WO2019069144A1

WO2019069144A1 - Systems and methods for managing internet of things (iot) data for a smart city using an iot datahub

Info

Publication number: WO2019069144A1
Application number: PCT/IB2018/050927
Authority: WO
Inventors: Arun Vijayakumar; Srinivasa Raghavan VENKATACHARI; Harish Kumar DHANASEKARAN
Original assignee: Tata Consultancy Services Limited
Priority date: 2017-10-06
Filing date: 2018-02-15
Publication date: 2019-04-11

Abstract

Systems and methods for managing Internet of Things (IoT) data for a smart city using an IoT datahub. The traditional systems and methods provide for an IoT platform for smart cities but none of them provide for an integrated platform which facilitates abstracting, storing and sharing contextually enriched structured IoT data referring PAS 182 model standards. Embodiments of the present disclosure provide for managing Internet of Things (IoT) data for smart cities using the IoT datahub by obtaining data from devices or sensors connected to the IoT datahub, transforming the data, contextually correlating the data, enhancing value of the contextually correlated data, aggregating the contextually correlated data to generate a structured data by an unified Application programming Interface (API) to be accessed by users connected to the IoT datahub.

Description

SYSTEMS AND METHODS FOR MANAGING INTERNET OF THINGS (IoT) DATA FOR A SMART CITY USING AN IoT DATAHUB

CROSS REFERENCE TO RELATED APPLICATIONS AND PRIORITY

[0001] This patent application claims priority to India Patent Application 201721035551, filed on October 06, 2017, the entirety of which is hereby incorporated by reference

TECHNICAL FIELD

[0002] The present disclosure generally relates to managing Internet of Things (IoT) data for a smart city using an IoT datahub. More particularly, the present disclosure relates to systems and methods for managing Internet of Things (IoT) data for a smart city using an IoT datahub

BACKGROUND

[0003] Smart cities comprise of complex environments where several areas of innovation meet in order to substantially improve socioeconomic development and quality of life. Internet of things (IoT) technologies in smart cities have included solutions for specific urban infrastructures such as transportation; critical infrastructure such as water, and energy management, urban-scale IoT platforms, solutions for citizen engagement and integration of social networks into the IoT. Many challenging issues still exist before a highly effective software platform for smart cities can be created, including: enabling interoperability between a city's multiple systems, managing large amounts of data, supporting the required scalability, and dealing with a large variety of sensors. It is expected that both the variety and quality of data streams generated by city infrastructure and citizens will continue to increase as additional solutions come online to address efficiency in urban sub-systems. With increase in need of building smart IoT applications the number silo applications is also increasing. Sensor and device data can be overwhelming because of its volume, connectivity methods, data format, and frequency. The only way to standardize and cope with all the data generated by connecting billions of unconnected objects is by having a smart city platform with a robust software base able to function at scale and at speed to compute and analyze various data sources and formats. Such a platform includes the flexibility to allow relatively easy integration with other cloud enterprise applications and be fully integrated with the rest of the information technology (IT) infrastructure.

[0004] The smart city IoT technologies accesses multiple data streams from heterogeneous city sub-systems. There is a need to reduce dependence on underlying silos by providing for a standard model in which sensors and devices may be stored in structured formats which are standardized for capturing data from heterogeneous devices and sensors. There is a need for an information platform which is populated with data from discrete sources which includes predominantly IoT data, enterprise data and crowd sourced data and from which data is taken to multiple destinations. These data structures designed and created by keeping PAS 182 as a reference is capable of storing contextually and business enriched multi-vendor, multi-sensor/device IoT data. This correlated and enriched data will facilitate faster development of smart and intelligent applications and bring out meaningful insights. PAS 182 specifies a conceptual model to allow the interoperability of systems and data transmission between different institutions. The standard creates a framework for Smart Cities that allows information sharing across organizations and people at multiple levels, the derivation of data and the observation of decisions in operational data. The traditional systems and methods have provided for an API Proxy to provide a unified API to the catalogue of resources available on a smart city hub and aggregation of data from diverse data sources and push them into the hub to make them available to IoT developers. However, none of the traditional systems and methods have provided for a unified platform which uses PAS 182 as reference and can provide a structured data to be accessed by the users.

SUMMARY

[0005] The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below.

[0006] Systems and methods of the present disclosure enable managing Internet of Things (IoT) data for a smart city using an IoT datahub. In an embodiment of the present disclosure, there is provided a method for managing Internet of Things (IoT) data for a smart city using an IoT datahub, the method comprising: obtaining, by one or more hardware processors, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub; performing, using a data integration layer, a transformation of the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns to be integrated for correlation; based upon the one or more transformed sets of data performing: (i) clustering, by the one or more hardware processors, the one or more transformed sets of data to obtain one or more sets of contextually grouped data; and (ii) correlating, by the one or more hardware processors, the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns; aggregating, by the one or more hardware processors, the one or more sets of correlated data for managing the IoT data; correlating the one or more sets of contextually grouped data by optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data, optimizing the one or more sets of contextually grouped data by performing, by the one or more hardware processors, a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance values of the correlated data; managing the IoT data by sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub; and performing, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data.

