Claims:STATEMENT OF CLAIMS

We claim
1. A method for managing data received from at least one Internet of Things (IoT) device (301), the method comprising
comparing a timestamp in a new received data with a timestamp of previous received data to check if a time threshold has crossed by a summarizing engine (302); and
running at least one summarizer by the summarizing engine (302) if the time threshold has crossed, wherein the summarizer generates synthetic data which summarizes the new received data for the time period.
2. The method, as claimed in claim 1, wherein the method further comprises the summarizer can be run by the summarizing engine (302) based on at least one of user preferences; and configured parameters.
3. The method, as claimed in claim 1, wherein the received data can be at least one of a data; and a media.
4. The method, as claimed in claim 3, wherein the method further comprises
determining at least one frame with at least one pre-defined activity by the summarization engine (302), if the received media is media; and
extracting the at least one determined frame by the summarization engine (301).
5. The method, as claimed in claim 1, wherein running at least one summarizer further comprises
performing statistical analytics by the summarizing engine (302) on the new received data for the time period;
performing media analytics by the summarizing engine (302) on the new received data for that time period; and
generating synthetic data by the summarizing engine (302) from the analytics.
6. The method, as claimed in claim 5, wherein generating the synthetic data comprises statistically summarizing the received data during the time period by the summarizing engine (302), using at least one of count, mean, median, mode, standard deviation and sum of all the values during the time period.
7. The method, as claimed in claim 1, wherein the method further comprises providing at least one notification to at least one user, based on the generated synthetic data, by the summarizing engine (302).
8. A system comprising of a summarization engine (302) connected to at least one Internet of Things (IoT) device (301), at summarization engine (302) configured for
comparing a timestamp in a new received data with a timestamp of previous received data to check if a time threshold has crossed; and
running at least one summarizer if the time threshold has crossed, wherein the summarizer generates synthetic data which summarizes the new received data for the time period.
9. The system, as claimed in claim 8, wherein the summarization engine (302) is further configured for running the summarizer based on at least one of user preferences; and configured parameters.
10. The system, as claimed in claim 8, wherein the received data can be at least one of a data; and a media.
11. The system, as claimed in claim 10, wherein the summarization engine (302) is further configured for
determining at least one frame with at least one pre-defined activity, if the received media is media; and
extracting the at least one determined frame.
12. The system, as claimed in claim 8, wherein the summarization engine (302) is further configured for running the at least one summarizer by
performing statistical analytics on the new received data for the time period;
performing media analytics on the new received data for that time period; and
generating synthetic data from the analytics.
13. The system, as claimed in claim 12, wherein the summarization engine (302) is further configured for generating the synthetic data by statistically summarizing the received data during the time period using at least one of count, mean, median, mode, standard deviation and sum of all the values during the time period.
14. The system, as claimed in claim 8, wherein the summarization engine (302) is further configured for providing at least one notification to at least one user, based on the generated synthetic data.

Dated: 24th May 2017 Signature:

Name of Signatory: Somashekar Ramakrishna
, Description:TECHNICAL FIELD
[001] Embodiments herein relate to Internet of Things (IoT), and more particularly to a method and system for summarization of IoT events.

BACKGROUND
[002] A typical IoT system comprises of a large number of sensors and actuators connected to a cloud network. The sheer number of events, generated by a network of IoT devices, can be very large and difficult to manage. However, methods are not available to carry out practical analysis on the raw events received from the IoT devices.
[003] Current solutions merely attempt to reduce the amount of collected data by collecting data only at pre-defined intervals or on pre-defined events occurring. So, a user has access to either the complete data or discrete sets of data, as collected. However, this may result in data being missed and poor characterization of the data.
OBJECTS
[004] The principal object of embodiments herein is to provide methods and systems for summarization of events based on data collected from at least one Internet of Things (IoT) device, wherein the data can comprise of at least one of raw data, media, and so on.
