Patent Publication Number: US-2023164230-A1

Title: Method and apparatus for operating devices in iot environment

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is a continuation application, claiming priority under § 365(c), of an International application No. PCT/KR2022/001367 filed on Jan. 26, 2022, which is based on and claims the benefit of an Indian Complete patent application number 202141053389, filed on Nov. 19, 2021, in the Indian Patent Office, the disclosure of which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     1. Field 
     The disclosure relates to the field of an Internet of Things (IoT) environment. More particularly, the disclosure relates to operating devices in an IoT environment based on change in a relative position of one or more objects with respect to one or more entities. 
     2. Description of Related Art 
     In general, an Internet of Things (IoT) environment includes a plurality of IoT devices with which users may interact and control their operations. Also, an operational state of the IoT devices may be controlled automatically for enhancing a user experience. In existing methods, the operational state of the IoT devices may be controlled based on at least one of, but is not limited to, time, a location, a status of the IoT devices, security related aspects, a routine, a user personalized inputs, and so on (as depicted in  FIG.  1 A ). However, whenever a user wants to perform one or more activities associated with a change in a relative position of one or more objects, the existing methods do not involve controlling/modifying the operational state of the IoT devices based on the change in the relative position of the one or more objects associated with the one or more IoT devices. Thus, the user has to perform the one or more activities by manually operating the IoT devices, which become cumbersome for the user to repeat the activities manually every time. 
     Consider an example scenario, as depicted in  FIG.  1 B , wherein the user picks up clothes and detergent liquid for washing. In such a scenario, the user wants an automated solution to turn ON a washer for washing the clothes instead of manually turning ON the washer. However, the existing methods do not provide the automated solution to turn ON the washer when there is a change in a relative position of the clothes and the detergent liquid with respect to the user. 
     Consider another example scenario, as depicted in  FIG.  1 C , wherein the user performs workout daily using a yoga mat and a yoga ball. In such a scenario, the user wants an automated solution to turn ON a television (TV) and an air purifier every time instead of manually turning ON the TV and the air purifier. However, the existing methods do not provide the automated solution to turn ON the TV and the air purifier based on an interaction of the user with the yoga mat and the yoga ball. Thus, manually turning ON the TV and the air purifier every time while performing the workout may become cumbersome for the user. 
     Consider another example scenario, as depicted in  FIG.  1 D , wherein the user wants an automated solution to turn ON a geyser and a tap whenever the user picks up a towel, instead of manually turning ON the geyser and the tap. However, the existing methods do not provide the automated solution to turn ON the geyser and the tap based on a change in a relative position of the towel with respect to the user. 
     The above information is presented as background information only to assist with an understanding of the disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the disclosure. 
     SUMMARY 
     Aspects of the disclosure are to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the disclosure is to provide methods and systems for operating Internet of Things (IoT) devices in an IoT environment. 
     Another aspect of the disclose is to provide methods and systems for predicting an initiation of one or more activities by one or more entities based on a change in a relative position of one or more objects with respect to the one or more entities and modifying an operational state of the one or more IoT devices associated with the predicted one or more activities. 
     Another aspect of the disclose is to provide methods and systems for determining an intensity of the one or more activities being performed by the one or more entities and tuning the operational state of the one or more IoT devices associated with the one or more activities based on the determined intensity of the one or more activities. 
     Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the presented embodiments. 
     In accordance with an aspect of the disclosure, a method for operating devices by an electronic device in an Inter of Things (IoT) environment is provided. The method includes monitoring a movement of at least one object from a first location to a second location, identifying a relative position of the at least one object in the second location with respect to at least one entity, predicting an initiation of at least one activity by the at least one entity, based on the determined relative position of the at least one object with respect to the at least one entity, and modifying an operational state of at least one device associated with the predicted at least one activity. 
     In accordance with another aspect of the disclosure, an electronic device for operating devices in an Internet of Things (IoT) environment is provided. The electronic device includes a memory, and a controller coupled to the memory and configured to, monitor a movement of at least one object from a first location to a second location, identify a relative position of the at least one object in the second location with respect to at least one entity, predict an initiation of at least one activity by the at least one entity, based on the determined relative position of the at least one object with respect to the at least one entity, and modify an operational state of at least one device associated with the predicted at least one activity. 
     Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses various embodiments of the disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other aspects, features, and advantages of certain embodiments of the disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which: 
         FIGS.  1 A,  1 B,  1 C, and  1 D  depict example use cases of operating Internet of Things (IoT) using existing methods according to the related arts; 
         FIG.  2    depicts an Internet of Things (IoT) system, according to an embodiment of the disclosure; 
         FIG.  3    is a block diagram depicting various components of an electronic device for operating IoT devices, according to an embodiment of the disclosure; 
         FIG.  4    depicts a device operator performable in the electronic device for operating the IoT devices, according to an embodiment of the disclosure; 
         FIGS.  5 A and  5 B  are example diagrams depicting detecting a movement of one or more objects along with a user and identifying a relative position of the one or more objects, according to various embodiments of the disclosure; 
         FIG.  6    is an example diagram depicting calculation of a relative positional change index of the one or more objects, according to an embodiment of the disclosure; 
         FIG.  7    is an example diagram depicting grouping of the one or more objects along with entities, according to an embodiment of the disclosure; 
         FIG.  8    is an example diagram depicting prediction of initiation of one or more activities by the entities, according to an embodiment of the disclosure; 
         FIG.  9    is an example diagram depicting updating of an IoT activity prediction database, according to an embodiment of the disclosure; 
         FIG.  10 A  is an example diagram depicting monitoring of an intensity of an activity, according to an embodiment of the disclosure; 
         FIG.  10 B  is an example diagram depicting monitoring of vital parameters using Ultra-Wideband (UWB) sensors, according to an embodiment of the disclosure; 
         FIG.  10 C  is an example diagram depicting monitoring of a change in a relative position of an entity using the UWB sensors, according to an embodiment of the disclosure; 
         FIG.  10 D  is an example diagram depicting monitoring of change in an ambience due to the one or more activities, according to an embodiment of the disclosure; 
         FIG.  11    is an example diagram depicting tuning of an operational state of the IoT devices based on the intensity of the activities, according to an embodiment of the disclosure; 
         FIGS.  12 A and  12 B  are example conceptual diagrams depicting controlling of the operational state of the one or more IoT devices, according to various embodiments of the disclosure; 
         FIGS.  13 A and  13 B  depict an example use case scenario of operating the IoT devices on predicting an initiation of a yoga activity by a user, according to various embodiments of the disclosure; 
         FIG.  14    depicts an example use case scenario of operating the IoT devices on predicting the initiation of a chopping activity by a user in a kitchen, according to an embodiment of the disclosure; 
         FIGS.  15 A and  15 B  depict an example use case scenario of operating the IoT devices on predicting the initiation of a sleeping activity by a user in a bedroom, according to various embodiments of the disclosure; 
         FIGS.  16 A and  16 B  depict an example use case scenario of operating the IoT devices on predicting the initiation of a cooking activity by a user in a kitchen, according to various embodiments of the disclosure; 
         FIGS.  17 A and  17 B  depict an example use case scenario of operating the IoT devices automatically on predicting the initiation of a cleaning activity by a user, according to various embodiments of the disclosure; 
         FIG.  18    depicts an example use case scenario of operating the IoT devices automatically on predicting the initiation of a bathing activity by a user, according to an embodiment of the disclosure; 
         FIG.  19    depicts an example use case scenario of operating the IoT devices based on a false prediction of a yoga activity by a user, according to an embodiment of the disclosure; and 
         FIG.  20    is a flow chart depicting a method for operating the IoT devices, according to an embodiment of the disclosure. 
     
    
    
     Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures. 
     DETAILED DESCRIPTION 
     The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness. 
     The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the disclosure is provided for illustration purpose only and not for the purpose of limiting the disclosure as defined by the appended claims and their equivalents. 
     It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces. 
     Embodiments herein disclose methods and systems for operating Internet of Things (IoT) devices in an IoT environment based on a change in a relative position of one or more objects with respect to one or more entities. 
     Referring now to the drawings, and more particularly to  FIGS.  2 ,  3 ,  4 ,  5 A,  5 B,  6 ,  7 ,  8 ,  9 ,  10 A to  10 D,  11 ,  12 A,  12 B,  13 A,  13 B,  14 ,  15 A,  15 B,  16 A,  16 B ,  17 A,  17 B,  18 ,  19 , and  20 , where similar reference characters denote corresponding features consistently throughout the figures, there are shown example embodiments. 
       FIG.  2    depicts an Internet of Things (IoT) system, according to an embodiment of the disclosure. The IoT system  200  includes an IoT cloud server  202 , a plurality of IoT devices  204   a - 204   n,  a plurality of objects  206   a - 206   n,  and an electronic device  208 . 
