPLICATIONS OF PREDICTIVE ANALYTICS Factory Failures FinanceSmarter Healthcare Multi-channel sales Telecom Manufacturing Traffic Control Trading Analytics Fraud and Risk Renewable Energy Spam Filters Retail: Churn
- 52. PREDICTIVE ANALYTICS TOOLS IN MARKET
- 53. LEARNING LINKS • https://www.coursera.org/browse/data-science • http://www.kdnuggets.com/2015/09/15-math-mooc-data-science.html • http://www.datasciencecentral.com/profiles/blogs/how-to-become-a- data-scientist-for-free • http://www.r-bloggers.com/in-depth-introduction-to-machine-learning- in-15-hours-of-expert-videos/ • http://online.stanford.edu/course/machine-learning-3 • http://online.stanford.edu/course/mining-massive-datasets • Kaggle : The leading platform for predictive analytics competitions • https://www.analyticsvidhya.com/
- 54. QUESTIONS AND DISCUSSION THANK YOU SO MUCH FOR YOUR VALUABLE TIME Mitesh Gupta Email: [email protected] LinkedIn: https://in.linkedin.com/in/mitesh- gupta-28014633