[005] Another object of the embodiments herein is to provide systems, which can receive large number of events generated by IoT devices, and summarize the received events.
[006] A further object of the embodiments herein is to generate summarized data that can be used for further statistical analysis.

BRIEF DESCRIPTION OF FIGURES
[007] Embodiments herein are illustrated in the accompanying drawings, through out which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
[008] FIG. 1 illustrates an example classification of IoT devices based on capability, according to embodiments as disclosed herein;
[009] FIG. 2 illustrates classification of measures, according to embodiments as disclosed herein;
[0010] FIG. 3 illustrates a system for summarizing data received from at least one IoT device, according to embodiments as disclosed herein;
[0011] FIG. 4 is a flowchart depicting the process of the summarization engine managing data, according to embodiments as disclosed herein;
[0012] FIG. 5 is an example flowchart depicting the functionality performed by an hourly summarizer, according to embodiments as disclosed herein;
[0013] FIG. 5b illustrates an example scenario, depicting the functionality performed by a daily summarizer after the type of the event is classified, according to embodiments as disclosed herein;
[0014] FIG. 6 is an example flowchart depicting the functionality performed by a daily summarizer, according to embodiments as disclosed herein;
[0015] FIG. 7 is an example flowchart depicting the functionality performed by a weekly summarizer, according to embodiments as disclosed herein;
[0016] FIG. 8 is an example flowchart depicting the functionality performed by a monthly summarizer, according to embodiments as disclosed herein; and
[0017] FIG. 9 is an example flowchart depicting the functionality performed by a yearly summarizer, according to embodiments as disclosed herein.

DETAILED DESCRIPTION
[0018] 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. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. 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.
[0019] The embodiments herein disclose summarizing events, received from Internet of Things (IoT) devices, by providing methods and systems that can be used to summarize data coming into a system, comprising of IoT devices, at the certain intervals and provide the data to a user.
[0020] The embodiments disclose methods and systems for summarizing raw data received from a plurality of sensors/devices in an IoT environment, at certain intervals and provide the data to a user. Further, the embodiments allow summarizing media data, received from devices in the IoT environment, based on factors such as activity in the media, presence of objects/persons/artifacts in the media, presence/absence of sounds/audio/noise, or the like. The embodiments allow storing data characteristics extracted from raw data received from a plurality of sensors, such that the data characteristics can be used to determine an approximation of the raw data.
[0021] Referring now to the drawings, and more particularly to FIGS. 1 through 9, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0022] FIG. 1 illustrates an example classification of IoT devices based on capability. As depicted in FIG. 1, the IoT devices can comprise of smaller components. Each of the IoT devices can be configured with at least one capability(s) 101, wherein the capabilities can provide at least one functions. Examples of the capabilities can be actuators, sensors, and streamers. The actuators can be classified into analog actuators and digital actuators. Digital actuators can further comprise of multi-value actuator and toggle actuator. The sensors can be digital sensors and analog sensors. Analog sensors can high low watermark sensor, high watermark sensor, low watermark sensor, value sensor, and so on. The streamer can be further classified into audio streamer, video streamer, and byte streamer.
[0023] Examples of the capabilities of the IoT devices are discussed as follows:
Actuator: The capability of an actuator 102 signifies that the IoT device is controllable. The capabilities include switching, regulating, or the like. The following are the sub-classifications of actuators:
Analog Actuator 102a: The capability signifies that an analog actuator 102a can be set to have multiple values. A typical example of the analog actuator can be a thermostat or a dimmer switch.
Digital Actuator 102b: The capability of a digital actuator 102b is similar to that of an analog actuator, but the digital actuator allows discrete values to be set. Examples of the types of digital actuators can be:
Multi Value Actuator 102ba: This is an actuator that allows multiple discrete values to be set. In an example, the multi value actuator 102ba can be a fan regulator, which has a discrete number of settings to which the speed can be set.