     Referring to  FIG.  2   , an IoT cloud server  202 , a plurality of IoT devices  204   a - 204   n,  and an electronic device  208  may be connected with each other. In an example, the IoT cloud server  202 , the plurality of IoT devices  204   a - 204   n,  and the electronic device  208  may be connected with each other using a communication network  210 . The communication network  210  may include at least one of, but is not limited to, a wired network, a value added network, a wireless network, a satellite network, or a combination thereof. Examples of the wired network may be, but are not limited to, a Local Area Network (LAN), a Wide Area Network (WAN), an Ethernet, and so on. Examples of the wireless network may be, but are not limited to, a cellular network, a wireless LAN (e.g., wireless fidelity (Wi-Fi)), Bluetooth, Bluetooth low energy, Zigbee, Wi-Fi Direct (WFD), Ultra-wideband (UWB), infrared data association (IrDA), near field communication (NFC), and so on. In another example, the electronic device  208 , and the plurality of IoT devices  204   a - 204   n  may be connected with each other directly (for example: via a direct communication, via an access point, and so on). In another example, the electronic device  208  and the plurality of IoT devices  204   a - 204   n  may be connected to the IoT cloud server  202  and the electronic device  208  may be connected to the plurality of IoT devices  204   a - 204   n  through the IoT cloud server  202 . In another example, the electronic device  208  may be connected to the plurality of IoT devices  204   a - 204   n  using the communication network  210  and the electronic device  208  and the plurality of IoT devices  204   a - 204   n  may be connected to the IoT cloud server  202  using the communication network  210 . In another example, IoT cloud server  202 , the plurality of IoT devices  204   a - 204   n,  and the electronic device  208  may be connected with each other via a relay, a hub, and a gateway. It is understood that the IoT cloud server  202 , the plurality of IoT devices  204   a - 204   n,  and the electronic device  208  may be connected to each other in any of various manners (including those described above) and may be connected to each other in two or more of various manners (including those described above) at the same time. 
     The IoT cloud server  202  referred herein may be configured as a hardware device independent of the electronic device  208  but is not limited thereto. The IoT cloud server  202  may be a component of the electronic device  208  or may be a server designed to be classified into software. 
     The IoT cloud server  202  referred herein may be a server that obtains, stores, and manages device information, capabilities, location information, and an operational state of each of the plurality of IoT devices  204   a - 204   n  present in an IoT environment. Examples of the IoT environment may be, but are not limited to, a smart home environment, a smart office environment, a smart hospital environment, and so on. The device information may include information such as, but is not limited to, an identification value (for example: device ID information) of each of the plurality of IoT devices  204   a - 204   n,  a device type of each of the plurality of IoT devices  204   a - 204   n,  and so on. In an example herein, the identification value/device ID information may include information such as, but are not limited to, a Media Access Control (MAC) identifier (MAC ID), a serial number, a unique device ID, and so on. The capabilities include information about one or more capabilities of each of the plurality of IoT devices  204   a - 204   n.  Examples of the capabilities of the IoT device ( 204   a - 204   n ) may be, but are not limited to, an audio, a video, a display, an energy limit, data sensing capability, and so on. The location information includes information about a location of each of the plurality of IoT devices  204   a - 204   n.  The location of the IoT device ( 204   a - 204   n ) may indicate an area or a room (for example: a living room, a kitchen, a bedroom, a study room, a child room, a ward, a conference room, a factory unit, and so on) in the IoT environment, where the IoT device ( 204   a - 204   n ) is present. The operational state of the IoT device ( 204   a - 204   n ) provides information about at least one of, but is not limited to, a power ON/OFF state, an operation being performed by each of the plurality of IoT devices  204   a - 204   n,  and so on. Examples of the operation may be, but are not limited to, audio casting, video casting, controlling lights, energy managing, purifying air, sensing environmental factors (such as temperature humidity, smoke, or the like), and so on. The IoT cloud server  202  may update the device information, the capabilities, and the location information, on adding or removing any of the plurality of IoT devices  204   a - 204   n  in the IoT environment. The IoT cloud server  202  may also update the operational state of the IoT devices  204   a - 204   n.  In an example, the IoT cloud server  202  may update the operational state of the IoT devices  204   a - 204   n  continuously. In another example, the IoT cloud server  202  may update the operational state of the IoT devices  204   a - 204   n  at periodic intervals. In another example, the IoT cloud server  202  may update the operational state of the IoT devices  204   a - 204   n  on occurrence of at least one event. In an example, the event may include turn ON/OFF of the IoT devices  204   a - 204   n.    
     The plurality of IoT devices  204   a - 204   n  may be devices capable of exchanging information with each other and other devices (such as, the IoT cloud server  202 , the electronic device  208 , or the like). The plurality of IoT devices  204   a - 204   n  may be deployed in various locations or areas or rooms in the IoT environment with which users may interact and control the operations of each IoT device ( 204   a - 204   n ). 
     Examples of the plurality of IoT devices  204   a - 204   n  may be, but are not limited to, a smart phone, a mobile phone, a video phone, a computer, a tablet personal computer (PC), a netbook computer, a laptop, a wearable device, a vehicle infotainment system, a workstation, a server, a personal digital assistant (PDA), a smart plug, a portable multimedia player (PMP), a moving picture experts group phase 1 or phase 2 (MPEG-1 or MPEG-2) audio layer-3 (MP3) layer, a mobile medical device, a light, a voice assistant device, a camera, a home appliance, one or more sensors, and so on. Examples of the home appliance may be, but are not limited to, a television (TV), a digital video disc (DVD) player, an audio device, a refrigerator, an air conditioner (AC), an air purifier, a chimney, a cooktop, a vacuum cleaner, an oven, microwave, a washing machine, a dryer, a set-top box, a home automation control panel, a security control panel, a game console, an electronic key, a camcorder, an electronic picture frame, a coffee maker, a toaster oven, a rice cooker, a pressure cooker, and so on. Examples of the sensors may be, but are not limited to, a temperature sensor, a humidity sensor, an infrared sensor, a gyroscope sensor, an atmospheric sensor, a proximity sensor, a red green blue (RGB) sensor (a luminance sensor), a photo sensor, a thermostat, an Ultraviolet (UV) light sensor, a dust sensor, a fire detection sensor, a carbon dioxide (CO2) sensor, a smoke sensor, a window contact sensor, a water sensor, or any other equivalent sensor. A function of each sensor may be intuitively inferred by one of ordinary skill in the art based on its name, and thus, its detailed description is omitted. 
     The plurality of IoT devices  204   a - 204   n  may perform the one or more operations/actions based on their capabilities. Examples of the operations may be, but are not limited to, playing media (audio, video, or the like), capturing the media, purifying the air, performing cooling, or heating of a defined area, controlling lights, sensing various environmental factors (for example: temperature, smoke, humidity, or the like), and so on. The plurality of IoT devices  204   a - 204   n  may perform the respective one or more actions simultaneously. 
     The plurality of IoT devices  204   a - 204   n  may register with the IoT cloud server  202  and/or the electronic device  208  by communicating the device information, the capabilities, the operational state, and the location information to the IoT cloud server  202  and/or the electronic device  208 , once being deployed in the IoT environment. A user may register the plurality of IoT devices  204   a - 204   n  with the IoT cloud server  202  using the electronic device  208 . 
     The plurality of objects  206   a - 206   n  referred herein may be at least one of, things, the users, or the like, present in the IoT environment with which entities may interact to perform one or more activities. 
     In an example, the plurality of objects  206   a - 206   n  may be the IoT devices  204   a - 204   n.  In another example, the plurality of objects  206   a - 206   n  may not be the IoT devices  204   a - 204   n.  In an example, the plurality of objects  206   a - 206   n  may include at least one of, but is not limited to, a yoga mat, a yoga ball, a towel, clothes, a detergent liquid, vegetables, cooking utensils, broom stick, a knife, a pillow, a water bottle, a door, a window, and so on. 
     The entities referred herein may include one of, but is not limited to, the users, the IoT devices  204   a - 204   n,  and so on. Examples of the one or more activities performed by the entities using the one or more objects may be, but are not limited to, performing yoga/exercise, cooking, cleaning, sleeping, chopping vegetables, and so on. The user may register the plurality of objects  206   a - 206   n  with the IoT cloud server  202  and/or the electronic device  208 , once being deployed in the IoT environment. Embodiments herein use the terms “objects,” “entities,” and so on, interchangeably through the document. 
     The electronic device  208  referred herein may be a device used to control the operations of the plurality of IoT devices  204   a - 204   n  and to monitor the one or more objects  206   a - 206   n.  The electronic device  208  may also be a user device that is being used by the user to connect, and/or interact, and/or control the operations of the plurality of IoT devices  204   a - 204   n.  Examples of the electronic device  208  may be, but are not limited to, a smart phone, a mobile phone, a video phone, a computer, a tablet personal computer (PC), a netbook computer, a laptop, a wearable device, a personal digital assistant (PDA), a workstation, a server, an IoT device, or any other device that may be used to connect, and/or interact, and/or control the operations of the plurality of IoT devices  204   a - 204   n.    
     The electronic device  208  obtains, stores, and maintains the device information, the capabilities the location information, the operational state, or the like of each device ( 204   a - 204   n ) present in the IoT environment by directly communicating with each device ( 204   a - 204   n ) through the communication network  210 . Alternatively, the electronic device  208  obtains, stores, and maintains the device information, the capabilities the location information, the operation state, or the like of each device ( 204   a - 204   n ) present in the IoT environment from the IoT cloud server  202  through the communication network  210 . 
     In an embodiment, the electronic device  208  may be configured to operate the one or more IoT devices  204   a - 204   n  based on a change in a relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. Operating the IoT devices  204   a - 204   n  include modifying/controlling the operational state of the IoT devices  204   a - 204   n.  The electronic device  208  also obtains, determines, or generates a control command for controlling the operational state of the IoT devices  204   a - 204   n,  by utilizing the device information, the capabilities, the location information, or the like of each IoT device ( 204   a - 204   n ). The electronic device  208  may transmit the control command to any of the IoT devices  204   a - 204   n  to perform actions based on the stored capabilities of the respective IoT devices  204   a - 204   n.  The electronic device  208  may receive a result of performing the actions according to the control command from the IoT devices  204   a - 204   n.    