[0007] In an embodiment of the present disclosure, there is provided a system for managing Internet of Things (IoT) data for a smart city using an IoT datahub, the system comprising one or more processors; one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to: obtain, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub; perform, using a data integration layer, a transformation of the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns to be integrated for correlation; based upon the one or more transformed sets of data perform: (i) clustering, the one or more transformed sets of data to obtain one or more sets of contextually grouped data; and (ii) correlating, the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns; aggregate, the one or more sets of correlated data for managing the IoT data; correlate the one or more sets of contextually grouped data by optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data; optimize the one or more sets of contextually grouped data by performing a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance value of the correlated data; and manage the IoT data by sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub; and perform, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data.

[0008] In yet another embodiment, one or more non-transitory machine readable information storage mediums comprising one or more instructions is provided. The one or more instructions when executed by one or more hardware processors causes the one or more hardware processors to perform a method for managing Internet of Things (IoT) data for a smart city using an IoT datahub, said method comprising: obtaining, by the one or more hardware processors, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub; performing, using a data integration layer, a transformation of the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns to be integrated for correlation; based upon the one or more transformed sets of data performing: (i) clustering, by the one or more hardware processors, the one or more transformed sets of data to obtain one or more sets of contextually grouped data; and (ii) correlating, by the one or more hardware processors, the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns; aggregating, by the one or more hardware processors, the one or more sets of correlated data for managing the IoT data; correlating the one or more sets of contextually grouped data by optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data; optimizing the one or more sets of contextually grouped data by performing, by the one or more hardware processors, a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance values of the correlated data; sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub; and performing, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data. [0009] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:

[0011] Fig. 1 illustrates a block diagram of a system for managing Internet of Things

(IoT) data for a smart city using an IoT datahub according to an embodiment of the present disclosure;

[0012] Fig. 2 is an architecture illustrating the components of a system for managing Internet of Things (IoT) data for a smart city using an IoT datahub according to an embodiment of the present disclosure; and

[0013] Fig. 3 is a flowchart illustrating the steps involved for managing Internet of Things (IoT) data for a smart city using an IoT datahub according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0015] The embodiments of the present disclosure provide systems and methods for managing Internet of Things (IoT) data for a smart city using an IoT datahub. Smart cities may comprise of complex environments where several areas of innovation meet in order to substantially improve socioeconomic development and quality of life. Managing smart cities data may comprise, inter-alia, of employing IoT based devices and methods to handle big data, digital data and others. The IoT based devices typically gather data and stream it over the Internet to a central source, where it is analyzed and processed. The capabilities of devices connected to the IoT platform continue to advance by combining data into more useful information. Rather than just reporting raw data, connected devices are required to send higher-level information back to machines, computers, and people for further evaluation and decision making. This transformation from data to structured data is important because it will allow us to make faster, more intelligent decisions, as well as control our environment more effectively.

[0016] Smart city IoT technologies require access to multiple data streams from heterogeneous city sub-systems. There is a need to reduce dependence on underlying silos by providing for a structured model in which sensors and devices may be stored in structured formats which are standardized for capturing data from heterogeneous devices and sensors. There is a need for an information platform which is populated with data from discrete sources which includes predominantly the IoT data, enterprise data and crowd sourced data and from which data is taken to multiple destinations. The traditional systems and methods have provided for an API proxy to provide a unified API to the catalogue of resources available on a smart city hub and aggregation of data from diverse data sources and push them into the hub to make them available to IoT developers. However, none of the traditional systems and methods have provided for a unified platform which follows PAS 182 as reference and can provide a structured data to be accessed by the users.

[0017] Referring now to the drawings, and more particularly to FIGS. 1 through FIGS. 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

[0018] FIG. 1 illustrates an exemplary block diagram of a system 100 managing Internet of Things (IoT) data for a smart city using an IoT datahub. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.

[0019] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.

