Alex G. Lee (email@example.com)
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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