Making Sense of IoT Data
                     Making Sense of IoT Data
Satya K Vivek
Writes for Gadgeon.com, an 
IoT software development services from Gadgeon, and has 
expertise in Gadgeon-IoT software development company.
 
Today we are living in an environment of data and connectivity. Literally 
every device in this world can be connected to the internet via the 
technology called IoT.  Devices are instrumented with sensors, which 
effectively identify, measure, and dispatch data in different formats and 
frequency. As more and more sensors get deployed, we are witnessing an 
explosion in data volume. Advancement of IoT technologies makes it easy 
to process the huge volume of data generated and make sense out of 
them. Different devices connected over these sophisticated systems are 
capable of real-time monitoring that generates valuable insights that save 
energy, money, and time.
Different stages of dealing with IoT data
An IoT environment consists of mechanisms for data collection, data 
aggregation and data analytics. The collected data is processed and 
analyzed to generate dashboards, reports, and alerts. This process of 
making sense out of data involves different steps using different 
technologies and computing techniques. Major steps involved are 
discussed below:
Gathering of data
Gathering of data is the most important first steps in the data value chain. 
There are multiple types of Sensors available today that enables collection 
of a variety of data in an IoT environment. These sensors measure the 
different conditions of physical things on a time scale. Sensors on noticing 
a change in data, or at defined intervals, gather and transmit the data in 
real-time. These sensors are connected to an edge device, which collects 
and harmonize the data. The edge device collates and prepares the data 
for storage and forwarding to the cloud servers. The data along with its 
metadata will be transmitted to the server for storage and further 
processing.  The following figure illustrates the data collection and 
aggregation features.
Configure and Provision the Data
In IoT environments, devices and sensors are connected to the edge 
gateway. The devices and sensors are to be configured and provisioned at 
the gateway to uniquely identify each data source, recognize the data - 
what data (temperature, pressure, vibration etc.), data type, format, units, 
and source so on. Edge Gateways provides web based UI for performing 
the configuration and provisioning of devices and sensors connected.  This 
allows the edge gateways to uniquely recognize the data and transmit to 
the application servers at the cloud or on-premise services. The time-
stamped data allows it to be processed in batches or in near real-time. 
Collect and Harmonize the Data
Edge gateways help in transforming data from the devices. The edge 
gateway collates the data received from each devices or sensors. These 
data could be collected at different frequency, format and could be using 
different interface protocols. Gateways harmonize these different factors 
and prepare the data for transmission to the server. Edge gateways will 
use multiple protocols to connect with the Cloud and transmit data like 
MQTT, IoT hub, Kafka.
Securing Data
It is important to store and transmit data securely. This would help in 
retaining its privacy and integrity. Different encryption technologies and 
security protocols are used in ensuring security of data. Access to the data 
outside of the network is prevented through multi-level authentication 
techniques. The data is handled and interpreted by only authorized user 
roles and profiles, enforced at the application levels. 
Storing data
The cloud server receives data from all connected gateways and sensors. 
The data packets received are stored in a database designed for the 
application. The historian databases enhance the ability to perform 
predictive analytics on the data. 
Analyzing data
It is necessary to evaluate IoT data to ensure its proper utilization. 
Applications process and analyze the data to extract different insights and 
information from the data received.  Different anomaly conditions are 
identified and defined, so that the moment data crosses the threshold, 
automated alerts can be generated and distributed to authorized people. 
This enables real-time remote monitoring of operations and processes.  
The data is used to compute key performance indicators (KPIs) so that 
appropriate dashboards and reports are generated, enabling data driven 
decision making across the organization. 
Advanced analytical engines using AI and ML techniques are applied on 
the data to predict equipment failures, prescribe outcomes etc. 
preventing unscheduled equipment failures leading to improved bottom-
line for the company. Analyzing IoT data is imperative to successful large-
scale management. Robust IoT systems use data analytics for the 
prediction of trends and demands. Accordingly, the businesses deploy 
policies and processes that allow making necessary adjustments easy.
About Gadgeon
Gadgeon is known for its expertise in Industrial IoT and engineering 
excellence. We connect devices, operations, and processes to create 
business value, and revolutionize enterprises with the power of data. As 
an end-to-end technology services company, we successfully enabled the 
digital journey of customers with critical digital services ranging from 
embedded systems, cloud app development, mobile app development, 
data & analytics, application modernization, emerging technology based 
solutions, and testing & test automation across the industries such as 
connected factory, telecom & datacom, digital healthcare, CSPs, and 
home & building automation.
Thank you for time in reading this article!