Semantic IoT: The Key to Realizing IoT Value
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(Artificial Intelligence) enabled diet charts to recommend or suggest add-on items
based on the restaurant traffic, current selection, or environmental conditions like
time or weather of a day. Although it is a lucrative upsell opportunity, it is not
followed seriously as a critical-mission system. The success of the industry requires
proper integration of SIoT, machine learning, and AI as the top revenue-generating
engines. It must consider modern analytic models to be embedded with a customer
lifecycle completely. For example, displaying a client’s name and his or her choice
or preferences on the menu chart enhances his or her satisfaction level and makes the
customer feel special. It further provides both the customer and the organization many
inputs such as his interests, behaviors, or future intention in real-life environments.
Similarly, the SIoT powered AI chat-bouts assist a client to tackle simple issues, make
him pleased with the user-friendly experience. The information on recommendations
or offers based on customer choice will boost revenue and drives his intentions
due to personalization. A survey shows 91% of consumers are inclined to buy a
product based on offers and recommendations from companies which, remember,
recognize, and value his or her association with a product [34]. Similarly, 83% of
customers are willing to share their information to enable a personalized experience
that helps SI in IoT. Thus, it is possible for a business to forecast a customer’s
next move, by consistently feeding and upgrading SI information collected from the
different IoT framework. An intelligent embedded IoT, AI, and SI in business models
using customers’ profiles and information facilitate the recommendation of the nextsuitable-action on every stage of a customer life-cycle. In this regard, the simulating
engines or machine learning algorithms can provide the desired feedback for further
new developments for better outcomes. Ultimately, it boosts revenue with increased
productivity, reduced operational expenses, improved personalization at scale and
customer satisfaction. The SI enabled automated IoT embedded with intelligence can
allow thousands of such models to function concurrently for the benefit of both the
service or product provider and the customer. A few of the state-of-art ML algorithms
with their attributes, advantages, and disadvantages have been summarized in Table
2 [35].
5 Semantic Ontology
A few of the popular approaches applied to envisage the SI are the proxy gateway,
Unified Data Models, and Frameworks, Ontologies.
Proxy gateway is an intermediate communication that sends a request and delivers
the corresponding responses for another node. It communicates with other nodes in
the other native data model as well as encoding scheme hence remains transparent.
For example, the data model translator is an entity in the proxy gateway which
translates between two data models, although for the heterogeneous network, it is
not suitable. Due to this disadvantage, the proxy gateways apply to small networks
and can facilitate technical interoperability. The unified data models such as the
LWM2M objects, IPSO Smart Objects, Cluster Library (Zigbee Alliance), and ETRI
91
(Artificial Intelligence) enabled diet charts to recommend or suggest add-on items
based on the restaurant traffic, current selection, or environmental conditions like
time or weather of a day. Although it is a lucrative upsell opportunity, it is not
followed seriously as a critical-mission system. The success of the industry requires
proper integration of SIoT, machine learning, and AI as the top revenue-generating
engines. It must consider modern analytic models to be embedded with a customer
lifecycle completely. For example, displaying a client’s name and his or her choice
or preferences on the menu chart enhances his or her satisfaction level and makes the
customer feel special. It further provides both the customer and the organization many
inputs such as his interests, behaviors, or future intention in real-life environments.
Similarly, the SIoT powered AI chat-bouts assist a client to tackle simple issues, make
him pleased with the user-friendly experience. The information on recommendations
or offers based on customer choice will boost revenue and drives his intentions
due to personalization. A survey shows 91% of consumers are inclined to buy a
product based on offers and recommendations from companies which, remember,
recognize, and value his or her association with a product [34]. Similarly, 83% of
customers are willing to share their information to enable a personalized experience
that helps SI in IoT. Thus, it is possible for a business to forecast a customer’s
next move, by consistently feeding and upgrading SI information collected from the
different IoT framework. An intelligent embedded IoT, AI, and SI in business models
using customers’ profiles and information facilitate the recommendation of the nextsuitable-action on every stage of a customer life-cycle. In this regard, the simulating
engines or machine learning algorithms can provide the desired feedback for further
new developments for better outcomes. Ultimately, it boosts revenue with increased
productivity, reduced operational expenses, improved personalization at scale and
customer satisfaction. The SI enabled automated IoT embedded with intelligence can
allow thousands of such models to function concurrently for the benefit of both the
service or product provider and the customer. A few of the state-of-art ML algorithms
with their attributes, advantages, and disadvantages have been summarized in Table
2 [35].
5 Semantic Ontology
A few of the popular approaches applied to envisage the SI are the proxy gateway,
Unified Data Models, and Frameworks, Ontologies.
Proxy gateway is an intermediate communication that sends a request and delivers
the corresponding responses for another node. It communicates with other nodes in
the other native data model as well as encoding scheme hence remains transparent.
For example, the data model translator is an entity in the proxy gateway which
translates between two data models, although for the heterogeneous network, it is
not suitable. Due to this disadvantage, the proxy gateways apply to small networks
and can facilitate technical interoperability. The unified data models such as the
LWM2M objects, IPSO Smart Objects, Cluster Library (Zigbee Alliance), and ETRI