Toggle Actuator 102bb: This is a case of a digital actuator, which allows only two values to be set, typically on and off.
Sensor: The capability of a sensor 103 includes performing read only operation. The settings that may be allowed in the sensor 103 can be related to performance parameters. The primary value of the sensor 103 cannot be set from outside, but can only be read. Examples of the types of sensors 103 can be as follows:
Analog sensor: An analog sensor 103a can have multiple values in a range. Examples of the analog sensor can be, but not restricted to a thermometer, humidity sensor or any other sensor that provides values with a precision. The following are examples of the classifications of analog sensors:
Low watermark sensor: A low watermark sensor 103aa is a sensor that has a predefined value set as threshold. This sensor 103aa reports a value only when the value is lower than the threshold.
High watermark sensor: A high watermark sensor 103ab is a sensor that has a predefined value set as threshold. This sensor 103ab reports a value only when the value is higher than the threshold.
High low watermark sensor: A high low watermark sensor 103ac is a sensor that has a lower and higher predefined values set as thresholds. This sensor 103ac reports a value when the value is higher than the higher predefined value or lower than the lower predefined value.
Value sensor: A value sensor 103ad reports all the values that it reads.
Digital sensor: The digital sensor 103b can have multiple discrete values. In an example, the digital sensor 103b can be a sensor that counts the number of people in a space or number of times a certain event takes place.
Streamer: The streamer 104 offers a constant stream of bytes. In an example, the streamer 104 can be classified into the following three types:
Audio streamer: An audio streamer 104a sends an audio stream encoded into a byte stream. The audio streamer 104a can support a control channel that includes functions such as pause, mute and other relevant function that requires a proper functioning of an audio stream.
Video streamer: A video streamer 104b sends a video stream encoded into a byte stream. The video streamer 104b can support a control channel that includes functions such as pause, mute and other relevant function that requires proper functioning of a video stream.
Byte streamer: A byte streamer 104c is a stream that cannot be classified as either an audio or a video. It is an opaque stream of bytes that is transferred. Actual utility of a byte stream can be specific to a scenario.
[0024] FIG. 2 illustrates example classification of measures. The IoT device has the ability to send data to the cloud. The data that is sent can be associated with a unit of measurement. For example, the data, i.e., temperature, measured by a thermometer is associated with Fahrenheit or Celsius. A temperature value in Fahrenheit cannot be directly compared to a temperature value in Celsius. Hence, the data needs to be classified based on the unit of measurement associated with the data. The embodiments propose a measure based classification system, which allows comparing data values that are associated with different units of measurement, as depicted in FIG. 2. Examples of the classification of the measure 201 can be as follows:
Measure with media 201a: Amongst IoT devices, a significant number of them share images or videos of an occurrence. The IoT devices are mainly monitoring devices, which send data that accompanies videos, images and audio. The measures do not have values that are significant and statistical analysis other than frequency count cannot be carried out. The IoT devices have pointers to the media that can be used for subsequent analysis and generate secondary events.
Measure with unit 201b: There are measures, which are associated with units of measurement. The units can be used for normalizing the data to a same unit for any statistical analysis. In an example, a set of values received in Fahrenheit and Celsius can be converted to a single unit and then statistical analysis such as standard deviation, min, max, mean, median, mode, regression, or the like, can be performed.
[0025] FIG. 3 illustrates a system for summarizing data received from at least one IoT device. The IoT devices usually transmit a large amount of data, which may not be of much use in its raw form. But the data can be summarized at certain intervals, and the summarized data may be more appropriate for a user to interpret the data. The embodiments present a system and architecture that can be used to summarize influx of such data into the system at the certain intervals and provide the data to the user.