[0020] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0021] According to an embodiment of the present disclosure, referring to FIG. 2, the architecture and components of the IoT datahub providing a system for managing Internet of Things (IoT) data for a smart city using an IoT datahub may now be considered in detail. The IoT datahub comprises of a data integration layer 202 for performing a data transformation on the data received from multiple sensors or devices. An application programming interface (API) 203 shares contextually clustered data to generate a structured set of data to be accessed by the users connected to the IoT datahub. The API 203 may, inter-alia, support, for example, queries by other system in response to which it supplies data in accordance with the query details. The APIs 203 may also be used to define the communications and interoperability between modules of a single system. The API 203 may comprise of multiple APIs, where each of these APIs may comprise of a raw data, the one or more sets of contextually grouped or the one or more sets of contextually grouped correlated data to be shared with the user users connected to the IoT datahub. The API 203 may be Representational State Transfer (REST) APIs with JavaScript Notation Object (JSON) as input / output exposed over a secured Hypertext transfer protocol (HTTPs). A complex event processing engine 204 assists in combining data from multiple sources to suggest patterns or events (for example, a medicine alert if pulse rate crosses 60 per minute).

[0022] FIG. 3, with reference to FIGS. 1 and 2, illustrates an exemplary flow diagram of a method for managing Internet of Things (IoT) data for a smart city using an IoT datahub. In an embodiment the system 100 comprises one or more data storage devices of the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIG. 1 and the flow diagram. In the embodiments of the present disclosure, the hardware processors 104 when configured the instructions performs one or more methodologies described herein. [0023] In an embodiment of the present disclosure, at step 301, the one or more hardware processors 104 obtain a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub. The plurality of devices (that may be connected to the IoT datahub) may comprise of sensors, smartphones, smart watches, tablets, smart televisions, computers, laptops, smart home system, smart accessories, networked appliances or devices or other devices for monitoring or interacting with or for people and/or places, or any combination thereof. The first set of data may comprise of measurements, user commands, user-reported status updates, raw data extracted from an online or virtual data source, such as a geo-location map, a social network, a calendar, a media network, data from smart meters for electricity, gas and water and the smart grids they create, data from high-value crops which can be monitored by wireless sensors for a range of parameters (air temperature, humidity, soil temperature, soil moisture, leaf wetness, atmospheric pressure, solar radiation, trunk/stem/fruit diameter, wind speed and direction, and rainfall), with real-time data gathered by an on-site gateway, sent to the IoT datahub and accessed via internet-connected personal computers or smartphones, or any other data captured via smart sensors or any combination of the data received from the plurality of devices thereof. It may be noted that the first set of data that may be obtained from the plurality of devices but is not limited to smart data only. The first set of data may comprise of the data obtained from a plurality of other sources (other than smart sensors or devices) like payroll data, clinical data etc. The first set of data may further comprise of a metadata. For example, metadata may be document data about data elements or attributes, (name, size, data type, etc.) and data about records or data structures (length, fields, columns, etc.) and data about data (where it is located, how it is associated, ownership, etc.).

[0024] In an embodiment of the present disclosure, at step 302, the one or more hardware processors 104, using a data integration layer 202, transform the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns and units to be integrated for correlation. The data integration may comprise, inter-alia, of combining or integrating the first set of data received from the plurality of devices in the step 301 and transforming it to the one or more transformed sets of data in such a way that it may be used for correlation. For example, if a location sensor informs that the person is in the bedroom, a bed presence sensor informs (or senses) that he is in the bed. Room temperature generates an alert that air conditioner (AC) is on and the smart meter provides the power consumption. A smart meter reader of the AC vendor can give data of the person and date of purchase of the AC. The smart meter data, the temperature sensor data and the location sensors data may be integrated using the data integration layer 202 to obtain data sets pertaining to optimum hours for keeping the AC on and thus facilitate a personalized environment of the person.

[0025] According to an embodiment of the present disclosure, the one or more hardware processors 104, transform the first set of data such that the one or more transformed sets of data comprises uniformed integrated data patterns and units. This may now be understood in detail by an example. Suppose, the first set of data is received (from a plurality of devices or sensors etc.) comprise of a humidity sensor, motion sensor and a temperature sensor. Each of these sensors may comprise of their own observation patterns and units. The data integration layer 202 will collect and integrate all the observation patterns and units of the humidity sensor, the motion sensor and the temperature sensor into one group (comprising of an integrated data with respect to the observation patterns and units), for example, homeAC optimization. The user may then expose the integrated data by an application programming interface (API) 203. The integrated data may then be used, for example, to check one or more values to select temperature setting of AC or to switch off or switch on. Therefore, using the integrated data, the one or more transformed sets of data may be obtained. Hence, the first set of data may be integrated and translated as:

HUMIDITY 80% or more, TEMPERATURE - 35°Celsius or more, FAN

MOTION - OFF, SWITCH AC ON.

