IoT implementation and Challenges

Prof. Ahmed Banafa
College of Engineering
San Jose State University
San Jose, CA , USA

 The Internet of Things (IoT) is the network of
physical objects—devices, vehicles, buildings and
other items which are embedded with electronics,
software, sensors, and network connectivity, which
enables these objects to collect and exchange data.

 Implementing this concept is not an easy task by any
measure for many reasons including the complex
nature of the different components of the ecosystem
of IoT.

 To understand the gravity of this task, we will explain
all the five components of IoT Implementation…

 Sensors
 Networks
 Standards
 Intelligent Analysis
 Intelligent Actions

IoT implementation and Challenges

 According to (IEEE) sensors can be defined as: An
electronic device that produces electrical, optical, or
digital data derived from a physical condition or event.
 Data produced from sensors is then electronically
transformed, by another device, into information
(output) that is useful in decision making done by
“intelligent” devices or individuals (people).

Types of Sensors:
 Active Sensors
 Passive Sensors

The selection of sensors greatly impacted by many factors,
including:
 Purpose (Temperature, Motion, Bio…etc.)
 Accuracy
 Reliability
 Range
 Resolution
 Level of Intelligence (dealing with noise and
interference)

The driving forces for using sensors in IoT today are new
trends in technology that made sensors cheaper, smarter
and smaller.

Challenges facing IoT sensors:
 Power consumption
 Security
 Interoperability

 The second step of this implantation is to transmit the
signals collected by sensors over networks with all the
different components of a typical network including
routers, bridges in different topologies, including LAN,
MAN and WAN.

 Connecting the different parts of networks to the
sensors can be done by different technologies
including Wi-Fi, Bluetooth, Low Power Wi-Fi , Wi-Max,
regular Ethernet , Long Term Evolution (LTE) and the
recent promising technology of Li-Fi (using light as a
medium of communication between the different parts
of a typical network including senores)

The driving forces for wide spread network adoption in
IoT can be summarized as follows:
 High Data rate
 Low Prices of data usage
 Virtualization (X - Define Network trends )
 XaaS concept (SaaS, PaaS, and IaaS)
 IPv6 deployment

Challenges facing network implementation in IoT:
 The enormous growth in number of connected devices
 Availability of networks coverage
 Security
 Power consumption

 The third stage in the implementation process includes
the sum of all activities of handling, processing and
storing the data collected from the sensors.

 This aggregation increases the value of data by
increasing, the scale, scope, and frequency of data
available for analysis but aggregation only achieved
through the use of various standards depending on the
IoT application in used.

Types of Standards :
 Two types of standards relevant for the aggregation
process; technology standards (including network
protocols, communication protocols, and data-
aggregation standards) and regulatory standards
(related to security and privacy of data, among other
issues).

Technology Standards :
 Network Protocols (e.g.: Wi-Fi)
 Communications Protocols (e.g.: HTTP)
 Data aggregation standards (e.g.: Extraction,
Transformation, Loading (ETL))

Regulatory Standards
 Set and administrated by government agencies like
FTC, for example Fair Information Practice Principles
(FIPP) and US Health Insurance Portability and
Accountability Act (HIPAA) just to mention few.

Challenges facing the adoptions of standards within IoT
 Standard for handling unstructured data: Structured
data are stored in relational databases and queried
through SQL. Unstructured data are stored in different
types of noSQL databases without a standard querying
approach.

 Security and privacy issues: There is a need for clear
guidelines on the retention, use, and security of the
data as well as metadata (the data that describe other
data).

 Regulatory standards for data markets: Data brokers
are companies that sell data collected from various
sources. Even though data appear to be the currency of
the IoT, there is lack of transparency about, who gets
access to data and how those data are used to develop
products or services and sold to advertisers and third
parties.

 Technical skills to leverage newer aggregation tools:
Companies that are keen on leveraging big-data tools
often face a shortage of talent to plan, execute, and
maintain systems.

 The fourth stage in IoT implementation is extracting
insight from data for analysis, Analysis is driven by
cognitive technologies and the accompanying models
that facilitate the use of cognitive technologies.

 With advances in cognitive technologies’ ability to
process varied forms of information, vision and voice
have also become usable.
 On the next slide, list of selected cognitive
technologies that are experiencing increasing adoption
and can be deployed for predictive and prescriptive
analytics:

 Computer vision refers to computers’ ability to identify
objects, scenes, and activities in images
 Natural-language processing refers to computers’
ability to work with text the way humans do, extracting
meaning from text or even generating text that is
readable.
 Speech recognition focuses on accurately transcribing
human speech

Factors driving adoption intelligent analytics within the
IoT:
 Artificial intelligence models can be improved with
large data sets that are more readily available than
ever before, thanks to the lower storage costs.

 Growth in crowdsourcing and open- source analytics
software: Cloud-based crowdsourcing services are
leading to new algorithms and improvements in
existing ones at an unprecedented rate.

 Real-time data processing and analysis: Analytics tools
such as complex event processing (CEP) enable
processing and analysis of data on a real-time or a
near-real-time basis, driving timely decision making
and action.

Challenges facing the adoptions of intelligent analytics
within IoT:
 Inaccurate analysis due to flaws in the data and/or
model: A lack of data or presence of outliers may lead
to false positives or false negatives, thus exposing
various algorithmic limitations

 Legacy systems’ ability to analyze unstructured data:
Legacy systems are well suited to handle structured
data; unfortunately, most IoT/business interactions
generate unstructured data

 Legacy systems’ ability to manage real- time data:
Traditional analytics software generally works on
batch-oriented processing, wherein all the data are
loaded in a batch and then analyzed

 The fifth stage deals with intelligent actions , Intelligent
actions can be expressed as M2M and M2H interfaces
for example with all the advancement in UI and UX
technologies.

Factors driving adoption of intelligent actions within the
IoT:
 Lower machine prices
 Improved machine functionality
 Machines “influencing” human actions through
behavioral-science rationale
 Deep Learning tools

Challenges facing the adoption of intelligent actions
within IoT:
 Machines’ actions in unpredictable situations
 Information security and privacy
 Machine interoperability
 Mean-reverting human behaviors
 Slow adoption of new technologies

 The Internet of Things (IoT) is an ecosystem of ever-
increasing complexity, it’s the next weave of innovation
that will humanize every object in our life, which is the
next level to automating every object in our life.
 Convergence of technologies will make IoT
implementation much easier and faster, which in turn
will improve many aspects of our life at home and at
work and in between.

IoT implementation and Challenges

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