[0026] As depicted in FIG. 3, the summarization engine 302 receives all the data generated by the IoT devices (301). The summarization engine 302 can further comprise of a summarization filter 303, a notification module 304, at least one database 305. The database 305 can be at least one of a local memory, or a remotely located memory. Examples of the database 305 can be, but not limited to, a file server, a data server, the cloud, and so on. The database 305 can comprise of data received from the IoT device(s) 301, summarized data, configured parameters, user preferences (such as sensitivity of device(s) 301, turn on/off notifications, mode of sending the notifications to the user, format of sharing the notifications, and so on), trigger points, and so on.
[0027] The summarization filter 303 comprises of a plurality of summarizers. Examples of the summarizers can be, but not limited to, hourly, n-hourly, daily, weekly, monthly, yearly, and so on. On receiving data from the summarization engine 302, the summarization filter 303 checks the time stamp for a received data. If the time stamp is for a period belonging to a specific summarizer, the summarization filter 303 runs the corresponding summarizer. Whether a summarizer needs to be run is evaluated by the summarization filter 303, on receipt of each data and is run only when the period boundary is crossed. The summarization filter 303 can collect all data points received during the time period and compute a plurality of statistical measures for that period such as mean, median, mode, standard deviation and so on. The summarization filter 303 can perform statistical analytics on the data. The summarization filter 303 can perform media analytics on the data. The summarization filter 303 can generate synthetic events from the analytics. The summarization filter 303 can generate the synthetic events by statistically summarizing the data during a period using techniques such as at least one count, mean, median, mode, standard deviation and sum of all the values during the period. The summarization engine 302 can notify at least one user using the notification engine 304, based on user preferences. If the user has requested for an email communication, the summarization engine 302 can use the notification engine 304 to send an email notification to the user. The summarization filter 303 can also use data from a previous summarization. The summarization filter 303 can store the summarized data in the database 305.
[0028] Whenever data is received, the summarization filter 303 can compare the timestamp of the new data with the timestamp of previous data to check if the time threshold has crossed (wherein the threshold can be hourly, after a specific number of hours, daily, weekly, monthly, and so on). If the time threshold has been crossed (i.e., the new data is in a new time period), the summarization filter 303 runs the corresponding summarizer, similarly if the new event is for a new day, daily summary is run and so on for all the other summarizers. Media is also summarized for these periods. When a hourly summary is run, we collect all the media received during previous period and run a summary algorithm that extract frames with most amount of activity during that period.
[0029] The summarization engine 302 can enable a user to access the summarized data, as stored in the database 305. The summarization engine 302 can enable the user to configure parameters and parameters such as sensitivity of device(s) 301, turn on/off notifications, mode of sending the notifications to the user, format of sharing the notifications, and so on. The summarization engine 302 can enable the user to modify the summarized data stored in the database 305, based on at least one configuration and/or parameter.
[0030] In an example, if the time stamp is for a new hour, the summarization filter 303 runs the hourly summarizer. As part of summarizer, the summarization filter 303 collects all the data points received during the period and computes the statistical measure(s) for that period. If the summarization filter 303 is running a monthly summarizer, the summarization filter 303 can use the data generated by the weekly summarizer. If the summarization filter 303 is running a weekly summarizer, the summarization filter 303 can use the data generated by the daily summarizer. If the data is media, the summarization filter 303 can collect all the media received during previous period and determine the frames with activity from the media (wherein the activity can be pre-defined and examples of the activity can be but not limited to, movement, sound, and so on). The summarization filter 303 can extract the determined frames.
[0031] For every event received, the summarization filter 303 checks if the summarizer boundary is crossed. For example if the last data was received at 12:59PM and the next data is received at 1:01PM, then the summarization filter 303 would run the hourly summarizer. Similarly, if the last data was received at 11:59PM and next data is received at 12:01 AM, then the summarization filter 303 would run the daily summarizer.
[0032] The summarization engine 302 can also authenticate a user before permitting the user to access data. The summarization engine 302 can also authenticate the IoT device(s) 301, before accepting data sent by the IoT device(s) 301.