[0026] According to an embodiment, the data integration layer 202 is capable of handling very high volumes of data (e.g., "big data"). For example, the data integration layer 202 may (as discussed above) frequently process interval data from millions or more of sensors and meters (e.g., digital sensors and meters). Further, the integration can be carried out in an asynchronous batch or real-time mode. The data integrator layer 202 may also incorporate real-time and batch data. As just one example, with respect to the energy industry and the utilities sector in particular, such real-time and batch data can come from, for example, utility systems, building characteristic systems, utility energy conservation measures and rebate databases, and industry-standard benchmark systems etc. Also, the data integration layer 202 may perform initial data validation. The data integration layer 202 may examine the structure of the incoming data to ensure that required fields are present and that the data is of the right data kind and format. For example, the data integration layer 202 may recognize when the format of the provided data does not match the expected format (e.g., a number value is erroneously provided as text), prevents the mismatched data from being loaded, and logs the issue for review and investigation. It may be noted that the embodiments of the present disclosure does not restrict the data integration layer 202 to perform integrating and transforming the first set of data only and restricted to the above format. The data integration layer 202 may perform various other functions (for example, importing and monitoring) on the first set of data received and may generate the transformed sets of data in any other format or using any combinations of the information / data thereof.

[0027] According to an embodiment of the present disclosure, at step 303, the one or more hardware processors 104 cluster the one or more transformed sets of data for contextually grouping the one or more transformed sets of data to obtain one or more sets of contextually grouped data. For example, a patient's pulse reading data when he is at office, at home or in the park may be grouped or clustered to check when he is more active. Here location is the context. The contextual data grouping increases productivity and social relationships for individuals. Moreover, the contextual data grouping and analysis may be used for improving services for corporations by providing, for example, targeted advertising and/or location-based services. The embodiments of the present disclosure facilitate mapping of the one or more transformed sets of data for contextually grouping the one or more transformed sets of data to obtain the one or more sets of contextually grouped data. This may now be understood with the help of an example. Suppose, a citizen's power consumption is being recorded using a smart meter which is being tracked by the citizen and also by an AC dealer. The power consumption data may comprise, inter- alia, of location, bill amount, units, power distributor, optimum usage indications and over-power user alerts etc.

[0028] The data may then be integrated and transformed for obtaining the one or more transformed sets of data. Further, the one or more transformed data sets may then be contextually grouped (for example, according to the bill amount) which can benefit both the citizen and the dealer. For the dealer, the one or more contextually grouped data sets may include a set of dealer cost models (with respect to various ACs) corresponding to the one or more of the data sets resulting from the contextual grouping. An average price ratio (for example, price paid/dealer cost) model for the one or more data sets may also be generated using the obtained data. Further, the one or more contextually grouped data sets may help the dealer to monetize the data or advantages through the various access and distribution channels, including utilizing a provided web site, distributed widgets, data, the results of data analysis, etc. For example, monetization may be achieved using ACs (model, finance options, insurance, etc.) related advertising where the dealer of the AC may sell display ads, contextual links, sponsorships, etc. to ACs related advertisers, for example, regional marketing groups, dealers, finance companies or insurance providers. Similarly, the citizen may optimize his expenses without compromising on any comfort as he has the one or more contextually grouped data sets based upon the bill amount. Further, the citizen can select a best AC as he has the contextually grouped data. The contextual grouping may also be helpful for other purposes for example, for creating histograms of data at multiple levels. "Good," "great," or other prices and corresponding price ranges may also be determined based on median or floor pricing or algorithmically determined dividers (for example, between the "good," "great," or "overpriced" ranges). These prices or price ranges may be based on statistical information determined from the one or more contextually grouped data sets corresponding to the specified AC. For example, "good" and "great" prices or price ranges may be based on a number of standard deviations from a mean price associated with the sales transactions of the one or more contextually grouped data sets corresponding to the specified AC. For example, a "great" price range may be any price which is more than one half a standard deviation below the mean price, while a "good" price range may be any price which is between the mean price and one half standard deviation below the mean. An "overpriced" range may be anything above the average price or the mean or may be any price which is above the "good" price range.

[0029] According to an embodiment of the present disclosure, at step 303, the one or more hardware processors 104 further correlate the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns and units. The correlation of the contextually grouped data provides various advantages which, inter-alia, may comprise of understanding the relationship between the one or more correlated sets of data, performing normalization and comparison of the one or more correlated sets of data. Data correlation offers an intelligent way of associating a portion of the datasets with another portion of the datasets. The data correlation may be based on time synchronization, shared social relation (e.g., devices are owned by user accounts in the same social group), shared data dimension (e.g., both devices measures weight), shared data source profile (e.g., location or device-type, etc.), data owner profile (e.g., user profile or user configurations), shared known semantic (e.g., both devices are considered "kitchenware"), shared known context (e.g., both devices are operated in the context of exercising), or any combination thereof. For example, the one or more contextually grouped data sets on exercising patterns of a person may be correlated against pulse rate data of the person.