     For operating the IoT devices  204   a - 204   n,  the electronic device  208  monitors a movement of the one or more objects  206   a - 206   n  in the IoT environment. On monitoring the movement of the one or more objects  206   a - 206   n,  the electronic device  208  identifies the relative position of each of the one or more objects  206   a - 206   n  in the second location with respect to the one or more entities. The relative position of an object (e.g., one of the one or more objects  206   a - 206   n ) indicates a change in a position of the object from the first location/timestamp T 1  to the second location/timestamp T 2 . In an embodiment herein, the position of the object refers to three dimensional (3D) coordinates. Embodiments herein use the terms such as “relative position,” “change in the relative position,” “change in the position,” and so on, interchangeably through the document. 
     In an embodiment, the electronic device  208  uses one or more Ultra-wideband (UWB) sensors to monitor the movement of the one or more objects  206   a - 206   n  from the first location to the second location and to identify the relative position of the one or more objects  206   a - 206   n  in the second location with respect to the entity(ies). The one or more UWB sensors transmit signal pulses using a single omni-directional transmission antenna (Tx) and receives scattered signals/reflected signal/patterns of UWB signals using an omni-directional receiver antenna (Rx). The one or more UWB sensors provide the received patterns of UWB signals/reflected signal to the electronic device  208 . The electronic device  208  analyzes various properties of the patterns of UWB signals/reflected signal received from the one or more UWB sensors to monitor the movements of each object and to identify the relative position of each object with respect to the entity. Examples of the properties of the reflected signal may be, but are not limited to, a Received Signal Strength Index (RSSI), a Time Difference of Arrival (TDOA), a Time of Arrival (TOA), an Angle of Arrival (AOA), and so on. 
     On monitoring the movement of the one or more objects  206   a - 206   n  and identifying the relative position of the one or more objects  206   a - 206   n,  the electronic device  208  predicts an initiation of the one or more activities by the entity in relation with the identified relative position of the one or more objects  206   a - 206   n  with respect to the entity. Examples of the one or more activities, a yoga/meditation activity, a cooking activity, a cleaning activity, a chopping activity, a sleeping activity, and so on. 
     For predicting the one or more activities, the electronic device  208  detects an interaction of the entity with the one or more objects  206   a - 206   n  using the one or more UWB sensors. The electronic device  208  receives the pattern of UWB signals received from the one or more UWB sensors and analyzes the received pattern of UWB sensors to detect the interaction of the entity with one or more objects  206   a - 206   n.    
     On detecting the interaction of the entity with the one or more objects  206   a - 206   n,  the electronic device  208  derives parameters of the entity by analyzing the pattern of UWB signals received from the one or more UWB sensors. Examples of the parameters of the entity may be, but are not limited to, an identity of the entity, vital parameters of the entity, a location of the entity, a timestamp depicting time of interaction of the entity with the one or more objects  206   a - 206   n,  and so on. The vital parameters may be derived if the entity is the user. Examples of the vital parameters of the entity may be, but are not limited to, a breathing rate, a heart rate, and so on, of the user. The electronic device  208  also fetches past operation information state for the entity with respect to the one or more objects  206   a - 206   n  with which the user has initiated the interaction. The past operation information state provides information about previously monitored one or more activities of the entity, the operational state of the one or more IoT devices  204   a - 204   n  associated with the previously monitored one or more activities of the entity, the parameters of the entity associated with the previously monitored one or more activities of the entity, and so on, with respect to the one or more objects  206   a - 206   n.  For fetching the past operation information state for the entity, the electronic device  208  creates an object-entity mapping pair. The object-entity mapping pair includes a mapping of the one or more objects  206   a - 206   n  and the associated entity. The electronic device  208  fetches the past operation information state from an IoT activity prediction database  314  for the created object-entity mapping pair. The IoT activity prediction database  314  includes the past operation information for the plurality of entities with respect to the one or more objects  206   a - 206   n.    
     On deriving the parameters of the entity and fetching the past operation information state for the entity, the electronic device  208  groups the one or more objects  206   a - 206   n  based on the relative position of the one or more objects  206   a - 206   n  in the second location and the identified interaction of the entity with the one or more objects  206   a - 206   n.  For grouping each object (e.g., one of the one or more objects  206   a - 206   n ), the electronic device  208  computes a relative positional change index for the object (e.g., one of the one or more objects  206   a - 206   n ). The relative positional change index of the object (e.g., one of the one or more objects  206   a - 206   n ) indicates a probability for considering the object for the grouping. The electronic device  208  computes the relative positional change index for the object (e.g., one of the one or more objects  206   a - 206   n ) based on a position of the object (e.g., one of the one or more objects  206   a - 206   n ) with respect to the entity in the first location at a timestamp T 1  and the relative position of the object (e.g., one of the one or more objects  206   a - 206   n ) with respect to the entity in the second location at a timestamp T 2 . The electronic device  208  compares the relative positional change index computed for each object (e.g., one of the one or more objects  206   a - 206   n ) with a threshold. If the relative positional change index of the object is less than the threshold, the electronic device  208  does not consider the respective object for the grouping. If the relative positional change index of the object is greater than the threshold, the electronic device  208  considers the respective object for the grouping. 
       FIG.  4    depicts a device operator performable in the electronic device for operating the IoT devices, according to an embodiment of the disclosure. 
     Referring to  FIG.  4   , on computing the relative positional change index for each object (e.g., one of the one or more objects  206   a - 206   n ), an electronic device  208  analyzes at least one of, the relative positional change index of each object, the interaction of the entity with one or more objects  206   a - 206   n,  and a past operation information state of the entity using a first neural network  402  (as depicted in  FIG.  4   ) to group the one or more objects  206   a - 206   n.  Grouping the one or more objects  206   a - 206   n  using the first neural network is described in detail in conjunction with  FIG.  4   . 
     On grouping the one or more objects, the electronic device  208  predicts the initiation of the one or more activities by the entity in relation with the identified relative position of the one or more objects  206   a - 206   n  with respect to the entity. The electronic device  208  predicts the initiation of the one or more activities by the entity by analyzing at least one of, but is not limited to, the detected interaction of the entity with the one or more objects  206   a - 206   n,  the parameters of the entity, the past operation information state of the entity, the grouping of each of the one or more objects  206   a - 206   n,  and so on. 
     On predicting the initiation of the one or more activities by the entity in relation with the identified relative position of the one or more objects  206   a - 206   n  with respect to the entity, the electronic device  208  modifies the operational state of the one or more IoT devices  204   a - 204   n  associated with the predicted one or more activities. For modifying the operational state of the one or more IoT devices  204   a - 204   n,  the electronic device  208  detects the one or more IoT devices  204   a - 204   n  associated with the predicted activities. For detecting the one or more IoT devices  204   a - 204   n,  the electronic device  208  fetches previous operational state changes for the predicted one or more activities from an activity device mapping database  316  (as depicted in  FIG.  3   ). The previous operational state changes indicate information about the IoT devices  204   a - 204   n  and the associated operational state changes with respect to the previously monitored activities that have been initiated by the entity. The electronic device  208  analyzes at least one of, but is not limited to, the predicted one or more activities, the previous operational state changes fetched for the predicted one or more activities, the past operation information state of the entity, the grouping of the one or more objects  206   a - 206   n  along with the entity, and so on, using a second neural network  404  to detect the one or more IoT devices  204   a - 204   n  for the predicted one or more activities. The detection of the IoT devices  204   a - 204   n  for the predicted activities using the second neural network  404  is depicted in  FIG.  4   . 
     Once the one or more IoT devices  204   a - 204   n  have been detected for the predicted one or more activities, the electronic device  208  detects a current operational state of the one or more IoT devices  204   a - 204   n  detected for the predicted one or more activities. In an example, the electronic device  208  may detect the current operational state of the one or more IoT devices  204   a - 204   n  by communicating directly with the one or more IoT devices  204   a - 204   n.  In another example, the electronic device  208  may detect the current operational state of the one or more IoT devices  204   a - 204   n  by communicating with the IoT cloud server  202 . On detecting the current operational state of the one or more IoT devices  204   a - 204   n,  the electronic device  208  generates a correlation of the predicted one or more activities with the one or more objects  206   a - 206   n  in the first location and the second location, the relative position of the one or more objects  206   a - 206   n  with respect to the entity, and the current operational state of the one or more IoT devices  204   a - 204   n.  The electronic device  208  modifies the operational state of the one or more IoT devices  204   a - 204   n  associated with the predicted one or more activities simultaneously with the predicted one or more activities. The electronic device  208  modifies the operational state of the one or more IoT devices  204   a - 204   n  by analyzing at least one of, but is not limited to, the generated correlation, the predicted one or more activities, the past operation information state of the entity, the current operational state of the one or more IoT devices  204   a - 204   n,  user preferences, and so on. In an example, the user preferences indicate the IoT devices, whose operational state has to be changed, the specific operational states, an association of the IoT devices  204   a - 204   n  with the specific activities, and so on. 
     The electronic device  208  further reverts the modified operational state of the one or more IoT devices to a previous operational state, on detecting that the entity moves away from the respective one or more objects  206   a - 206   n.  The electronic device  208  reverts the modified operational state of the one or more IoT devices  204   a - 204   n  to the previous operational state using a previous IoT device status history. The previous IoT device status history indicates the operational state of the IoT devices at each timestamp. 
