1. Internet of Things

2. Flow of Presentation :
• IoT
• IoT Architecture
• Big Data
• 4 V’s of Big Data
• IoT and Big Data
• Conclusion

3. Internet of Things :
IoT Examples :
Fig. Examples of Internet of Things
• Internet of Things (IoT) is an ecosystem of connected physical objects that are accessible
through the internet.
Garbage Management
with IoT
Smart Farming
with IoT
Energy Management
with IoT
Pollution Control
with IoT
• Internet of Things (IoT) refers to a system of connected physical objects via the internet. The
‘thing’ in IoT can refer to any device which is assigned through an IP address.
• A ‘thing’ collects and transfers data over the internet without any manual intervention with the help
of embedded technology. It helps them to interact with the external environment or internal states
to take the decisions.

4. • Stage 1 (Sensors/Actuators):
A thing in the context of “Internet of Things”, should be equipped with sensors and actuators thus giving
the ability to emit, accept and process signals.
• Stage 2 (Data Acquisition Systems):
The data from the sensors starts in analogue form which needs to be aggregated and converted into digital
streams for further processing. Data acquisition systems perform these data aggregation and conversion
functions.
• Stage 3 (Edge Analytics):
Once IoT data has been digitized and aggregated, it may require further processing before it enters the
data center, this is where Edge Analytics comes in
• Stage 4 (Cloud Analytics):
Data that needs more in-depth processing gets forwarded to physical data centers or cloud-based systems.
IoT Architecture
Fig. Architecture of Internet of Things

5. Introduction
Data : The quantities, characters, or symbols on which operations are performed by a
computer, which may be stored and transmitted in the form of electrical signals and
recorded on magnetic, optical, or mechanical recording media.
Big Data : It is also data but with a huge size. Big Data is a term used to describe a collection of
data that is huge in size and yet growing exponentially with time. In short such data is
so large and complex that none of the traditional data management tools are able to store it or
process it efficiently.
Types of Big Data :-
Structured Unstructured
Semi
Structured

6. 4 V’s of Big Data
The speed at which vast
amounts of data are
being generated,
collected and analyzed.
the incredible
amounts of
data generated
each second
The different
types of data
we can now
use.
Veracity is the quality or
trustworthiness of the
data.

7. IoT & Big Data
• According to the Cisco Report around 15 billion devices will connected to Internet by 2022.
• Connected Home is the largest growing Sector in IoT
• Connected Cars is the fastest growing Sector in IoT
Table. Global IoT Growth / M2M Connections by Vertical

8. Storing IoT Data
IoT devices typically have limited data storage capabilities, so the bulk of the data acquired by IoT devices
needs to be communicated using communication protocols such as MQTT or CoAP, and then ingested by
IoT services for further processing and storage.
Data storage technologies that are often adopted for IoT event data include NoSQL databases and time series databases.
• NoSQL databases are a popular choice for IoT data used for analytics because they support high
throughput and low latency, and their schema-less approach is flexible, allowing new types of data to be
added dynamically. Open source NoSQL databases used for IoT data include Couchbase, Apache
Cassandra, Apache CouchDB, MongoDB and Apache HBase, which is the NoSQL database for Hadoop.
• Time series databases can be relational or based on a NoSQL approach. They are designed specifically
for indexing and querying time-based data, which is ideal for most IoT sensor data, which is temporal in
nature. Time series databases used for IoT data include InfluxDB, OpenTSB, Riak, Prometheus and
Graphite.
IoT
Devices
MQTT
Storage Analysis
Visualize

9. Analysis IoT Data
IoT data needs to be analyzed in order to make it useful, but manually processing the flood of data produced by IoT
devices is not practical. So, most IoT solutions rely on automated analytics.
Analytics can be performed in real-time as the data is received or through batch processing of historical data. Analytics
approaches include distributed analytics, real-time analytics, edge analytics, and machine learning.
Distributed analytics
Real-time analytics are also ideal for time series data, because unlike batch processing, real-time analytics tools usually
support controlling the window of time analysis, and calculating rolling metrics, for example, to track hourly averages
over time rather than calculating a single average across an entire dataset.
Real-time analytics
Hybrid engines that can be used for either stream or batch analytics include Apache Spark etc.
Distributed analytics is necessary in IoT systems to analyze data at scale, particularly when dealing with historical
data that is too vast to be stored or processed by a single node
Apache Hadoop is a batch processing framework that uses a MapReduce engine to process distributed data. It was
one of the first open source frameworks to take off for big data analytics.

10. IoT data analytics is essential to the management of large scale, complex IoT
systems like connected cities, where analytics are used to predict demand and
rules are applied to adjust services in response, such as to control adaptive
traffic signals or manage smart lighting.
We have provided an overview of tools and approaches for making sense of IoT
data, including managing data, using analytics to gain insights, and applying
rules to perform actions.
Conclusion

11. https://www.edureka.co/blog/what-is-iot/
https://www.zdnet.com/article/iot-to-drive-growth-in-connected-devices-through-2022-cisco/
https://developer.ibm.com/tutorials/iot-lp301-iot-manage-data/
https://www.tutorialspoint.com/hadoop/index.htm
https://spark.apache.org/
References