[0030] According to an embodiment of the present disclosure, an example of correlating the one or more contextually grouped sets of data may now be considered. Suppose, a set of data comprising glucose level and one or more exercising patterns of a person is obtained from a smartphone. The set of data may be integrated and transformed as:

MOTION: JUMPING

PULSE RATE: HIGH

BLOOD PRESSURE: 210/120 mmHg (millimeter of mercury)

HEART BEAT: 90 per minute

GLUCOSE LEVEL: 60 mg/DL (milligrams per deciliter)

The set of data may then be correlated against a threshold value set by a doctor who has also prescribed a medicine for the blood pressure for the person. Suppose, the threshold values set by the doctor 130/90 mmHg for the blood pressure and 90 mg/DL for the glucose level. The integrated and transformed set of data may be correlated against the prescribed threshold of 130/90 mmHg, which means that the system 100 through the one or more hardware processors 104 may generate an alert "too high blood pressure while exercising" or "high activity level leads to sudden drop in glucose level." This correlative alert may further be used to generate interoperable rules to notify the user to stop exercising after a certain activity level is reached in order to avoid sudden drops in glucose level. Further, the one or more correlated sets of data may compared with other persons (like a person of similar age and similar medical problems) to guide them with useful exercising tips since the person is administered with blood pressure medicine and based on his body vitals has one or more set patterns.

[0031] According to an embodiment of the present disclosure, the step of correlating the one or more sets of contextually grouped data further comprises optimizing the one or more sets of contextually grouped data by at least one value enhancing process by performing a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets. The present disclosure thus facilitates an easy onboarding of sensors or devices as objects, its type, observation pattern, location, agent etc. facilitate fast realization of smart solutions eliminating dependency to many vendors. This may be understood by referring to the example at step 303 again. Suppose, a citizen's power consumption is being recorded using a smart meter which is being tracked by the citizen and also by an AC dealer. The power consumption data may comprise, inter-alia, of location, bill amount, units, power distributor, optimum usage indications and over-power user alerts etc. The data may then be integrated and transformed for obtaining the one or more transformed sets of data. Further, the one or more transformed data sets may then be contextually grouped (for example, according to the bill amount) which can benefit both the citizen and the dealer. As explained above, the one or more contextually grouped data sets may help the dealer to monetize the data or advantages through the various access and distribution channels, including utilizing a provided web site, distributed widgets, data, the results of data analysis, etc.

[0032] The contextually grouped data may further be value enhanced by integrating with various other data values or information. For example, monetization may be achieved using ACs (model, finance options, insurance, etc.) related advertising where the dealer of the ACs may sell display ads, contextual links, sponsorships, etc. to ACs related advertisers, for example, regional marketing groups, dealers, finance companies or insurance providers. The monetized data may further be again value enhanced and optimized by matching and/or comparing with the contextually correlated data (for example, correlating and adding data values pertaining to the ACs performance in humid conditions on in particular months) for creating histograms of data at multiple levels. For example, in addition to the "good," "great," or other prices and corresponding price ranges determined in the step 303 above, the user or the customer can have the "best" option in the "city" available. These prices or price ranges may be again be derived based on statistical information determined from the one or more contextually grouped data sets corresponding to the specified AC and may be used by the users for making purchase or replacement related decisions.

[0033] According to an embodiment of the present disclosure, at step 304, the one or more hardware processors 104 aggregate the one or more sets of correlated data for generating aggregated data sets (comprising of a structured data) for managing the IoT data. The present disclosure provides an advantage over the traditional systems and methods as it provides the flexibility of aggregating the one or more sets of correlated data (that is the transformed and the contextually grouped correlated IoT data obtained in the previous step) for generating the structured IoT data which may be used in multiple ways by the users (for example, for generating and prioritizing alerts for his medicine routines based upon benchmarks set by obtaining a structured data set from the aggregated data). For example, a heartbeat rate data record of a patient may be correlated with a pulse monitor data record because of a shared semantic and context of health related data. However, these health related data may be aggregated further into a cluster for other non-health-related activities on the same day because the relevant grouping of the cluster pertains to the activities of the day.

[0034] According to an embodiment, the aggregated data sets may be leveraged inter- alia, to improve the IoT data operation performance during data collection, data storage, data processing, and/or data querying. The IoT data aggregation comprises processors, operations, or functions for redefining IoT related data such that the IoT data can be searched, processed, analyzed, or otherwise used, for example, more efficiently. Further, the data aggregation may be classified into various data aggregation types. Example of the data aggregation types, presented by way of example and without limitation, include intra-stream data aggregation, inter-stream data aggregation, and application-level data aggregation. Intra-stream data aggregation may refer to combining (aggregating) data items within the same data stream. For example, multiple data items that are generated from the same sensor may be aggregated with each other during an example intra-stream data aggregation. Inter-stream data aggregation may refer to combining (aggregating) data items from different data streams. Application-Level data aggregation may comprise aggregation of application-level messages (e.g., request and/or response messages at the IoT service layer).