     In an embodiment, the electronic device  208  also determines an intensity of the one or more activities being performed by the entity. For determining the intensity of each activity, the electronic device  208  monitors one or more intensity factors for the activity. The intensity factors include at least one of, but is not limited to, the vital parameters of the entity involved in the one or more activities, a change in a relative position of the entity involved in the one or more activities, a change in an ambience due to the one or more activities, and so on. The electronic device  208  receives the pattern of UWB signals from the one or more UWB sensors and analyzes the properties of the received pattern of UWB signals to determine the one or more intensity factors for the activity. On determining the one or more intensity factors for each activity, the electronic device  208  determines the intensity of each activity by processing the monitored intensity factor using a third neural network  406 . Determining the intensity of the one or more activities using the third neural network is described in detail in conjunction with  FIG.  4   . 
     On determining the intensity of the one or more activities, the electronic device  208  may (or may not) tune the operational state of the IoT devices  204   a - 204   n  associated with the one or more activities based on the intensity of the one or more activities and past history stored for the one or more activities in an activity database  318 . 
       FIG.  2    shows blocks of the IoT system  200 , but it is to be understood that other embodiments are not limited thereon. In other embodiments, the IoT system  200  may include a greater or lesser number of blocks. Further, the labels or names of the blocks are used only for illustrative purpose and does not limit the scope of the embodiments herein. One or more blocks can be combined together to perform same or substantially similar function in the IoT system  200 . 
       FIG.  3    is a block diagram depicting various components of an electronic device for operating IoT devices, according to an embodiment of the disclosure. 
     Referring to  FIG.  3   , an electronic device  208  includes a memory  302 , a communication interface  304 , an input unit  306 , an output unit  308 , a sensor unit  310 , and a processor (e.g., controller  312 ). The electronic device  208  may also be coupled with the IoT activity prediction database  314 , the activity device mapping database  316 , and the activity database  318 . The IoT activity prediction database  314  includes information about the past operation information state of the plurality of entities with respect to the one or more objects  206   a - 206   n.  The activity device mapping database  316  includes the previous operational state changes for the plurality of activities. The activity database  318  includes the past history stored for the particular activity. 
     The memory  302  referred herein may include at least one type of storage medium, from among a flash memory type storage medium, a hard disk type storage medium, a multi-media card micro type storage medium, a card type memory (for example, a secure digital (SD) or an extreme digital (XD) memory), random-access memory (RAM), static RAM (SRAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), programmable ROM (PROM), a magnetic memory, a magnetic disk, or an optical disk. The memory  302  may store at least one of, but is not limited to, the movement of the one or more objects  206   a - 206   n,  the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities, the activities initiated by the one or more entities with respect to the one or more objects  206   a - 206   n,  the past operation information state of the plurality of entities, the current operational state of the plurality of IoT devices  204   a - 204   n,  the previous IoT device status history, information about the modified operational state of the IoT devices  204   a - 204   n,  the intensity of the activities being performed by the entities, and so on. 
     The memory  302  may also store a device operator  400  (as depicted in  FIG.  4   ), which has been executed by the controller  312  to operate the IoT devices  204   a - 204   n  in the IoT environment based on the change in the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. 
     The memory  302  may also store the first neural network  402 , the second neural network  404 , and the third neural network  406  (as depicted in  FIG.  4   ). The first neural network  402  may be processed by the controller  312  to group the one or more objects  206   a - 206   n.  The second neural network  404  may be processed by the controller  312  to detect the IoT devices  204   a - 204   n  for the predicted one or more activities. The third neural network  406  may be processed by the controller  312  to determine the intensity of the one or more activities. 
     Examples of the first neural network  402 , the second neural network  404 , and the third neural network  406  may be, but are not limited to, an Artificial Intelligence (AI) model, a multi-class Support Vector Machine (SVM) model, a Convolutional Neural Network (CNN) model, a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann Machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), a regression based neural network, a deep reinforcement model (with rectified linear unit (ReLU) activation), a deep Q-network, and so on. The first neural network  402 , the second neural network  404 , and the third neural network  406  may include a plurality of nodes, which may be arranged in layers. Examples of the layers may be, but are not limited to, a convolutional layer, an activation layer, an average pool layer, a max pool layer, a concatenated layer, a dropout layer, a fully connected layer, a SoftMax layer, and so on. Each layer has a plurality of weight values and performs a layer operation through calculation of a previous layer and an operation of a plurality of weights/coefficients. A topology of the layers of the first neural network  402 , the second neural network  404 , and the third neural network  406  may vary based on the type of the first neural network  402 , the second neural network  404 , and the third neural network  406 , respectively. In an example, the first neural network  402 , the second neural network  404 , and the third neural network  406  may include an input layer, an output layer, and a hidden layer. The input layer receives a layer input (depending upon on the associated first/second/third neural network) and forwards the received layer input to the hidden layer. The hidden layer transforms the layer input received from the input layer into a representation, which may be used for generating the output in the output layer. The hidden layers extract useful/low level features from the input, introduce non-linearity in the network and reduce a feature dimension to make the features equivalent to scale and translation. The nodes of the layers may be fully connected via edges to the nodes in adjacent layers. The input received at the nodes of the input layer may be propagated to the nodes of the output layer via an activation function that calculates the states of the nodes of each successive layer in the network based on coefficients/weights respectively associated with each of the edges connecting the layers. 
     The first neural network  402 , the second neural network  404 , and the third neural network  406  may be trained using at least one learning method to group the one or more objects  206   a - 206   n,  detect the one or more IoT devices  204   a - 204   n  for the predicted one or more activities, and determine the intensity of the one or more activities, respectively. Examples of the learning method may be, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, regression-based learning, and so on. The trained first neural network  402 /the second neural network  404 /the third neural network  406  may be a neural network model in which a number of layers, a sequence for processing the layers and parameters related to each layer may be known and fixed for performing the intended functions. Examples of the parameters related to each layer may be, but are not limited to, activation functions, biases, input weights, output weights, and so on, related to the layers. A function associated with the learning method may be performed through the non-volatile memory, the volatile memory, and the controller  312 . The controller  312  may include one or a plurality of processors. At this time, one or a plurality of processors may be a general purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial Intelligence (AI)-dedicated processor such as a neural processing unit (NPU). 
     The one or a plurality of processors group the one or more objects  206   a - 206   n,  detect the one or more IoT devices  204   a - 204   n  for the predicted one or more activities, and determine the intensity of the one or more activities in accordance with a predefined operating rule of the first neural network  402 , the second neural network  404 , and the third neural network  406 , respectively, stored in the non-volatile memory and the volatile memory. The predefined operating rules of the first neural network  402 , the second neural network  404 , and the third neural network  406  are provided through training the modules using the learning method. 
     Here, being provided through learning means that, by applying the learning method to a plurality of learning data, a predefined operating rule or the first neural network  402 /the second neural network  404 /the third neural network  406  of a desired characteristic is made. Grouping the one or more objects  206   a - 206   n,  detecting the one or more IoT devices  204   a - 204   n  for the predicted one or more activities, and determining the intensity of the one or more activities may be performed in the electronic device  208  itself in which the learning according to an embodiment is performed, and/or may be implemented through a separate server/system. 
     The communication interface  304  may include one or more components, which allow the electronic device  208  to communicate with another device (for example: another electronic device, the IoT cloud server  202 , the plurality of IoT devices  204   a - 204   n,  and so on) using the communication methods that have been supported by the communication network  210 . The communication interface  304  may include the components such as, a wired communicator, a short-range communicator, a mobile/wireless communicator, and a broadcasting receiver. 
     The wired communicator may allow the electronic device  208  to communicate with the other devices using the communication methods such as, but are not limited to, wired LAN, the Ethernet, and so on. The short-range communicator may allow the electronic device  208  to communicate with the other devices using the communication methods such as, but are not limited to, Bluetooth low energy (BLE), near field communicator (NFC), WLAN (or Wi-fi), Zigbee, infrared data association (IrDA), Wi-Fi direct (WFD), UWB communication, Ant+ (interoperable wireless transfer capability) communication, shared wireless access protocol (SWAP), wireless broadband internet (WiBro), wireless gigabit alliance (WiGig), and so on. The mobile communicator may transceive wireless signals with at least one of a base station, an external terminal, or a server on a mobile communication network/cellular network. In an example, the wireless signal may include a speech call signal, a video telephone call signal, or various types of data, according to transceiving of text/multimedia messages. The broadcasting receiver may receive a broadcasting signal and/or broadcasting-related information from the outside through broadcasting channels. The broadcasting channels may include satellite channels and ground wave channels. In an embodiment, the electronic device  208  may or may not include the broadcasting receiver. 
     The input unit  306  may be configured to allow the user to interact with the electronic device  208 . 
     The output unit  308  may be configured to indicate the modified operational state of the IoT devices  204   a - 204   n  to the user. The output unit  308  may include at least one of, for example, but is not limited to, a sound output module/voice assistant module, a display, a vibration motor, a User Interface (UI) module, a light emitting device, and so on, to indicate the modified operational state of the IoT devices  204   a - 204   n  to the user. The UI module may provide a specialized UI or graphics user interface (GUI), or the like, synchronized to the electronic device  208 , according to the applications. 
     The sensor unit  310  may include the one or more UWB sensors, which may be used for monitoring the movements of the one or more objects  206   a - 206   n,  identifying the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities, deriving the parameters of the entity, and determining the intensity of the one or more activities. 