[0033] FIG. 4 is a flowchart depicting the process of the summarization engine managing data. When new data is received (401), the summarization engine 302 checks (402) if any of the summarizers need to be run. The summarization engine 302 can run each of the summarizers’ part of the chain, when the time boundary for a period changes. For example, when the hour flips over to next hour, the hourly summarizer runs, when the first event for new day is received, the daily summarizer is run and so on. If any of the summarizers need to be run, the summarization engine 302 runs (403) the required summarizer. The various actions in method 400 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 4 may be omitted.
[0034] FIG. 5 is an example flowchart depicting the functionality performed by an hourly summarizer. The summarization engine 302 checks (501) if it is a new hour, on receiving data from the IoT device(s) 301. If it is not a new hour, the summarization engine 302 stores (502) the raw data. If it is a new hour, the summarization engine 302 summarizes (503) the hourly data (which can be an event and/or media). The summarization engine 302 generates (504) hourly synthetic data, accordingly and processes (505) the raw data accordingly. The various actions in method 500 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 5 may be omitted.
[0035] FIG. 6 is an example flowchart depicting the functionality performed by a daily summarizer. The summarization engine 302 checks (601) if it is a new day, on receiving data from the IoT device(s) 301. If it is not a new day, the summarization engine 302 stores (602) the raw hourly data. If it is a new day, the summarization engine 302 summarizes (603) the daily data (which can be an event and/or media) and performs (604) analytics on the daily data. The summarization engine 302 generates (605) daily synthetic data and stores (606) the summarized data. The various actions in method 600 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 6 may be omitted.
[0036] FIG. 7 is an example flowchart depicting the functionality performed by a weekly summarizer. The summarization engine 302 checks (701) if it is a new week, on receiving data from the IoT device(s) 301. If it is not a new week, the summarization engine 302 stores (702) the raw daily event. If it is a new week, the summarization engine 302 summarizes (703) the daily data (which can be an event and/or media) and performs (704) analytics on the weekly data. The summarization engine 302 generates (705) weekly synthetic data and stores (706) the summarized data. The various actions in method 700 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 7 may be omitted.
[0037] FIG. 8 is an example flowchart depicting the functionality performed by a monthly summarizer. The summarization engine 302 checks (801) if it is a new month, on receiving data from the IoT device(s) 301. If it is not a new month, the summarization engine 302 stores (802) the raw weekly event. If it is a new month, the summarization engine 302 summarizes (803) the monthly data (which can be an event and/or media) and performs (804) analytics on the monthly data. The summarization engine 302 generates (805) monthly synthetic data and stores (806) the summarized data. The various actions in method 800 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 8 may be omitted.
[0038] FIG. 9 is an example flowchart depicting the functionality performed by a yearly summarizer. The summarization engine 302 checks (901) if it is a new year, on receiving data from the IoT device(s) 301. If it is not a new year, the summarization engine 302 stores (902) the raw monthly event. If it is a new year, the summarization engine 302 summarizes (903) the yearly data (which can be an event and/or media) and performs (904) analytics on the yearly data. The summarization engine 302 generates (905) yearly synthetic data and stores (906) the summarized data. The various actions in method 900 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 9 may be omitted.
[0039] In an example, as depicted in FIG. 5, when the type of the event is identified as hourly and when the hour count is incremented; the hourly summarizer runs, when the first event for a new day is received. Similarly the functionality of the daily summarizer, the weekly summarizer, and the two monthly summarizers are depicted in FIGS. 6-9 respectively.
[0040] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 3 include blocks, which can be at least one of a hardware device, or a combination of hardware device and software module.
[0041] The embodiment disclosed herein achieve summarization of events received from IoT devices by providing a system and architecture that can be used to summarize data coming into a system, comprising of IoT devices, at the certain intervals and provide the data to a user. Therefore, it is 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 method is implemented in a preferred embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of portable device that can be programmed. The device may also include means which could be e.g. hardware means like e.g. an ASIC, 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. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0042] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.