[0035] The aggregation of the one or more sets of correlated data for generating the aggregated data sets (comprising of a structured data) for managing the IoT data may be explained with the help of below example. A patient's data is obtained from multiple sensors (such as the PIR sensor or a bed sensor) indicating various kinds of information and data values (in multiple patterns and units) for example, pulse rate, values associated with the heart beats. The data values may further include fasting blood glucose levels, and hemoglobin levels. This information and the data values of the patients generated multiple times during the day may be required for different time intervals by the doctors. Previously, the information and the data values could only be generated using the data from a database. Even then, the data values were distributed among different data models. In order to generate the information and data values, different queries directed to the different data models were generated to retrieve the needed information and there is a time lag (for example, 24 to 48 hours) when the information and data values are updated, refreshed and retrieved.

[0036] The present invention aggregates the information and data values from the multiple sensors and the data may be structured into the structured data sets. For instance, the pulse rates and the heartbeat rates for the patient may be received from the multiple sensors which collected the data values at home. Additional pulse rate and the heartbeat rates may be received from the patient's hospital. Each set of data may be structured in different formats. Further, the one or more correlated sets of data obtained by comparing the data values against the threshold set by a doctor (for example, a blood pressure range) may be aggregated. Thus the embodiments of the present disclosure support complex event processing. The one or more hardware processors 104 may by using complex event processing engine 204 perform complex event processing.

For example, the aggregated data sets comprising of the structured data may be generated as follows:

First data set- PATIENT NAME: XYZ

DATA VALUES COLLECTED FOR: CARDIOLOGY PULSE RATE: 90 per minute HEART BEATS: 100 per minute COLLECTION SOURCE: SENSORS

COLLETION PLACE AND TIME: HOME, 0500 hours, 1000 hours, 1700 hours and 2300 hours

OPTIMAL PULSE RATE AS PER DOCTOR'S OPINION: 90 per minute

DOCTOR' S ADVISE: BED REST

Second data set-

PATIENT NAME: XYZ

DATA VALUES COLLECTED FOR: ANATOMY AND CARDIOLOGY\ PULSE RATE: 80 per minute BLOOD PRESSURE: 90/90 mmHg COLLECTION SOURCE: HOSPITAL VISIT

COLLETION PLACE AND TIME: HOSPITAL 1100 hours and 2100 hours

OPTIMAL BLOOD PRESSURE AS PER DOCTOR'S OPINION: 120/80 mmHg

DOCTOR' S ADVISE: WALKING. [0037] According to an embodiment of the present disclosure, the step of managing the IoT data comprises sharing the one or more sets of contextually clustered correlated data by an application programming interface (API) 203 to generate a structured set of data to be accessed by the users connected to the IoT datahub. In the traditional systems and methods API may typically comprise of an interface (or interfaces) provided by one software module to other modules, typically built for the function of distributing data. The API may, inter-alia, support, for example, queries by other system in response to which it supplies data in accordance with the query details. The API may also be used to define the communications and interoperability between modules of a single system. However, the embodiments of the present disclosure apart from providing for the normal API functions, further facilitate integrating multiple APIs, where each of these APIs 203 may comprise of a raw data, the one or more sets of contextually grouped or the one or more sets of contextually grouped correlated data to be shared with the user users connected to the IoT datahub.

[0038] The APIs 203 may be Representational State Transfer (REST) APIs with JavaScript Notation Object (JSON) as input / output exposed over a secured Hypertext transfer protocol (HTTPs). For example, one of the API 203 may simply share raw data to the user like energy consumption data for ten days from a smart meter or patient's pulse reading for the last one hour to a doctor. The other API 203 may share the one or more set of contextually grouped data like a user's pulse reading while he is in a park or in an office. Here location is the context. Similarly, another API 203 may share the one or more contextually correlated or business enriched data sets, for example, the API 203 communicate to a vendor a list of customer whose AC needs maintenance or replacement. Here the AC list, power consumption, customer, vendor details etc. may get contextually correlated to obtain the one or more sets of contextually correlated structured data (as explained in the step 303 above) for communicating to the users via the API 203. Similarly taking another example, if a doctor wants to know if his patients are active today, the API 203 may share the one or more set of contextually correlated structured data comprising of patients movement patterns, physiological reading, activity patterns etc. and based upon the one or more set of contextually correlated structured data the doctor may decide on the status of the patient.

[0039] The technical advantages of the present disclosure and it's comparison with the traditional systems and methods may now be considered in detail. The embodiments of the present disclosure facilitate integrated and unified IoT model that is, performing the transformation, clustering, correlating and aggregating and generating of the structured data referring PAS 182 model as well. The PAS 182 is aimed at organizations that provide services to communities in cities, and manage the resulting data, as well as decision-makers and policy developers in cities. PAS 182 was established to enable interoperability between silo capabilities. None of the traditional systems and methods has been able to so far implement PAS 182 or present an integrated and unified IoT model referring PAS 182. Further, it may be noted that the embodiments of the present disclosure facilitate the IoT related data from the plurality of sensors and devices to be stored in structured formats which are standardized for capturing data from heterogeneous devices and sensors. This data model/structure of the datahub is thus novel and built referring PAS 182 interoperability standards. Further, the present disclosure confirms to the HyperCat standards.