     The controller  312  may include one or a plurality of processors. The one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an Artificial Intelligence (AI)-dedicated processor such as a neural processing unit (NPU). 
     The controller  312  may be configured to operate the IoT devices  204   a - 204   n  in the IoT environment based on the change in the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. The controller  312  monitors the movement of the one or more objects  206   a - 206   n  and identifies the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. The controller  312  predicts the initiation of the one or more activities by the one or more entities in relation with the determined relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. The controller  312  modifies the operational state of the one or more IoT devices  204   a - 204   n  associated with the predicted one or more activities. 
     The controller  312  also determines the intensity of the one or more activities being performed by the one or more entities. The controller  312  tunes the operational state of the one or more IoT devices  204   a - 204   n  associated with the one or more activities based on the determined intensity of the one or more activities. 
     The controller  312  processes/executes the device operator  400  to operate the IoT devices  204   a - 204   n  in the IoT environment. As depicted in  FIG.  4   , the device operator  400  includes an object identification and monitoring module  408 , a relative positional change index calculating module  410 , an entity information deriving module  412 , a classifier/grouping module (e.g., classifier module  414 ), an activity prediction module  416 , an IoT activity initiator module  418 , an update module  420 , an activity impact identifier module  422 , and an IoT activity handling module  424 . 
       FIGS.  5 A and  5 B  are example diagrams depicting detecting a movement of one or more objects along with a user and identifying a relative position of the one or more objects, according to various embodiments of the disclosure. 
     The object identification and monitoring module  408  may be configured to detect the movement of the one or more objects  206   a - 206   n  from one position to another and identify the relative position of the one or more objects with respect to the one or more entities. The object identification and monitoring module  408  uses the one or more UWB sensors to detect the movement of the objects and identify the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. The relative position of the object (e.g., one of the one or more objects  206   a - 206   n ) may be provided in a form of the 3D coordinates. The object identification and monitoring module  408  also identifies the position of each object (e.g., one of the one or more objects  206   a - 206   n ) at the different timestamps using the one or more UWB sensors. The object identification and monitoring module  408  is described in detail in conjunction with  FIG.  5 A . 
     Referring to  FIG.  5 A , for detecting the movement of the objects and identifying the relative position of the one or more objects, the object identification and monitoring module  408  allows the UWB sensor(s) to transmit the radar signal in the IoT environment and receives the reflected signal from the object(s) (e.g., one of the one or more objects  206   a - 206   n ) and the entity(ies). In an example, consider the object as a yoga ball and the entity as a user. The object identification and monitoring module  408  receives the reflected signal/patterns of UWB signals from the UWB sensor for detecting the movement of the yoga ball with respect to the user at the particular location. The object identification and monitoring module  408  removes outliers from the received patterns of UWB signals to remove unwanted data. The object identification and monitoring module  408  passes the patterns of UWB signals after removing the outliers to a pattern matcher  502 . The pattern matcher  502  detects the movement of the object(s) from the first location to the second location and determines the relative position of the object(s) in the second location with respect to the entity(ies) using a pattern matching method with distance calculation. The pattern matching method with distance calculation may be inferred by an ordinary person skilled in the art based on its name, thus its detailed description is omitted. The pattern matcher  502  detects the movement of the object(s) and determines the relative position of the object(s) with respect to the entity(ies) with a certain probability and the associated location. In an example herein, the pattern matcher  502  detects that the yoga ball is in motion with respect to the user at a living room (an example of the location) with an example probability of 0.9. Thus, based on the objects  206   a - 206   n,  the entity/user and the associated location may be affirmed. The pattern matcher  502  may also determine the position of the object at the first location. 
     Referring to  FIG.  5 B , an example table indicating the movement of the objects with respect to the user is depicted in  FIG.  5 B . 
     The object identification and monitoring module  408  provides the information about the detected movement of the object(s) and the relative position of the object(s) with respect to the entity to the relative positional change index calculating module  410 . 
       FIG.  6    is an example diagram depicting calculation of a relative positional change index of the one or more objects, according to an embodiment of the disclosure. 
     The relative positional change index calculating module  410  may be configured to determine the relative positional change index for each object, based on the detected movement of the corresponding object with respect to the entity(ies). The relative positional change index calculating module  410  is described in detail in conjunction with  FIG.  6   . 
     Referring to  FIG.  6   , a relative positional change index calculating module  410  receives the position/3D coordinates of one or more objects  206   a - 206   n  with respect to one or more entities at a first location/timestamp T 1  and a second location/timestamp T 2  from an object identification and monitoring module  408 . In an example herein, consider that the objects  206   a - 206   n  may include a yoga mat, a yoga ball, and a window, and the entity may be a user. The relative positional change index calculating module  410  calculates the relative positional change index for the yoga mat, the yoga ball, and the window with respect to the user based on a difference of the associated coordinates at the first location/T 1  and the second location/T 2 . The relative positional change index of the object may indicate the probability, which determines whether to consider the object for grouping or not. For example, the relative positional change index calculated for the yoga mat, the yoga ball, and the window may be high, high, and low, respectively. In such a scenario, the yoga ball, and the yoga mat may be considered to group along with the user and the window may be discarded from grouping. The relative positional change index provides the relative positional change index of each object (e.g., one of the one or more objects  206   a - 206   n ) to the classifier module  414 . 
     The entity information deriving module  412  may be configured to detect the interaction of the one more entities with the one or more objects  206   a - 206   n  (whose movement has been detected) and derive the parameters of the one or more entities using the one or more UWB sensors. The entity information deriving module  412  may also be configured to fetch the past operation information state for each entity from the IoT activity prediction database  314  with respect to the one or more objects  206   a - 206   n  with which the corresponding entity has been interacted. The entity information deriving module  412  provides the information about the detected interaction of the one or more entities with the one or more objects  206   a - 206   n,  the parameters of the one or more entities, and the past operation information state of the one or more entities to the classifier module  414 . 
       FIG.  7    is an example diagram depicting grouping of the one or more objects along with entities, according to an embodiment of the disclosure. 
     The classifier module  414  may be configured to group the one or more objects  206   a - 206   n  with respect to the one or more entities. The classifier module  414  is depicted in detail in conjunction with  FIG.  7   . 
     Referring to  FIG.  7   , a classifier module  414  uses a first neural network  402  to group one or more objects  206   a - 206   n  with respect to one or more entities. The classifier module  414  provides the relative positional change index of each object, the interaction of the one or more entities with the one or more objects  206   a - 206   n,  and the past operation information state of the entities of the one or more objects  206   a - 206   n  as inputs to the first neural network  402 . The first neural network  402  processes the received inputs and classifies the one or more objects  206   a - 206   n  and the one or more entities moving together into the same group. For example, as depicted in  FIG.  7   , the classifier module  414  classifies the user moving together with a yoga mat and a yoga ball into the same group. The classifier module  414  provides information about the grouping of the one or more objects  206   a - 206   n  along with one or more entities to the activity prediction module  416 . 
     The activity prediction module  416  may be configured to predict the initiation of the one or more activities by the one or more entities in relation with the identified relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. The activity prediction module  416  analyzes at least one of, the detected interaction of the one or more entities with the one or more objects  206   a - 206   n,  the parameters of the one or more entities, the past operation information state, the grouping of the one or more objects  206   a - 206   n,  and so on, to predict the initiation of the one or more activities by the one or more entities in relation with the identified relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. 
       FIG.  8    is an example diagram depicting prediction of initiation of one or more activities by the entities, according to an embodiment of the disclosure. 
     The IoT activity initiator module  418  may be configured to modify the operational state of the one or more IoT devices  204   a - 204   n  based on the predicted activities. The IoT activity initiator module  418  is described in detail in conjunction with  FIG.  8   . 
     Referring to  FIG.  8   , for modifying the operational state of one or more IoT devices  204   a - 204   n,  an IoT activity initiator module  418  accesses the previous operational state changes for the predicted one or more activities from an activity device mapping database  316 . The IoT activity initiator module  418  feeds at least one of, but is not limited to, the predicted one or more activities, the past operation information state of the entity, the grouping of the one or more objects  206   a - 206   n  along with one or more entities, the previous operational state changes with respect to the predicted one or more activities, and so on, to the second neural network  404  as inputs. The second neural network  404  analyzes the received inputs to detect the one or more IoT devices  204   a - 204   n  for the predicted one or more activities. 
     For example, consider that the activity prediction module  416  predicts that a user is going to perform a yoga, on detecting the movement of a yoga ball and a yoga mat along with the user. In such a scenario, the IoT activity initiator module  418  provides the information about the predicted activity, the past operation information state of the user (which provides information about the previous activities initiated by the user with respect to the interaction of the yoga ball and the yoga mat and the associated operational state changes of the IoT devices  204   a - 204   n ), the grouping of the yoga ball and the yoga mat along with the user, the previous operational state changes with respect to the predicted activity (i.e., performing the yoga), and so on, to the second neural network  404 . The second neural network  404  detects the IoT devices  204   a - 204   n  such as, a Television (TV), an air purifier, and an Air Conditioner (AC) present in the second location (i.e., the location where the user is present) as the associated IoT devices  204   a - 204   n  for performing the yoga. The IoT activity initiator module  418  detects the current operational state of the TV, the air purifier, and the AC by communicating with the IoT cloud server  202  or by directly communicating with the TV, the air purifier, and the AC. The IoT activity initiator module  418  generates the correlation of the predicted one or more activities with the one or more objects  206   a - 206   n  (the yoga ball and the yoga mat), the relative position of the one or more objects  206   a - 206   n  with respect to the user, and the current operational state of the TV, the air purifier, and the AC. The IoT activity initiator module  418  modifies the operational state of the TV, the air purifier, and the AC, based on the generated correlation and the user preferences. In an example herein, as depicted in  FIG.  8   , the IoT activity initiator module  418  turns ON the TV and plays a yoga/meditation video on the TV, turns ON the air purifier, and sets a mode of the AC to a low noise mode. 
