There are many reasons behind the state of insecurity in IoT. Some of it has to do with the industry being in its “gold rush” state, where every vendor is hastily seeking to dish out the next innovative connected gadget before competitors do.
• Under such
circumstances, functionality becomes the main focus and #security takes a back
seat.
Connectivity • Connecting so many devices will be
one of the biggest challenges of the future of IoT, and it will defy the very
structure of current communication models and the underlying technologies.
• At present we rely on the centralized,
server/client paradigm to authenticate, authorize and connect different nodes
in a network.
This model is sufficient for current IoT
ecosystems, where tens, hundreds or even thousands of devices are involved. But
when networks grow to join billions and hundreds of billions of devices,
centralized brokered systems will turn into a bottleneck.
• Such systems will require huge investments and
spending in maintaining cloud servers that can handle such large amounts of
information exchange, and entire systems can go down if the server becomes
unavailable.
• The future of IoT will very much have to depend
on decentralizing IoT networks. Part of it can become possible by moving
functionality to the edge, such as using #fog computing models where smart
devices such as IoT hubs take charge of time-critical operations and cloud
servers take on data gathering and analytical responsibilities.
• Other solutions involve the use of peer-to- peer
communications, where devices identify and authenticate each other directly and
exchange information without the involvement of a broker. Networks will be
created in meshes with no single point of failure.
• This model will have its own set of challenges,
especially from a security perspective, but these challenges can be met with
some of the emerging IoT technologies such as #Blockchain.
• IoT is growing in many different directions, with
many different technologies competing to become the standard. This will cause
difficulties and require the deployment of extra hardware and software when
connecting devices.
• Other compatibility issues stem from non- unified
cloud services, lack of standardized #M2M protocols and diversities in firmware
and operating systems among IoT devices.
• Some of these technologies will eventually become
obsolete in the next few years, effectively rendering the devices implementing
them useless.
• This is especially important, since in contrast
to generic computing devices which have a lifespan of a few years, IoT
appliances (such as smart fridges or TVs) tend to remain in service for much
longer, and should be able to function even if their manufacturer goes out of
service.
Standards • Technology standards which include
network protocols, communication protocols, and data-aggregation standards, are
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.
Challenges facing the adoptions of standards within
IoT
• Standard for handling unstructured data:
Structured data are stored in relational databases and queried through #SQL for
example. Unstructured data are stored in different types of #NoSQL databases
without a standard querying approach.
• 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.
Intelligent Analysis & Actions • The last stage
in IoT implementation is extracting insights from data for analysis, where
analysis is driven by cognitive technologies and the accompanying models that
facilitate the use of cognitive technologies.
• Artificial intelligence (#AI) models can be
improved with large data sets that are more readily available than ever before,
thanks to the lower storage
• 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
• 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 second phase of this stage is intelligent
actions which can be expressed as #M2M and M2H interfaces for example with all
the advancement in UI and UX technologies.
• Lower machine prices
• Improved machine functionality
• Machines “influencing” human actions through
behavioral-science rationale
• Deep Learning tools
• Machines’ actions in unpredictable situations
• Information security and privacy
• Machine interoperability
• Mean-reverting human behaviors
• Slow adoption of new technologies
Business • The bottom line is a big motivation for
starting, investing in, and operating any business, without a sound and solid
business model for IoT we will have another bubble this model must satisfy all
the requirements for all kinds of e-commerce; vertical markets, horizontal
markets, and consumer markets.
• End-to-end solution providers operating in
vertical industries and delivering services using cloud analytics will be the
most successful at monetizing a large portion of the value in IoT.
• While many
IoT applications may attract modest revenue, some can attract more. For little
burden on the existing communication infrastructure, operators have the
potential to open up a significant source of new revenue using IoT
technologies.
IoT can be divided into 3 categories based on usage
and clients base:
1. Consumer IoT includes the connected devices such
as smart cars, phones, watches, laptops, connected appliances, and
entertainment systems.
2. Commercial IoT includes things like inventory
controls, device trackers, and connected medical devices.
3. Industrial IoT covers such things as connected
electric meters, waste water systems, flow gauges, pipeline monitors,
manufacturing robots, and other types of connected industrial devices and
systems.
• Clearly, it is important to understand the value
chain and business model for the IoT applications for each category of IoT.
Society • Understanding IoT from the customers and
regulators prospective is not an easy task for the following reasons:
• Customer demands and requirements change
constantly.
• New uses for devices—as well as new
devices—sprout and grows at breakneck speeds.
• Inventing
and reintegrating must-have features and capabilities are expensive and take
time and resources.
• The uses for Internet of Things technology are
expanding and changing—often in uncharted waters.
• Consumer Confidence: Each of these problems could
put a dent in consumers' desire to purchase connected products, which would
prevent the IoT from fulfilling its true potential.
• Lack of understanding or education by consumers
of best practices for IoT devices security to help in improving privacy, for
example change default passwords of IoT devices.
Privacy • The IoT creates unique challenges to
privacy, many that go beyond the data privacy issues that currently exist. Much
of this stems from integrating devices into our environments without us
consciously using them.
• This is becoming more prevalent in consumer
devices, such as tracking devices for phones and cars as well as smart
televisions.
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