IoT REALIZED CONNECTED CAR. Ian Huston Senior Data

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1 IoT REALIZED CONNECTED CAR Ian Huston Senior Data

2

3 BACK TO THE BEGINNING

4 REALLY COOL IoT PROJECT

5 COVERED BY AN NDA

6

7 CONNECTED CAR

8 PIECES ARE THE SAME

9 PREDICT THE DESTINATION

10 PREDICT THE RANGE

11

12 TCP SOCKET OVER BLUETOOTH HTTP POST OVER CELLULAR

13

14 HOW DOES THIS WORK?

15 START IN THE CAR

16 ON BOARD DIAGNOSTICS OBD II

17 :9000 PHONE PROVIDES CONNECTIVITY

18 {! }! "vehicle_speed":103,! "obd_standards":2,! "intake_manifold_pressure":"",! "accelerator_throttle_pos_e":14,! "engine_load":89,! "maf_airflow":33,! "latitude":" ",! "vin":"1hgcm82633a004352",! "bearing":" ",! "catalyst_temp":779,! "relative_throttle_pos":12,! "fuel_level_input":89,! "fuel_system_status":[2,0],! "accelerator_throttle_pos_d":29,! "acceleration":"0.953",! "throttle_position":21,! "barometric_pressure":97,! "control_module_voltage":13,! "longitude":" ",! "distance_with_mil_on":0,! "coolant_temp":94,! "intake_air_temp":34,! "rpm":1593,! "short_term_fuel":-2,! "time_since_engine_start":4054,! "absolute_throttle_pos_b":38,! "long_term_fuel":3!

19 Pivotal λ-architecture Speed Layer Serving Layer Pivotal CF Spring XD Pivotal GemFire Batch Layer Pivotal HD

20

21 What is Cloud Foundry? Open Source Multi-Cloud Platform Simple App Deployment, Scaling & Availability

22 Cloud Applications Haiku Here is my source code Run it on the cloud for me I do not care how. - Onsi

23 Multi-Cloud Same applications running across different cloud providers Private Public Hosted

24 Pivotal Cloud Foundry for Open Cloud Computing

25 $ cf push

26 Pivotal λ-architecture Speed Layer Serving Layer Pivotal CF Spring XD Pivotal GemFire Batch Layer Pivotal HD

27 ON THE SERVER

28 SPRING XD EXTREME DATA

29 Spring XD Unified, distributed, and extensible open-source system for data ingestion, real time analytics, batch processing, and data export. o Data Ingestion and Pipeline Processing o o Kafka, RabbitMQ, MQTT, JMS, HTTP, GPDB, HAWQ Partition, Filter, Transform, Split, Aggregate o Real Time Analytics and Complex Event Processing o o Spark Streaming, RxJava, JPMML Scoring Redis, GemFire, Cassandra, etc.. o Rapid Dashboarding o Batch Workflow Orchestration + ETL o Map Reduce, HDFS, PIG, Hive, GPDB, HAWQ, Spark Spring XD o RDBMS, FILE, FTP, Log, Mongo, Splunk

30 Ingestion Orchestration SPRING Extraction Real-time Analytics

31 DISTRIBUTED RUNTIME

32 STREAMING BATCH&

33

34 // Ingestion Stream Definitions! http filter acmeenrich shell hdfs!

35 // Tap Definitions! IoT-HTTP.shell > typeconversiontransformer! gemfire-server!

36 // Spark Example Job! job create pi --definition "sparkapp --name=pi \! --master=local[8] \! --appjar=path/lib/spark-examples hadoop2.6.0.jar \! --mainclass=org.apache.spark.examples.sparkpi \! --programargs=100" deploy!! job launch pi!

37 TCP SOCKET OVER BLUETOOTH HTTP POST OVER CELLULAR

38 hdfs Spring XD HTTP transformers python hdfs gemfire tap REST API GemFire

39 hdfs Spring XD transformers python PySpark hdfs gemfire tap REST API GemFire

40 Pivotal λ-architecture Speed Layer Serving Layer Pivotal CF Spring XD Pivotal GemFire Batch Layer Pivotal HD

41 Pivotal Big Data Suite Data Processing Advanced Analytics Apps at Scale Spring XD Pivotal Greenplum Database Pivotal GemFire Spark Pivotal HAWQ Redis Pivotal HD RabbitMQ Big Data Suite Services on Pivotal Cloud Foundry Spring XD Pivotal HAWQ Pivotal HD Redis RabbitMQ

42 Realtime Evaluation Batch Training Spring XD Pivotal HD Data Persistence

43

44 REALTIME DATA SCIENCE

45 JOURNEY 1PREDICT

46 HOW DOES IT WORK?

47 STORE SENSOR DATA

48 RECORDED JOURNEYS

49 OFFLINE BATCH TRAINING

50 JOURNEY CLUSTERS

51 INITIAL PREDICTION

52 DRIVING HOME TO WORK

53 DRIVING WORK TO HOME

54 ONLINE PREDICTION

55 RANGE 2PREDICT

56 ! "Predictions": {! "ClusterPredictions": {! "0": {! "EndLocation": [! ,! ! ],! "MPG_Journey": ,! "Probability": ! },!!

57 HORIZONTAL SCALABILITY

58 100,000 s OF CLIENTS

59 MILLIONS OF MESSAGES

60 PER MINUTE OVER 100 GB

61 IoT ISSUES

62 COMPATIBILITY

63 CONNECTIVITY

64 SECURITY

65 LOOKING INTO THE FUTURE

66 ADDITIONAL USE CASES

67 FLEET MANAGEMENT

68 PREDICTIVE MAINTENANCE

69 ACCIDENT ASSISTANCE

70

71 Ian Michael