       FIG.  9    is an example diagram depicting updating of an IoT activity prediction database, according to an embodiment of the disclosure. 
     The update module  420  may be configured to update the IoT activity prediction database  314  with information about the modified operational state of the one or more IoT devices  204   a - 204   n  with respect to the entity and the associated one or more objects  206   a - 206   n.  The update module  420  is described in detail in conjunction with  FIG.  9   . 
     Referring to  FIG.  9   , an update module  420  provides information about one or more objects  206   a - 206   n  and associated one or more entities, one or more activities initiated by the entities, and a modified/triggered operational state of IoT devices  204   a - 204   n  associated with the initiated one or more activities to a pattern correlation miner  902 . The pattern correlation miner  902  creates a mapping of the modified/triggered operational state of the IoT devices  204   a - 204   n  with the one or more objects  206   a - 206   n,  the one or more entities, the one or more activities initiated by the one or more entities, the interaction of the one or more entities with the one or more objects  206   a - 206   n,  the time of operation, and the location of the one or more objects  206   a - 206   n,  the one or more entities, and the one or more IoT devices  204   a - 204   n.  The pattern correlation miner  902  stores the mapping as the past operation information state in the IoT activity prediction database  314 . 
     The activity impact identifier module  422  may be configured to determine the intensity of the one or more activities being performed by the one or more entities. 
       FIG.  10 A  is an example diagram depicting monitoring of an intensity of an activity, according to an embodiment of the disclosure. 
       FIG.  10 B  is an example diagram depicting monitoring of vital parameters using Ultra-Wideband (UWB) sensors, according to an embodiment of the disclosure. 
       FIG.  10 C  is an example diagram depicting monitoring of a change in a relative position of an entity using the UWB sensors, according to an embodiment of the disclosure. 
       FIG.  10 D  is an example diagram depicting monitoring of change in an ambience due to the one or more activities, according to an embodiment of the disclosure. 
     Referring to  FIGS.  10 A to  10 D , an activity impact identifier module  422  uses one or more UWB sensors to determine an intensity of one or more activities. In an example herein, consider that the entity/user is performing a yoga. In such a scenario, the activity impact identifier module  422  receives the pattern of UWB signals/reflected signal from the one or more objects  206   a - 206   n  and the user from the one or more UWB sensors. The activity impact identifier module  422  analyzes the properties of the received reflected signal such as, but are not limited to, the RSSI, the TDOA, the TOA, the AOA, and so on, to determine/monitor the intensity factors with respect to performing the yoga by the user, as depicted in  FIG.  10 A . In an example, the intensity factors may include at least one of, the vital parameters of the user, the intensity of the relative change in the position of the entities, the change in the ambience, and so on. The vital parameters of the user may include at least one of, the heartbeat and breathing rate of the user, or the like. Determining of the heartbeat of the user using the one or more UWB sensors is depicted in  FIG.  10 B . Determining the vital parameters of the user helps in obtaining information about the heartbeat and the breathing rate of the user, which allows knowledge of how much the user is involved in the particular activity (for example herein, performing the yoga) and accordingly to operate the associated IoT devices  204   a - 204   n.  Determining the relative change in the position of the user while involved in performing the activity is depicted in  FIG.  10 C . As depicted in  FIG.  10 C , continuously monitoring the movement pattern of the user involved in the activity using the one or more UWB sensors may be used to determine the intensity of the relative change in the position of the user. Thus, the intensity of the activity may be determined. 
     Determining the impact on the ambience using the one or more UWB sensors is depicted in  FIG.  10 D . As depicted in  FIG.  10 D , the impact on the ambience/any change in the ambience due to the ongoing activity of the user may be determined by tracking the change in the position of the one or more objects  206   a - 206   n  along with the change in the vital parameters of the user using the one or more UWB sensors. 
     On determining the intensity factors for each activity, the activity impact identifier module  422  determines the intensity of the activity being performed by the user. 
       FIG.  11    is an example diagram depicting tuning of an operational state of the IoT devices based on the intensity of the activities, according to an embodiment of the disclosure. 
     The IoT activity handling module  424  may be configured to tune the operational state of the one or more IoT devices  204   a - 204   n  associated with the one or more activities, based on the intensity of the one or more activities. The IoT activity handling module  424  is described in detail in conjunction with  FIG.  11   . 
     Referring to  FIG.  11   , consider an example scenario, an IoT activity handling module  424  receives an intensity of an activity being performed by a user (i.e., performing yoga by the user). The intensity of the activity may be determined based on the vital parameters of the user, the intensity of the relative change in the position of the entity, and the impact on the ambience. In such a scenario, based on the intensity of the activity and the past history stored for the specific activity from the activity device mapping database  316 , the IoT activity handling module  424  tunes the operational state of the one or more IoT devices  204   a - 204   n  associated with the activity. In an example herein, as depicted in  FIG.  11   , the IoT activity handling module  424  decreases a temperature of the AC to accommodate heavy yoga training and changes a mode of the air purifier to accommodate more pure air supply due to heavy breathing detection. 
       FIGS.  3 , and  4    show blocks of the electronic device  208 , but it is to be understood that other embodiments are not limited thereon. In other embodiments, the electronic device  208  may include a greater or lesser number of blocks. Further, the labels or names of the blocks are used only for illustrative purpose and does not limit the scope of the embodiments herein. One or more blocks can be combined together to perform same or substantially similar function in the electronic device  208 . 
       FIGS.  12 A and  12 B  are example conceptual diagrams depicting controlling of an operational state of one or more IoT devices, according to various embodiments of the disclosure. Embodiments herein enable the electronic device  208  to operate/control the IoT devices  204   a - 204   n  based on the change in the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. 
     Referring to  FIG.  12 A , for operating IoT devices  204   a - 204   n,  an electronic device  208  identifies one or more objects  206   a - 206   n  that are moving in an IoT environment from the first location to the second location along with the one or more entities using the one or more UWB sensors. In an example, the objects  206   a - 206   n /entities may include a towel, a user, and so on. 
     On identifying the movement of the one or more objects  206   a - 206   n,  the electronic device  208  calculates the relative positional change index based on the position of the one or more objects  206   a - 206   n  at the first location/timestamp T 1  and the second location/timestamp T 2 . The relative positional change index of the object (e.g., one of the one or more objects  206   a - 206   n ) indicates the probability for considering the corresponding object for the grouping. The electronic device  208  discards the one or more objects  206   a - 206   n  for grouping if the relative positional change index of the one or more objects  206   a - 206   n  is less than the threshold. The electronic device  208  considers the information about the objects  206   a - 206   n  whose relative positional change index is greater than the threshold for grouping. 
     The electronic device  208  groups the one or more objects  206   a - 206   n  and the one or more entities that are moving together into the same group by analyzing at least one of, the relative positional change index, the interaction of the one or more entities with the one or more objects  206   a - 206   n,  the past operation information state of the one or more entities, and so on, using the first neural network  402 . For example, the electronic device  208  may group a user with a yoga mat and a yoga ball. 
     The electronic device  208  predicts the initiation of the one or more activities by the one or more entities in relation with the movement of the one or more objects  206   a - 206   n.  The electronic device  208  predicts the initiation of the one or more activities by analyzing at least one of, the detected interaction of the at least entity with the one or more objects  206   a - 206   n,  the parameters of the one or more entities, the past operation information state, and the grouping of the one or more objects  206   a - 206   n,  and so on. 
     On predicting the initiation of the one or more activities, the electronic device  208  detects the IoT devices  204   a - 204   n  for the predicted one or more activities. The electronic device  208  detects the IoT devices  204   a - 204   n  by analyzing at least one of, the predicted activity, the past operation information state of the entity, the grouping of one or more objects  206   a - 206   n,  the previous operational state changes with respect to the predicted activity, and so on, using the second neural network  404 . The electronic device  208  identifies the current operational state of the detected IoT devices  204   a - 204   n  by communicating with the IoT cloud server  202 . The electronic device  208  then generates the correlation of the predicted one or more activities with the one or more objects  206   a - 206   n  in the first location and the second location, the relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities, and the current operational state of the one or more IoT devices  204   a - 204   n.  On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the IoT devices  204   a - 204   n  associated with the predicted one or more activities. 
     Referring to  FIG.  12 B , an electronic device  208  determines an intensity of one or more activities in which the one or more entities have been involved. The electronic device  208  determines the intensity of each activity by identifying the intensity factors of each activity using the one or more UWB sensors. The electronic device  208  tunes the operational state of the IoT devices  204   a - 204   n  associated with the one or more activities based on the determined intensity of the one or more activities. 
       FIGS.  13 A and  13 B  depict an example use case scenario of operating IoT devices on predicting the initiation of a yoga activity by a user, according to various embodiments of the disclosure. 
     Referring to  FIG.  13 A , consider an example scenario, wherein an electronic device  208  detects a movement of objects  206   a - 206   n  such as, a yoga ball, a yoga mat, and a window in a living room along with the user by identifying the change in the relative position/3D coordinates of the yoga ball, the yoga mat, and the window with respect to the user. The electronic device  208  calculates the relative positional change index for the yoga ball, the yoga mat, and the window by computing the position of the yoga ball, the yoga mat, and the window, respectively at the timestamp T 1  and the timestamp T 2 . 