[0040] The present disclosure removes dependency on the data silos. The traditional systems and methods assume that produced data is managed by an application and limited to its original application. The data may remain mostly isolated and restricted to certain application areas, data centers, organizations, or only accessible in a specific city. However, the smart city data sources must provide for an aggregated data that can be released for use by other stakeholders or third parties (by following data security and integrity standards). The present disclosure removes the dependency on the underlying silos to ensure seamless orchestration of information from various sources, which includes static, virtualization and streaming for facilitating correlations of contextual and business enriched data sets supporting collaboration. This may be understood with the help of below example. Environmental sensors generating information on humidity, temperature and wind flow direction of a location between 4 PM and 6 PM. The capability is enabled by a vendor ABC. They store the information in a format which is customized for their requirements (for example degrees). Another vendor XYZ is enabling assisted living capability, monitoring elderly's movement and sleep pattern with help of motion and bed presence sensors. And the vendor MNP capture fitness parameters like step count, pulse with the help of a wearable. A care giving organization like hospital EFG would like to monitor the sleeping pattern compared against various parameters. This is a multi-vendor, multi technology silo capability scenario. For any type of collaboration, we need to enable all party concurrence and it may also impact the entire set of vendors who has hosted these capabilities. The present disclosure facilitates leveraging the information generated and stored in a pattern by multiple vendors in the IoT datahub, so that the information flow and collaboration is easy to be produce the structured sets of data to be shared to the users or other interested parties via the API 203. Since the information exchange and storage format are standardized by the present disclosure, the users and the interested parties (like third party vendors) can discover the information, correlate and build actionable insights. In the above case, comparing against, the environmental parameters, fitness information while jogging, his/her sleeping pattern can derived, for example, sound sleep when the environment is conducive and 10000 steps in jogging.

[0041] The embodiments of the present disclosure further support multi-tenancy. The multi-tenant system may comprise of the systems in which various elements of hardware and software of the database system may be shared by one or more tenants. For example, a given application server may simultaneously process requests for a great number of tenants, and a given database table may store rows for multiple tenants. A tenant may comprise of entities or a group of users who share a common access with specific privileges to the software. Each respective user within the multi-tenant system is thus associated with, assigned to, or otherwise belongs to a particular tenant of the plurality of tenants supported by the multi- tenant system. The multi-tenant architecture needs to set a shared functionality under an access control mechanism. In this sense, an access rights list for each type user should be provided. Because of IoT devices versatility, the handled functionality must be correctly defined, according to the user's usage. For example, the physical access control to a specific room can be prioritized. A multi-tenant solution for the smart city IoT data should offer to set which object' functionalities are under control, and exactly which commands have priority for each functionality. Although multiple tenants may share access to the server and the database, the particular data and services provided from the server to each tenant can be securely isolated from those provided to other tenants.

[0042] The multi-tenant architecture therefore allows different sets of users to share functionality and hardware resources without necessarily sharing any of the data belonging to or otherwise associated with other tenants. Each tenant can be configured in the platform and the data is isolated tenant wise. The system does not need any change, alteration of customization. New tenant (customer) can be on-boarded on the fly. Each tenant will have their own set of users, groups, configurations, master data, set of rules, alerts which are completely isolated and invisible to other tenants. For example, in the step 304 above, the first set of aggregated data may be used by a cardiologist, who may set his alerts or rules for guiding his patient (for example, prescribing a new medicine when pulse rate goes down) and the first set of aggregated data remains isolated from others. Similarly, the second set of aggregated data may be used by a physician, which remains isolated from the cardiologist.

[0043] The embodiments of the present disclosure facilitate supporting PaaS (platform-as-a-service) and SaaS (software-as-a-service) models. PaaS offerings typically facilitate deployment of web applications without the cost and complexity of buying and managing the underlying hardware and software and provisioning hosting capabilities, providing all of the facilities required to support the complete life cycle of building and delivering web application and service entirely available from the internet (for example Google App Engine™), while the SaaS platform allows developers to provide software solutions via the mediator server directly to customers, and ensures data availability and data security (for example Google Apps™). An example of how the present disclosure facilitate the PaaS model may now be considered. The present integrated IoT platform may be hosted on any cloud platform. Each customer access the platform from the cloud URL, which is provided and controlled by the owner. Being multi-tenant, the platform need not be replicated or redeployed for each and every customer or moved to different cloud location. The same platform would serve multiple customer. It is also deployed on an elastic infrastructure, which scales accordingly.

