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1. AI-Enabled IIoT for Live Smart City Event
Monitoring
Abstract
Recent advancements of the Industrial Internet of Things (IIoT) have
revolutionized modern urbanization and smart cities. While IIoT
rich events and objects of interest, processing a massive amount of IIoT data
and making predictions in real
artificial intelligence (AI) allow processing such a massive amount of IIoT data
and generating insights for further decision
we propose several key aspects of AI
monitoring. First, we have combined a human
crowdsourcing application with that of an AI
capture events and objects from IIoT data in real time. Second, we have
combined multiple AI algorithms that can run on distributed edge and cloud
nodes to automatically categorize the captured events and objects and
generate analytics, reports, and alerts from the IIoT data in real time. The
Enabled IIoT for Live Smart City Event
Recent advancements of the Industrial Internet of Things (IIoT) have
revolutionized modern urbanization and smart cities. While IIoT
rich events and objects of interest, processing a massive amount of IIoT data
and making predictions in real-time are challenging. Recent advancements in
artificial intelligence (AI) allow processing such a massive amount of IIoT data
erating insights for further decision-making processes. In this article,
we propose several key aspects of AI-enabled IIoT data for smart city
monitoring. First, we have combined a human-intelligence
crowdsourcing application with that of an AI-enabled IIoT framework to
capture events and objects from IIoT data in real time. Second, we have
combined multiple AI algorithms that can run on distributed edge and cloud
nodes to automatically categorize the captured events and objects and
cs, reports, and alerts from the IIoT data in real time. The
Enabled IIoT for Live Smart City Event
Recent advancements of the Industrial Internet of Things (IIoT) have
revolutionized modern urbanization and smart cities. While IIoT data contain
rich events and objects of interest, processing a massive amount of IIoT data
time are challenging. Recent advancements in
artificial intelligence (AI) allow processing such a massive amount of IIoT data
making processes. In this article,
enabled IIoT data for smart city
intelligence-enabled
bled IIoT framework to
capture events and objects from IIoT data in real time. Second, we have
combined multiple AI algorithms that can run on distributed edge and cloud
nodes to automatically categorize the captured events and objects and
cs, reports, and alerts from the IIoT data in real time. The

2. results can be utilized in two scenarios. In the first scenario, the smart city
authority can authenticate the AI-processed events and assign these events
to the appropriate authority for managing the events. In the second scenario,
the AI algorithms are allowed to interact with humans or IIoT for further
processes. Finally, we will present the implementation details of the scenarios
mentioned above and the test results. The test results show that the
framework has the potential to be deployed within a smart city.