     The electronic device  208  groups the yoga ball, the yoga mat, and the user in the same group as, the yoga ball, the yoga mat, and the user are moving together. The electronic device  208  discards the window from the grouping, as the relative positional change index is lesser than the threshold (i.e., indicating the low probability). The electronic device  208  then analyzes at least one of, the interaction of the user with the yoga ball and the yoga mat, the past operation state information of the user, the grouping of the yoga ball, the yoga mat, and the user in the same group, the parameters of the user, or the like and predicts the initiation of the yoga activity by the user (i.e., an example of the activity). 
     On predicting the yoga activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a TV, an air purifier, and an AC as the associated devices for the yoga activity. The electronic device  208  analyzes at least one of, the predicted yoga activity, the past operation information state of the entity with respect to the yoga ball and the yoga mat, the grouping of the yoga ball, and the yoga mat along with the user, the previous operational state changes for the predicted yoga activity, or the like, using the second neural network  404  to detect the TV, the air purifier, and the AC as the IoT devices  204   a - 204   n  for the predicted yoga activity. The electronic device  208  collects the current operational state of the TV, the air purifier, and the AC from the IoT cloud server  202 . The electronic device  208  generates the correlation of the yoga activity with the yoga ball and the yoga mat, the relative position of the yoga ball and the yoga mat with respect to the user, and the current operational state of the TV, the air purifier, and the AC. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the TV, the air purifier, and the AC. In an example herein, the electronic device  208  turns ON the TV and plays a yoga/meditation video on the TV, turns ON a filter mode of the air purifier, and allows a low noise mode of the AC (as the AC has already turned ON). 
     Referring to  FIG.  13 B , once modifying an operational state of the TV, the air purifier, and the AC, an electronic device  208  monitors the intensity of the yoga activity being performed by the user using the one or more UWB sensors. In an example herein, the monitored intensity of the yoga activity indicates a high breathing rate of the user, and a high movement of the yoga ball. In such a scenario, based on the monitored intensity of the yoga activity, the electronic device  208  tunes the operational state of the air purifier, and the AC. In an example herein, the electronic device  208  turns ON an ultra-filter mode for the air purifier and decreases temperature and sets a fan mode of the AC to high. 
       FIG.  14    depicts an example use case scenario of operating IoT devices on predicting the initiation of a chopping activity by a user in a kitchen, according to an embodiment of the disclosure. 
     Referring to  FIG.  14   , consider an example scenario, wherein an electronic device  208  detects a movement of objects  206   a - 206   n  such as, vegetables, a knife, and a chopping board along with the user in the kitchen by identifying the change in the relative position/3D coordinates of the vegetables, the knife, and the chopping table with respect to the user. The electronic device  208  calculates the relative positional change index for the vegetables, the knife, and the chopping table by computing the position of the vegetables, the knife, and the chopping table, respectively at the first timestamp T 1  and the second timestamp T 2 . 
     The electronic device  208  groups the vegetables, the knife, the chopping table, and the user in the same group as, the vegetables, the knife, the chopping table, and the user are moving together. The electronic device  208  then analyzes at least one of, the interaction of the user with the vegetables, the knife, and the chopping table, the past operation state information of the user with respect to the vegetables, the knife, and the chopping table, the grouping of the vegetables, the knife, the chopping table and the user in the same group, the parameters of the user, or the like, and predicts the initiation of the chopping activity by the user to chop the vegetables. 
     On predicting the chopping activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a group of kitchen lights (referred hereinafter as a kitchen light group), and a vegetable steamer as the associated devices for the chopping activity. The electronic device  208  analyzes at least one of, the predicted activity, the past operation information state of the user with respect to the vegetables, the knife, and the chopping board, the grouping of the vegetables, the knife, the chopping table, and the user into the same group, the previous operational state changes with respect to the predicted chopping activity, and so on, using the second neural network  404  to detect the IoT devices  204   a - 204   n  for the predicted chopping activity. The electronic device  208  collects the current operational state of the kitchen light group and the vegetable steamer from the IoT cloud server  202 . The electronic device  208  generates the correlation of the chopping activity with the vegetables, the knife, and the chopping board, the relative position of the vegetables, the knife, and the chopping board with respect to the user, and the current operational state of the kitchen light group and the vegetable steamer. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the kitchen light group and the vegetable steamer. In an example herein, the electronic device  208  turns ON the kitchen light group with bright white and 75% intensity and turns ON a pre-heat mode for the vegetable steamer. 
       FIGS.  15 A and  15 B  depict an example use case scenario of operating IoT devices on predicting the initiation of a sleeping activity by a user in a bedroom, according to various embodiments of the disclosure. 
     Referring to  FIG.  15 A , consider an example scenario, wherein an electronic device  208  detects a movement of objects  206   a - 206   n  such as, a pillow and a water bottle along with a user in the bedroom by identifying the change in the relative position/3D coordinates of the pillow and the water bottle with respect to the user. The electronic device  208  calculates the relative positional change index for the pillow and the water bottle by computing the position of the pillow and the water bottle, respectively at the timestamp T 1  and the timestamp T 2 . 
     The electronic device  208  groups the pillow, the water bottle, and the user in the same group as, the pillow, the water bottle, and the user are moving together. The electronic device  208  then analyzes at least one of, the interaction of the user with the pillow and the water bottle, the past operation state information of the user with respect to the pillow and the water bottle, the grouping of the pillow, the water bottle, and the user in the same group, the parameters of the user, or the like, and predicts the initiation of sleeping activity by the user in the bedroom. 
     On predicting the sleeping activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a smart lock of a door, an AC, and an air purifier as the associated devices for sleeping activity. The electronic device  208  analyzes at least one of, the predicted sleeping activity, the past operation information state of the user with respect to the pillow and the water bottle, the grouping of the pillow, the water bottle, and the user into the same group, the previous operational state changes with respect to the predicted sleeping activity, and so on, using the second neural network  404  to detect the smart lock of the door, the AC, and the air purifier as the associated devices for the predicted sleeping activity. The electronic device  208  collects the current operational state of the smart lock of the door, the AC, and the air purifier from the IoT cloud server  202 . The electronic device  208  generates the correlation of the sleeping activity with the pillow and the water bottle, the relative position of the pillow and the water bottle with respect to the user, and the current operational state of the smart lock of the door, the AC, and the air purifier. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the smart lock of the door, the AC, and the air purifier. In an example herein, the electronic device  208  sets the smart lock of the door to a lock state, turns ON a filter mode for the air purifier, and turns a night mode for the AC. 
     Referring to  FIG.  15 B , once modifying the operational state of the smart lock, the air purifier, and the AC, an electronic device  208  monitors the intensity of the sleeping activity being performed by the user using the one or more UWB sensors. In an example herein, the monitored intensity of the sleeping activity indicates that the user has covered himself with a quilt/blanket, which was on a bed near the user. In such a scenario, based on the monitored intensity of the sleeping activity, the electronic device  208  tunes the operational state of the AC. In an example herein, the electronic device  208  increases temperature of the AC and sets a fan mode to low for the AC. 
       FIGS.  16 A and  16 B  depict an example use case scenario of operating IoT devices on predicting the initiation of a cooking activity by a user in a kitchen, according to various embodiments of the disclosure. 
     Referring to  FIG.  16 A , consider an example scenario, wherein an electronic device  208  detects the movement of objects  206   a - 206   n  such as, vegetables, a knife, and cooking utensils along with the user in the kitchen, by identifying the change in the relative position/3D coordinates of the vegetables, the knife, and the cooking utensils with respect to the user. The electronic device  208  calculates the relative positional change index for the vegetables, the knife, and the cooking utensils by computing the position of the vegetables, the knife, and the cooking utensils, respectively at the first timestamp T 1  and the second timestamp T 2 . 
     The electronic device  208  groups the vegetables, the knife, the cooking utensils, and the user in the same group as, the vegetables, the knife, the cooking utensils, and the user are moving together. The electronic device  208  then analyzes at least one of, the interaction of the user with the vegetables, the knife, and the cooking utensils, the past operation state information of the user with respect to the vegetables, the knife, and the cooking utensils, the grouping of the vegetables, the knife, the cooking utensils and the user in the same group, the parameters of the user, or the like and predicts the initiation of the cooking activity by the user in the kitchen. 
     On predicting the cooking activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a group of lights present in the kitchen (referred as a kitchen light group) and a vegetable steamer/griller, as the associated devices for the cooking activity. The electronic device  208  analyzes at least one of, the predicted activity, the past operation information state of the user with respect to the vegetables, the knife, and the cooking utensils, the grouping of the vegetables, the knife, and the cooking utensils into the same group, the previous operational state changes with respect to the predicted cooking activity, and so on, using the second neural network  404  to detect the kitchen light group and the vegetable steamer as the associated IoT devices  204   a - 204   n  for the predicted cooking activity. The electronic device  208  collects the current operational state of the kitchen light group and the vegetable steamer from the IoT cloud server  202 . The electronic device  208  generates the correlation of the cooking activity with the vegetables, the knife, and the cooking utensils, the relative position of the vegetables, the knife, and the cooking utensils with respect to the user, and the current operational state of the kitchen light group and the vegetable steamer. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the kitchen light group and the vegetable steamer. In an example herein, the electronic device  208  turns ON the kitchen light group with bright white and 75% intensity and turns ON a pre-heat mode for the vegetable steamer. 
