Making sense of IoT, M2M and Big Data
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A short presentation on the IoT architecture and its key components.
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Making sense of IoT, M2M and Big Data
- 1. Image Source: IEEE Spectrum
- 3. The IoT Architecture Information Value Loop Applications Analytics DataAggregation Networks Sensors M2M Big Data IoT
- 5. Sensors –The Eyes & Ears Cost of a Sensor µ-Processor Clock Speed Key Issues: Power Constraints Energy Harvest DataTransfer Event-driven Security Encryption, Bandwidth Interoperability Std. vs Prop. Small, Low Power, Low Memory Devices
- 6. Networks –The Lifeline Home Area Network (Bluetooth, 6LoPAN, ZigBee, WiFi) Neighborhood Area Network (WiFi-Mesh, Cellular) Wide Area Network (Fixed, Cellular WAN) Key Issues: Coverage Range, Reliability Capacity Uplink-biased, Protocol Overhead Security Authentication, Privacy MultipleAccess Last Grasp, Sleep Mode Addressing IPv6, Multicast/Broadcast Protocol s MQTT, COAP QoSVariability OFDM, DSCP Too Many Devices,Too ManyTypes
- 7. Data Processing –The Brian SQL/NoSQL Queries KeyTechniques: Distributed Storage (Hadoop HDFS) Parallel Processing (MAP-Reduce,Apache Pig/Hive/HBase) In-memory Processing (Apache Spark) Too Many,Too Much,Too Fast DATA BIG DATA
- 8. Analytics –The Mind Open-source AnalyticsTools Key Areas: Artificial Intelligence (AI) Machine Learning Deep Learning Complex Event Processing (CEP) Natural Language Processing (NPL) Making Sense of Data
- 9. Applications –The Life of IoT
- 10. Case Study – ConnectedVehicles Autopilot Smart Charging Solar Charging Vehicle to Grid
Editor's Notes
- 250M Connected Vehicles by 2020 27B M2M Connections by 2024
- Calls Call Record Mediation BI Offers More Calls
- May require more than one slide
- IP – 20/40 bytes UDP/TCP – 8/20 bytes
- Data extraction – extracts data from homogeneous or heterogeneous data sources Data transformation – transforms the data for storing it in the proper format or structure for the purposes of querying and analysis Data loading – loads it into the final target (database, more specifically, operational data store, data mart, or data warehouse)
- ** Prediction Possibilities not Guarantees Supervised Learning (Machine Learning) Detect faces, identify people in images, recognize facial expressions (angry, joyful) Identify objects in images (stop signs, pedestrians, lane markers…) Recognize gestures in video Detect voices, identify speakers, transcribe speech to text, recognize sentiment in voices Classify text as spam (in emails), or fraudulent (in insurance claims); recognize sentiment in text (happy, angry, customer feedback) Deep Learning – Unsupervised Learning Hardware breakdowns (data centers, manufacturing, transport) Health breakdowns (strokes, heart attacks based on vital stats and data from wearables) Customer churn (predicting the likelihood that a customer will leave, based on web activity and metadata) Employee turnover (ditto, but for employees)
- May require more than one slide