Cloud based software as a service (SaaS) enables
cost effective value added services for many Internet of Things (IoT)
applications such as smart home, connected healthcare and preventive
maintenance of industrial equipment.
Samsung patent application US20130227569
illustrates the system that can gather data from thousands of the IoT sensors/devices
and analyze the data without the massive amount of investment in the server and
big data analytics infrastructure.
The cloud based IoT SaaS system provides a
virtual IoT sensors/devices cloud as an Infrastructure as a Service (IaaS), and
a service cloud as a Software as a Service (SaaS), to provide a flexible and
scalable system. The IaaS provides flexibility by handling heterogeneous IoT
sensors/devices. The SaaS provides scalability by relieving end users of
computational overheads, and enabling on-demand sharing of IoT sensors/devices
data to requesting end users. The SaaS also relieves end users from specifying IoT
sensors/devices characteristics, locating physical IoT sensors/devices, and
provisioning for the physical IoT sensors/devices. The end user, via a device
(e.g., smartphone), requests and receives services provided by the system.
Oracle patent application US20150227118 illustrates
the cloud based IoT SaaS system for facilitating automatic control of the smart
home devices based on past device behavior, current device events, sensor data,
and/or server-sourced data. Cloud-based big data analytics is accessible via a
server system for analyzing data associated with persons or buildings in a
geographic region about the building, such as local news and weather
information and data pertaining to appliances within the geographic region,
such as a neighborhood, zip code, and so on. The analyzed data is used to
develop the control rules to control smart home devices automatically.
The automatic control of the smart home
devices enable various benefits, such as triggering lights to automatically
turn on when a user enters a particular room at a particular time; activating a
sprinkler system when server-side data indicates that a fire is nearby;
automatically turning on a heater in advance of a home owner's return at a
particular time when the home temperature is below a predetermined level;
turning off a sound system and lights in various rooms after data indicates
that a user is preparing to sleep; turning off lower priority devices that may
conflict with higher priority devices, and so on.
Cloud-based big data analytics also can be
used to make a prediction about the future device usage and/or device behavior
and/or user behavior. The device usage and/or device behavior and/or user
behavior predictions can be used to generate control rules. The prediction can be
derived by comparing collected data with a sample table of data to determine
whether a correlation exists between the collected data and data in the sample
table of data. The prediction can be generated based on a correlation between
the collected data and data in the sample table of data. The prediction also
can be based on a frequency of occurrence of an instance of data in the collected
data (and timing information associated with occurrences of the instances of
data) to generate a probability estimate. The probability estimate is employed
to determine the prediction.
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