Cloud for Smart Healthcare
With the widespread use of smartphones and the explosion of sensors and connected devices, users are collecting an unprecedented amount of data on their health. In addition, program sponsors today can share aggregate program data with an ecosystem of partners that can set goals, monitor, advise and influence the programs for better outcome. Cloud based systems allow the statistical evaluation of user data obtained by health monitoring sensors (e.g., wearable monitors such as FitBit devices or blood pressure, temperature, pulse, weight, or activity monitors) which is vital for providing many value added smart healthcare services. However, privacy remains a major concern. US20150286800 illustrates a system that establishes trust by providing the user full control over his data by specifying what parts of the data, how, when and for how long is shared with a particular program sponsor or an ecosystem partner that can improve the outcomes of the program.
US20150088538 illustrates another cloud based smart health care system that provides various devices for tracking and monitoring health statistics and behaviors including oral health, fitness, heart health, bone health, salivary diagnostics and diabetes. Health statistics and behaviors are tracked and monitored by various devices including toothbrush, connected surface with sensors, salivary diagnostic system and breath analysis system.
Cloud for Smart Home
Various methods of monitoring states of smart home appliances and controlling functions or operations of the smart home appliances have been suggested. A user can monitor states of home appliances or control the home appliances through a network formed among the home appliances in smart home environments. The user can also remotely monitor the states of the home appliances or control the home appliances using a smartphone of the user. Such monitoring and controlling may be preformed based on data, associated with states and/or functions of the home appliances, generated by the home appliances. However, a conventional home appliance management system may not efficiently store, analyze, and process a large amount of data, associated with the states and/or the functions of the home appliances, generated at every unit time.
US20150134727 illustrates a cloud based system for managing home appliances that may efficiently manage a large amount of data generated by the home appliances. Data generated by the home appliances (metadata) includes an identifier, a make, a model, a time, a location, a status and an event. The cloud based home appliance management system determines the particular interpretation instructions (define particular control rules for the home appliances) that are associated with the particular ontology recognition of the data.
Cloud for Connected Car
Smartphones along with the cloud computing service can assist drivers with respect to improving driver safety. For example, the cloud-based assistive technology can warn drivers upon lane departures, impending collisions, and/or vehicles in blind-spots. US20150262486 (Microsoft) illustrates a technology by which driver safety technology such as collision detection is implemented via smartphone sensors and the cloud service that processes data received from vehicles associated with the smartphone. The cloud service receives the trajectory-related data. The trajectory-related data is used to predict collisions between vehicles and/or lane departures of vehicles. To operate the service in real-time with low latency, driving areas are divided into grids, e.g., based upon traffic density, having parallel grid servers each responsible for only vehicles in or approaching its own grid, and other parallel/distributed mechanisms of the cloud service. The grid servers associated with the grids determine whether vehicles that are known to the grid server to be in or approaching the associated individual grid are at risk of collision based on information received at the cloud service from smartphones related to the vehicles, and to output alert-related data for communication to the vehicles that is at risk of collision.
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