Page 353 - From Smart Grid to Internet of Energy

Big data, privacy and security in smart grids Chapter 8 317

The IoT based device number is expected to reach up to billions that can trans-
mit instant data. In addition to data volumes, variety and structures of transmit-
ted data will be heterogeneous depending IoT device types, structures, and
vendors. The IoT devices are foreseen to be converted from simple sensors
to much more sophisticated systems which have their own processors and stor-
age devices. Therefore, malfunction of such systems may cause to severe fail-
ures which cannot be handled with basic troubleshooting methods. On the other
hand, the updating frequency of data acquisition is another challenging topic in
IoT Big Data operations. The substantial part of IoT devices are assumed to
transmit measurement and control signals with higher frequencies while some
others such as cars and electric vehicles will generate data stacks at lower
frequencies [15].
The IoT devices and sensors comprise entity definitions as shown in Fig. 8.3.
The sensor-based communication infrastructure provides connection between
devices and applications at IoT service layer.
The third-party applications are also integrated to IoT ecosystem over ser-
vices interface. The interfaces are managed by application program interface
(API) based software which provide authentication and security controls
according to standards. The data layer applications are operated regarding to
subscriber data and enable users to store data. The inherited data stacks come
from several sources and devices and collected by Data and Protocol Mediator
that is responsible for acquiring provided data of IoT based devices and other
sources. The data and protocol mediator layer convert control demands and
requests to low level IoT protocols. The IoT big data storage layer enables mas-
sive and high volume IoT data acquisition for storage processes, and it presents
data for analytics and research purposes. The IoT big data processing section
allows to use hardware and analytics tools to generate data reports and intelli-
gence outputs.
The data storage capability of IoT devices will define flexibility of Big Data
applications since it will allow or prevent data derivation from intelligent
devices for smart monitoring and smart control operations. Another significant
challenge is related with privacy issues against intrusions and violations. Once
the data which is generated by IoT devices is stored in a Big Data analytic sys-
tem, and presented to use for third parties, it forces operators and service pro-
viders to improve system security and authentication controls. The functional
sections and layers of presented IoT Big Data architecture enable to obtain
IoT and Big Data services in a wide range. These services include management
functions, direct data control features, external data acquisition, establishing
connection between IoT data platforms and devices, and delivery of IoT based
data stacks. The Big Data analysis is based on data acquisition methods and data
access opportunities. The function provides data storage feature such as storing
the small scale data to hard disk drives or multiple disk usage depending the
increased data volumes. The stored data stack should be planned by considering