IoT REALIZED CONNECTED CAR. Ian Huston Senior Data
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- Hugh Martin
- 5 years ago
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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