Analytics in IoT - Logistics Industry

1. Analytics in Internet of
Things - IoT

2. Introduction

3. Introduction
 The Internet of Things (IoT) is a scenario in which objects, animals or people are provided with unique identifiers and the ability to
transfer data over a network without requiring human-to-human or human-to-computer interaction.
 Thus, the internet of things creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics.
Cross-correlation of this data could revolutionize the targeted marketing of products and services.
 As per Forbes, IoT analytics will be hot, with a five-year CAGR of 30%
 The Internet of Things (IoT) will be the next critical focus for data/analytics services. While the IoT trend has focused on the data
generation and production (sensors) side of the equation, the “Analytics” of Things is a particular form of big data analytics that often
involves anomaly detection and “bringing the data to the analytics.”
 The Internet of Things (IoT) has a potential transformational effect on the data center market, its customers, technology providers,
technologies, and sales and marketing models, according to Gartner, Inc. Gartner estimates that the IoT will include 26 billion units
installed by 2020, and by that time, IoT product and service suppliers will generate incremental revenue exceeding $300 billion,
mostly in services.

4. Business Case

5. Business Case - Scenario
Scenario
• A truck generates millions of event for a given route.
• An event could be a normal event: Starting/Stopping a vehicle
• An event could be violation event: Speeding, excessive acceleration and braking.
• Company uses application to monitor location and violation of truck/driver at real time.

6. Architecture

7. HDFS (Truck Event)
High Level Architecture
YARN
ElasticSearch
Active
MQ
Kafka
SPOUT
ES BOLTHDFS BOLTMonitoring
BOLT
T(1
)
T(2
) T(N) Truck Event Topic
Events
Table
Hadoop
ELASTICSEARCH
STORM STREAM PROCESSING
Web App
Inbound Messaging KafkaTruck Streaming Data

8. Architecture Highlights
 Use of Real – time streaming application to capture truck sensor data like
Kafka and Storm
 Use of ElasticSearch for Retrieving the data present in HDFS
 Web App for displaying Dashboard and Insights processed using in-memory
tools like Spark
 Use of machine learning tools like Spark Mllib/H2O for predictive analysis to
discovering patterns in data related to violations like speeding and excessive
acceleration/braking, sudden deceleration
 Use of Active MQ to display alerts and messages based on data captured

9. Technologies Used / Infrastructure

10. Hadoop [HDFS]
IoT – Device Data APIs
P
U
S
H
AD HOC
External
Sources
Real-Time
user
interface
 Alert and
Event(ActiveMQ)
Truck Sensor
Stream Processing
(STORM)
APIs
Inbound
Messaging
(Kafka)
Interactive
query (Spark
Core/SQL)
P
U
S
H
Real –time Serving
(ELASTICSEARCH)
Q
U
E
R
Y
G
E
T
/
P
O
S
T
A
P
I
HTTP GET
P
U
S
H
SQL

11. Technology Used
Technology Used
• Hadoop
• Hive
• ElasticSearch
• SpringBoot/Java
• Storm
• Spark Core
• Spark Sql
• Spark Mllib/H2o