[0044] An example of how the present disclosure facilitates the SaaS model may now be considered. Suppose there are three customers i.e. A, B and C. All these customers will be accessing the same resource (weblink). They may not mention the name of the tenant. Each customer will log in to the portal with their own credentials. Once they log in, they will see the application in their perspective, i.e. customer A will be see only his sensors, residents , reports etc. whereas customer B will see its own set of data. The data, reports, rules, algorithms, settings will not be visible for the others. Here A, B and C are tenants using the same application, which projects multi-tenancy. And, the platform is not deployed in the customer premise or private cloud. It is available a central accessible platform accessible through a common address.

[0045] It may be noted that the output of all the steps performed above (that is, steps 301 to 302) for example, the first set of information, the one or more transformed sets of data, the one or more sets of contextually correlated data, the aggregated data etc. gets stored in the memory 102 of the system 100.

[0046] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims. [0047] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application- specific integrated circuit (ASIC), a field- programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

[0048] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0049] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0050] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term "computer-readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, BLU-RAYs, flash drives, disks, and any other known physical storage media.

[0051] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.

Claims

WE CLAIM:

1. A method for managing Internet of Things (IoT) data for a smart city using an IoT datahub, the method comprising a processor implemented steps of:

obtaining, by one or more hardware processors, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub;

performing, using a data integration layer, a transformation of the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns to be integrated for correlation;

based upon the one or more transformed sets of data performing:

clustering, by the one or more hardware processors, the one or more transformed sets of data to obtain one or more sets of contextually grouped data; and correlating, by the one or more hardware processors, the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns; and

aggregating, by the one or more hardware processors, the one or more sets of correlated data for managing the IoT data.

2. The processor implemented method of claim 1, wherein the step of correlating the one or more sets of contextually grouped data further comprises optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data.

3. The processor implemented method of claim 2, wherein the step of optimizing the one or more sets of contextually grouped data comprises performing, by the one or more hardware processors, a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance values of the correlated data.

4. The processor implemented method of claim 1, wherein the step of managing the IoT data comprises sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub.

5. The processor implemented method of claim 1, wherein the step of clustering the one or more transformed sets of data is preceded by performing, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data.

6. A system comprising:

a memory storing instructions;

one or more communication interfaces; and

one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:

obtain, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub;

perform, using a data integration layer, a transformation of the first set of data for generating one or more transformed sets of data, wherein the one or more transformed sets of data comprises data patterns to be integrated for correlation;

based upon the one or more transformed sets of data perform:

clustering, the one or more transformed sets of data to obtain one or more sets of contextually grouped data; and

correlating, the one or more sets of contextually grouped data to obtain one or more sets of correlated data, wherein the one or more sets of correlated data comprises a plurality of data records grouped in accordance with the data patterns; and

aggregate, the one or more sets of correlated data for managing the IoT data.

7. The system of claim 4, wherein the one or more hardware processors are further configured to correlate the one or more sets of contextually grouped data by optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data.

8. The system of claim 5, wherein the one or more hardware processors are further configured to optimize the one or more sets of contextually grouped data by performing a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance value of the correlated data.

9. The system of claim 4, wherein the one or more hardware processors are further configured to manage the IoT data by sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub.

10. The system of claim 4, wherein the one more hardware processors are further configured to perform, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data.

11. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes the one or more hardware processor to perform a method for managing Internet of Things (IoT) data for a smart city using an IoT datahub, said method comprising: obtaining, by the one or more hardware processors, a first set of data comprising IoT related information extracted from a plurality of devices connected to the IoT datahub;

based upon the one or more transformed sets of data performing:

12. The one or more non-transitory machine readable information storage mediums of claim 11, wherein the step of correlating the one or more sets of contextually grouped data further comprises optimizing the one or more sets of contextually grouped data by at least one value enhancing process for managing the IoT data.

13. The one or more non-transitory machine readable information storage mediums of claim 11, wherein the step of optimizing the one or more sets of contextually grouped data comprises performing, by the one or more hardware processors, a comparison of the one or more sets of contextually grouped data with similar contextually grouped data sets based upon one or more similar contextual data values to enhance values of the correlated data.

14. The one or more non-transitory machine readable information storage mediums of claim 11, wherein the step of managing the IoT data comprises sharing the one or more sets of contextually grouped correlated data by an application programming interface (API) for generating a structured set of data to be accessed by a plurality of users connected to the IoT datahub.

15. The one or more non-transitory machine readable information storage mediums of claim 11, wherein the step of clustering the one or more transformed sets of data is preceded by performing, by the data integration layer, a validation of the one or more sets of contextually grouped data for analyzing one or more structured sets of input data.