     Referring to  FIG.  16 B , an electronic device  208  monitors the intensity of the cooking activity being performed by the user using the one or more UWB sensors. In an example herein, the intensity of the cooking activity indicates a change in a breathing pattern of the user due to cough, and a presence of smoke in the kitchen due to heating. In such a scenario, based on the monitored intensity of the cooking activity, the electronic device  208  turns ON a chimney and sets the chimney to a turbo mode, and turns ON an exhaust fan. 
       FIGS.  17 A and  17 B  depict an example use case scenario of operating IoT devices automatically on predicting the initiation of a cleaning activity by a user, according to various embodiments of the disclosure. 
     Referring to  FIG.  17 A , consider an example scenario, wherein an electronic device  208  detects the movement of a broomstick along with the user in a bedroom by identifying the change in the relative position/3D coordinates of the broomstick with respect to the user. The electronic device  208  calculates the relative positional change index for the broomstick by computing the position of the broomstick at the timestamp T 1  and the timestamp T 2 . 
     The electronic device  208  groups the broomstick and the user in the same group as, the broomstick and the user are moving together. The electronic device  208  then analyzes at least one of, the interaction of the user with the broomstick, the past operation state information of the user with respect to the broomstick, the grouping of the broomstick and the user in the same group, the parameters of the user, or the like and predicts the initiation of the cleaning activity by the user in the bedroom. 
     On predicting the cleaning activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a fan, lights, and an air purifier as the associated devices for the cleaning activity. The electronic device  208  analyzes at least one of, the predicted activity, the past operation information state of the user with respect to the broomstick, the grouping of the broomstick, and the user into the same group, the previous IoT devices  204   a - 204   n  and the associated operational state changes with respect to the predicted cleaning activity, and so on, using the second neural network  404  to detect the fan, the lights, and the air purifier as the associated devices for the initiated cleaning activity. The electronic device  208  collects the current operational state of the fan, the lights, and the air purifier for the cleaning activity. The electronic device  208  generates the correlation of the cleaning activity with the fan, the lights, and the air purifier, the relative position of the fan, the lights, and the air purifier with respect to the user, and the current operational state of the fan, the lights, and the air purifier. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the fan, the lights, and the air purifier. In an example herein, the electronic device  208  sets a mode of operation of the fan to low and increases brightness of the lights by 75%. 
     Further, once modifying the operational state of the fan, the lights, and the air purifier, the electronic device  208  monitors the intensity of the cleaning activity being performed by the user using the one or more UWB sensors as depicted in  FIG.  17 B . In an example herein, the monitored intensity of the cleaning activity indicates rapid sweeping movements of the broomstick, which is resulting in a lot of dust particles in air. In such a scenario, based on the intensity of the cleaning activity, the electronic device  208  tunes the operational state of the fan and the air purifier. In an example herein, the electronic device  208  turns OFF the fan and turns ON the air purifier and sets a filter mode to high. 
       FIG.  18    depicts an example use case scenario of operating IoT devices automatically on predicting the initiation of a bathing activity by a user, according to an embodiment of the disclosure. 
     Referring to  FIG.  18   , consider an example scenario, wherein an electronic device  208  detects movement of objects  206   a - 206   n  such as, a towel and a cupboard door along with the user in the bedroom by identifying the change in the relative position/3D coordinates of the towel and the cupboard with respect to the user. The electronic device  208  calculates the relative positional change index for the towel and the cupboard by computing the position of the towel and the cupboard, respectively at the timestamp T 1  and the timestamp T 2 . 
     The electronic device  208  groups the towel, the cupboard, and the user in the same group as, the towel, the cupboard and the user are moving together. The electronic device  208  then analyzes at least one of, the interaction of the user with the towel and the cupboard, the past operation state information of the user with respect to the towel and the cupboard, the grouping of the towel, the cupboard, and the user in the same group, the parameters of the user, or the like and predicts the initiation of the bathing activity by the user. 
     On predicting the bathing activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a geyser, and a bathtub tap as the associated devices for the bathing activity. The electronic device  208  analyzes at least one of, the predicted activity, the past operation information state of the user with respect to the towel and the cupboard door, the grouping of the towel, the cupboard door, and the user into the same group, the previous operational state changes for the predicted bathing activity, and so on, using the second neural network  404  to detect the geyser and the bathtub tap as the associated devices for the predicted bathing activity. The electronic device  208  collects the current operational state of the geyser and the bathtub tap as the associated devices for the predicted bathing activity. The electronic device  208  generates the correlation of the bathing activity with the towel and the cupboard door, the relative position of the towel and the cupboard door with respect to the user, and the current operational state of the geyser and the bathtub tap. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the geyser and the bathtub tap. In an example herein, the electronic device  208  turns ON the geyser and turns ON the bathtub tap to fill a bathtub. 
       FIG.  19    depicts an example use case scenario of operating the IoT devices based on a false prediction of a yoga activity by a user, according to an embodiment of the disclosure. 
     Referring to  FIG.  19   , consider an example scenario, wherein an electronic device  208  detects a movement of objects  206   a - 206   n  such as, a yoga ball, a yoga mat, and a window along with the user in a living room by identifying the change in the relative position/3D coordinates of the yoga ball, the yoga mat, and the window with respect to the user. The electronic device  208  calculates the relative positional change index for the yoga ball, the yoga mat, and the window by computing the position of the yoga ball, the yoga mat, and the window, respectively at the first timestamp T 1  and the second timestamp T 2 . 
     The electronic device  208  groups the yoga ball, the yoga mat, and the user in the same group as, the yoga ball, the yoga mat, and the user are moving together. The electronic device  208  discards the window from the grouping, as the relative positional change index is lesser than the threshold (i.e., indicating the low probability). The electronic device  208  then analyzes at least one of, the interaction of the user with the yoga ball and the yoga mat, the past operation state information of the user, the grouping of the yoga ball, the yoga mat, and the user in the same group, the parameters of the user, or the like and predicts the initiation of the yoga activity by the user. 
     On predicting the yoga activity, the electronic device  208  detects the IoT devices  204   a - 204   n  such as, a TV, an air purifier, and an AC as the associated devices for the yoga activity. The electronic device  208  analyzes at least one of, the predicted activity, the past operation information state of the entity, the grouping of one or more objects  206   a - 206   n,  the operational state changes of the IoT devices  204   a - 204   n  with respect to the predicted activity, and so on, using the second neural network  404  to detect the TV, the air purifier, and the AC as the IoT devices  204   a - 204   n  for the yoga activity. The electronic device  208  collects the current operational state of the TV, the air purifier, and the AC from the IoT cloud server  202 . The electronic device  208  generates the correlation of the yoga activity with the yoga ball and the yoga mat, the relative position of the yoga ball and the yoga mat with respect to the user, and the current operational state of the TV, the air purifier, and the AC. On generating the correlation, the electronic device  208  analyzes the generated correlation and the user preferences to modify the operational state of the TV, the air purifier, and the AC. 
     On modifying the operational state of the TV, the air purifier, and the AC, the electronic device  208  detects that the user has moved away from the yoga ball and the yoga mat to receive a phone call. In such a scenario, the electronic device  208  detects that the predicted activity/yoga activity as a false positive and reverts the operational state of the TV, the air purifier, and the AC to the previous operational state. 
     When the user finishes the call and returns to the yoga mat and the yoga ball, the electronic device  208  performs the above described operations to modify the operational state of the TV, the air purifier, and the AC based on the change in the relative position of the yoga mat and the yoga ball. 
       FIG.  20    is a flow chart depicting a method for operating IoT devices, according to an embodiment of the disclosure. 
     Referring to  FIG.  20    depicting a flow chart  2000 , at operation  2002 , the method includes monitoring, by an electronic device  208 , a movement of the one or more objects  206   a - 206   b  from the first location to the second location. At operation  2004 , the method includes identifying, by the electronic device  208 , the relative position of the one or more objects  206   a - 206   n  in the second location with respect to at least one entity. 
     At operation  2006 , the method includes predicting, by the electronic device  208 , the initiation of the one or more activities by the one or more entities, based on the determined relative position of the one or more objects  206   a - 206   n  with respect to the one or more entities. At operation  2008 , the method includes modifying, by the electronic device  208 , the operational state of the one or more IoT devices  204   a - 204   n  associated with the predicted one or more activities. 
     Embodiments herein modify operational state of Internet of Things (IoT) devices automatically based on an initiation of one or more activities by a user/entity through a relative change in position of objects in an IoT environment detected by a UWB sensor, which may ease user&#39;s long manual intervention in operating the related IoT devices. 
     Embodiments herein provide an automated real time quick action to a user based on the relative change in position of objects, which may be user friendly and help the user to modify the operational state of the IoT devices. 
     Embodiments herein improve connected user experience in a smart home environment with all the objects. 
     Embodiments herein provide an integrated IoT experience with non-IoT entities. 
     Embodiments herein increase customer lifetime value by easing out few operations in the IoT environment. 
     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 elements. The elements shown in  FIGS.  2 ,  3 , and  4    can be at least one of a hardware device, or a combination of hardware device and software module. 
     The embodiments disclosed herein describe methods and systems for operating devices in an IoT environment. 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 operations 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 such as, e.g., an application-specific integrated circuit (ASIC), or a combination of hardware and software means, e.g., an ASIC and a field programmable gate array (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 disclosure may be implemented on different hardware devices, e.g., by using a plurality of CPUs. 
     While the disclosure has been shown and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents.