WO2016116880A1

WO2016116880A1 - INTERNET OF THINGS (IoT) LOAD ENERGY MANAGEMENT AND CONTROL BASED ON MULTI-DIMENSIONAL FREQUENCY SPECTRAL SIGNATURE

Info

Publication number: WO2016116880A1
Application number: PCT/IB2016/050287
Authority: WO
Inventors: Prasanna Venkateswaran VIJAYAKUMAR; Vinaya Skanda NAGARAJ
Original assignee: Vijayakumar Prasanna Venkateswaran; Nagaraj Vinaya Skanda
Priority date: 2015-01-22
Filing date: 2016-01-21
Publication date: 2016-07-28

Abstract

The present disclosure generally relates to the domain of Io T load (electrical device) energy management and control. In particular, the present disclosure relates to systems and methods for controlling/energy managing networked Io T load devices based on their unique frequency spectrums. The present disclosure further relates to systems and architectures for enabling secured Io T load management in networked nodes/loads.

Description

INTERNET OF THINGS (IoT) LOAD ENERGY MANAGEMENT AND CONTROL BASED ON MULTI-DIMENSIONAL FREQUENCY SPECTRAL SIGNATURE

TECHNICAL FIELD

[0001] The present disclosure generally relates to the domain of networked Internet of Things (IoT) load (electrical device) energy management and control. In particular, the present disclosure relates to systems and methods for controlling/energy managing networked IoT load devices based on their unique frequency spectrums.

BACKGROUND

[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0003] The Internet is a global system of interconnected computers and computer networks that use a standard Internet protocol suite (e.g., the Transmission Control Protocol (TCP) and Internet Protocol (IP)) to communicate with each other. The Internet of Things (IoT) is based on the idea that everyday objects, not just computers and computer networks, can be readable, recognizable, locatable, addressable, and controllable via an IoT communications network (e.g., an ad-hoc system or the Internet).

[0004] A number of market trends are driving development of IoT devices. For example, increasing energy costs are driving governments' strategic investments in smart grids and support for future consumption, such as for electric vehicles and public charging stations. Increasing health care costs and aging populations are driving development for remote/connected health care and fitness services. A technological revolution in the home is driving development for new "smart" services, including consolidation by service providers marketing 'N' play (e.g., data, voice, video, security, energy management, etc.) and expanding home networks. Buildings are getting smarter and more convenient as a means to reduce operational costs for enterprise facilities.

[0005] There are a number of key applications for the IoT. For example, in the area of smart grids and energy management, utility companies can optimize delivery of energy to homes and businesses while customers can better manage energy usage. In the area of home and building automation, smart homes and buildings can have centralized control over virtually any device or system in the home or office, from appliances to plug-in electric vehicle (PEV) security systems. In the field of asset tracking, enterprises, hospitals, factories, and other large organizations can accurately track locations of high-value equipments, patients, vehicles, and so on. In the area of health and wellness, doctors can remotely monitor patients' health while people can track the progress of fitness routines.

[0006] In particular, it is desired to control the IoT devices from a one or more networked controllers (e.g., smart phone, tablet PC), and also enable the IoT devices to interact with each other, wherein such IoT devices can include lighting, air conditioners, heaters, fans, computers, audio devices, video devices, among others. Current solutions require that users download a dedicated application for each device. This is because the devices and the controller must talk the same language. However, there is no integration of the functionality between devices or relationships between devices.

[0007] Furthermore, IoT typically uses key enabling technologies such as Bluetooth Low Energy (BLE), LoRa, 6L0WPAN, Zigbee, Ethernet, Cellular, WiFi (WLAN) and/or other such communication standards to interface with networked devices, which although widely used, pose security concerns at network communication layers. While the communication software stack may have data encryption options such as AES, DES etc., they are still exposed to data sniffing vulnerabilities in particular more prone but not limited to Wireless network systems. For instance, in an example of intelligent LED module working based on BLEAViFi technology, it is easy to study how networked bulbs talk to each other and understand their message communication pattern. Attackers use sniffer tools to look at data packets and reconstruct packets that emulate an end point device, thereby leading to exposure of vital credentials and operating data. For instance, in an exemplary pattern, one of the messages between the smart LED lamps can be about username and password, which can be retrieved by posing as a new lamp joining the network, based on which credentials for the network can be stolen to in turn control the lights and possibly even other devices on the network.

[0008] Furthermore, with growing number of battery powered IoT devices such as in mobile devices, wearables, smart sensors, solar street lighting etc, it is a challenge for product developers to secure data in real time whilst also efficiently managing end point power utilization.

[0009] There is therefore a need in the art for mechanisms that allow secure and efficient load device identification, control, and energy management. [00010] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[00011] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term "about." Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[00012] As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.

[00013] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention. SUMMARY OF THE INVENTION

[00014] The present disclosure generally relates to the domain of IoT load (electrical device) energy management and control. In particular, the present disclosure relates to systems and methods for controlling/energy managing networked IoT load devices based on their unique frequency spectrums. The present disclosure further relates to systems and architectures for enabling secured IoT load management in networked nodes/loads.

[00015] According to one embodiment of the present disclosure, networked Internet of Things (IoT) load device of the present disclosure can include a means to generate its frequency spectrum based on one or a combination of analog input electrical and nonelectrical signals/parameters (such as input current, input voltage, output current, output voltage, capacitor current, capacitor voltage, frequency, temperature, time stamp, GPS coordinates, among other like input signals/parameters) to the device. In an exemplary implementation, frequency spectrum can be generated based on a Digital Signal Processor (DSP) that can be configured to, based on the one or more input signal(s)/parameters, implement an multi-variable FFT (MVFFT) algorithm, and generate the multi-dimensional frequency spectrum by taking output from a switch mode power converter. In another aspect, one or more input signal parameters including, but not limited to, current, voltage, and temperature can be used individually and/or in combination to enable the resulting frequency spectrum to create a unique signature for the load device, wherein such spectrum signature can therefore be different for each load device. In an aspect, the generated frequency spectrum can be stored in cloud storage and/or in a server or any other desired repository.

[00016] According to one embodiment, a load device controlling means, also interchangeably referred to as a controller and/or a control device, of the present disclosure can be configured to, in an implementation, receive spectrum signatures of one or more IoT load devices in order to efficiently detect the presence of the load devices by comparing the received signatures with ones stored in the cloud/repository, and upon match, authenticate the IoT device to control operations thereof, through means such as RF/wireless/wired.

[00017] According to one embodiment, such load devices controlling means/control devices can include, but is not limited to, one or a combination of a laptop, tablet PC, mobile phone, smart phone, or any other smart device that can receive/access the frequency spectrum signature of one or more load devices, match the signature with stored spectrum signature(s), and control/manage the load device based on the same. Such a system architecture enhances the security of IoT platforms, wherein even if a new load device joins the network, the proposed load device controlling means would be able to determine that its unique frequency spectrum signature is not stored in its repository/database and hence either add the new load device or reject it based on the configuration of the system.

[00018] According to another embodiment, system of the present disclosure further enables networked IoT load devices to interact and share communications with each other, wherein when a message is transmitted from a sender IoT load device, the recipient IoT load device can communicate with the cloud database/central repository, and match the stored frequency spectrum signature with that received along with the message from the sender IoT device. This enables secured communication between networked IoT load devices.

[00019] According to one embodiment, aspects of the present disclosure can implement a secured spectrum coding protocol (SSCP) configured to address security issues involved in IoT at the data link layer or part of the communication stack by securely encoding and decoding spectral signature information of client-server system, wherein each load device/module can include/incorporate the SSCP as a software module. In another aspect, load device of the present disclosure can include any electrical load device including but not limited to LED lighting, motors, connected vehicle components, solar inverters, mobile devices, wearable devices, wireless chargers, among other loads that are integrated with a network communication protocol.

[00020] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

[00021] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[00022] FIG. l illustrates an exemplary architecture for controlling one or more load devices in accordance with an embodiment of the present disclosure.

[00023] FIG. 2 illustrates an exemplary representation showing battery powered sensor devices that can enable energy management based on their unique frequency spectral signature. [00024] FIG. 3A illustrates generation and storage of unique frequency spectral signatures for networked IoT load devices in accordance with an aspect of the present disclosure.

[00025] FIG. 3B illustrates generation and storage of unique frequency spectral signatures for networked battery-enabled IoT sensors in accordance with an aspect of the present disclosure.

[00026] FIG. 4A shows receiving of communication messages (having spectrum signatures) from one or more load devices by a controller and/or another load device to decipher the identity of sensors by comparing received spectrum signatures with ones stored in the cloud/server.

[00027] FIG. 4B shows receiving of communication messages (having spectrum signatures) from one or more sensors by a controller and/or another load device/sensor to decipher the identity of sensors by comparing received spectrum signatures with ones stored in the cloud/server.

[00028] FIG. 5 illustrates an exemplary representation showing generation of spectrum signature in accordance with an embodiment of the present disclosure.

[00029] FIG. 6 illustrates an exemplary set of steps to convert an input parameter (such as current) to MVFFT to generate unique frequency spectrum based signature for a load device.

[00030] FIGs. 7 A and 7B illustrate exemplary output representations showing comparison of MVFFT' s of two identical motors.

[00031] FIG. 8 depicts a 3-dimensional image of the classification of loads in accordance with an embodiment of the present disclosure.

[00032] FIG. 9 depicts an exemplary arrangement of MVFFT signature in accordance with an embodiment of the present disclosure.

[00033] FIG. 10 illustrates an exemplary flow diagram for generation of MVFFT signature for a load device in accordance with an embodiment of the present disclosure.

[00034] FIG. 11 A illustrates a flow diagram showing configuration of high speed ADC peripherals.

[00035] FIG. 1 IB illustrates a flow diagram showing configuration of BLE radio.

[00036] FIG. l lC illustrates a flow diagram showing MVFFT generation in accordance with an embodiment of the present invention. [00037] FIG. 1 ID illustrates a flow diagram showing BLE data transfer in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

[00038] Unless the context requires otherwise, throughout the specification and claims which follow, the word "comprise" and variations thereof, such as, "comprises" and "comprising" are to be construed in an open, inclusive sense that is as "including, but not limited to."

[00039] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[00040] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

[00041] The headings and abstract of the disclosure provided herein are for convenience only and do not interpret the scope or meaning of the embodiments.

[00042] Reference will now be made in detail to the exemplary embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts.

[00043] The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed. [00044] The present disclosure generally relates to the domain of IoT load (electrical device) energy management and control. In particular, the present disclosure relates to systems and methods for controlling/energy managing networked IoT load devices based on their unique frequency spectrums. The present disclosure further relates to systems and architectures for enabling secured IoT load management in networked nodes/loads.

[00045] According to one embodiment of the present disclosure, networked Internet of Things (IoT) load device of the present disclosure can include a means to generate its frequency spectrum based on one or a combination of analog input electrical and nonelectrical signals/parameters (such as input current, input voltage, output current, output voltage, capacitor current, capacitor voltage, frequency, temperature, time stamp, GPS coordinates, among other like input signals/parameters) to the device. In an exemplary implementation, frequency spectrum can be generated based on a Digital Signal Processor (DSP) that can be configured to, based on the one or more input signal(s)/parameters, implement an multi-variable FFT (MVFFT) algorithm and generate the frequency spectrum by taking output from a switch mode power converter. In another aspect, one or more input signal parameters including, but not limited to, current, voltage, and temperature can be used individually and/or in combination to enable the resulting frequency spectrum to create a unique signature for the load device, wherein such spectrum signature can therefore be different for each load device. In an aspect, generated frequency spectrum can be stored in cloud storage and/or in a server or any other desired repository.

[00046] According to one embodiment, a load device controlling means, also interchangeably referred to as a controller and/or a control device, of the present disclosure can be configured to, in an implementation, receive spectrum signatures of one or more IoT load devices in order to efficiently detect the presence of the load devices by comparing the received signatures with ones stored in the cloud/repository, and upon match, authenticate the IoT device to control operations thereof, through means such as RF/wireless/wired.

[00047] According to one embodiment, such load devices controlling means/control devices can include, but is not limited to, one or a combination of a laptop, tablet PC, mobile phone, smart phone, or any other smart device that can receive/access the frequency spectrum signature of one or more load devices, match the signature with stored spectrum signature(s), and control/manage the load device based on the same. Such a system architecture enhances the security of IoT platforms, wherein even if a new load device joins the network, the proposed load device controlling means would be able to determine that its unique frequency spectrum signature is not stored in its repository/database and hence either add the new load device or reject it based on the configuration of the system.

[00048] According to another embodiment, system of the present disclosure further enables networked IoT load devices to interact and share communications with each other, wherein when a message is transmitted from a sender IoT load device, the recipient IoT load device can communicate with the cloud database/central repository and match the stored frequency spectrum signature with that received along with the message from the sender IoT device. This enables secured communication between networked IoT load devices.

[00049] According to one embodiment, aspects of the present disclosure can implement a secured spectrum coding protocol (SSCP) configured to address security issues involved in IoT at the data link layer or part of communication stack by securely encoding and decoding spectral signature information of client-server system, wherein each load device/module can include/incorporate the SSCP as a software module. In another aspect, load device of the present disclosure can include any electrical load device including but not limited to LED lighting, motors, connected vehicle components, solar inverters, mobile devices, wearable devices, wireless chargers, among other loads that are integrated with a network communication protocol.

[00050] FIG. 1 illustrates an exemplary architecture 100 for controlling one or more load devices 104 in accordance with an embodiment of the present disclosure.

[00051] The present disclosure generally relates to the domain of IoT load device 104 (electrical device) energy management and control. In particular, the present disclosure relates to systems and methods for identifying/controlling/energy managing networked IoT load devices 104 based on their unique frequency spectrums. According to one embodiment of the present disclosure, an IoT load device such as 104-1, 104-2, ... 104-n, collectively referred to as IoT load device(s) 104 hereinafter, of the present disclosure can include a means to generate frequency spectrum of the IoT load device 104 based on one or more analog input signals (such as current, voltage, temperature, among other like input signals) to the device 104. In an aspect, frequency spectrum can be generated based on a Digital Signal Processor (DSP) 106 that can be configured to, based on the one or more input signal(s), implement an MVFFT (multi-variable FFT) algorithm and generate the frequency spectrum by measuring parameters from a switch mode power converter.

[00052] In an exemplary aspect, the MVFFT algorithm being used can include, but is not limited to, Fast Fourier transform, Short-Time Fourier transform (STFT), Gabor transform, and wavelet transform. In another aspect, one or more input signal parameters can be electrical and/or non-electrical parameters such as current, voltage, and temperature, which that can be used individually and/or in combination of each other in order to enable resulting frequency spectrum to create a unique signature for the load device 104, and this spectrum signature can then be used for uniquely identifying/controlling/detecting each IoT load device 104.

[00053] According to one embodiment, frequency spectrum signatures generated by one or more networked IoT load devices 104 can be stored in a cloud storage 108or any other repository/database 108such as a server that the load devices 104 are operatively coupled with.

[00054] According to one embodiment, system of the present disclosure can include one or more load device controlling means 102, also referred to as control device(s) and/or controller(s) 102,that can be configured to learn the frequency spectrum signature of one or more load devices 104 when the load devices 104 communicate with the controller(s) 102, and match the received frequency spectrum signature with the signature stored in the cloud database 106 in order to efficiently match and then detect the presence of the one or more networked IoT load devices 104 and control the operation thereof, through means such as RF/wireless/wired. One should appreciate that the present FIG. 1 shows only one control device 102, which is merely for simpler illustration of the present disclosure, and an actual implementation can always include one or more controllers/control devices 102.

[00055] According to one embodiment, load device controlling means 102 can include, but is not limited to, one or a combination of a laptop, tablet PC, mobile phone, smart phone, or any other smart device that can receive/access the frequency spectrum signature of one or more networked IoT load devices 104, match the signature with stored spectrum signature(s), and control/energy manage the load device 104 based on the same. Such a system architecture enhances the security of IoT platforms, wherein even if a new load device 104 joins the network, the proposed load device controlling means 102 would be able to determine that its unique frequency spectrum signature is not stored in its repository/database and hence either add the new load device 104 or reject it based on the configuration of the system.

[00056] According to another embodiment, system of the present disclosure further enables networked IoT load devices 104 to interact and share communications with each other, wherein when a message is transmitted from a sender IoT load device say 104-1, the recipient IoT load device say 104-2 can communicate with the cloud database/central repository 106, and match the stored frequency spectrum signature with that received alongwith the message from the sender IoT device 104-1. This enables secured communication between networked IoT load devices 104.

[00057] According to one embodiment, aspects of the present disclosure can implement a secured spectrum coding protocol (SSCP) configured to address the security issues involved in IoT at the data link layer or part of communication stack by securely encoding and decoding spectral signature information of client-server system, wherein each load device/module can include/incorporate the SSCP as a software module. In another aspect, load device 104 of the present disclosure can include any electrical load device 104 including but not limited to lighting devices such as LED modules, motors, connected vehicle components, solar inverters, mobile devices, wearable devices, wireless chargers, among other loads that are integrated with a network communication protocol.

[00058] According to one embodiment, use of MVFFT can lead to a better interpretation of current spectra of the load device in context, wherein, in an exemplary implementation for use of MVFFT, power density of measured phase current can be plotted, and results for different load devices can be compared to understand their difference in MVFFT output, and generate a signature to indicate such unique difference. Such differences can be contributed by presence of some well-defined sideband frequencies as well as magnitude differences in fundamental in harmonic spectrum of the measured line current. Use of MVFFT can also significantly reduce the amount of calculation required.

[00059] According to one embodiment, architecture of the present disclosure can also be configured such that load device controlling means 102 comprises a software API configured to calculate frequency spectrum signature of one or more load devices 104 (not having means to compute frequency spectrum signature at the device 104 end) at the load device controlling means 102 end, and control the load device(s) 104 based on such calculated frequency spectrum signature. Such a software API can be configured to implement spectral coding for securing communication between devices 102/load devices 104 and/or actuator networks.

[00060] FIG. 2 illustrates an exemplary representation showing battery powered sensor devices that can enable energy management based on their unique frequency spectral signature. As shown in FIG. 2, battery enabled sensor devices can include various application based sensors such as light sensor, position sensor, gas sensor, current sensor, flow sensor, temperature sensor, speed sensor, accelerometer, occupancy sensor, radiation sensor, pH sensor, gyro sensor, or any other sensor, which can include the architecture of the present disclosure such as include a DSP that can enable use of one or more electrical (such as input voltage, input current, boost charging, trickle charging, discharge profile, among other) and non-electrical parameters (such as temperature, time stamping, GPS coordinates, radiation, among others) to generate MVFFT and finally the frequency spectral signature of the device. The generated spectral signature can then be, using multiple means such as Ethernet, Zigbee, Bluetooth, WiFi or other similar means, sent and stored to a cloud storage medium so access to controllers/networked IoT load devices.

[00061] FIG. 3A illustrates generation and storage of unique frequency spectral signatures for networked IoT load devices in accordance with an aspect of the present disclosure. As shown, each load device 302-1, 302-2, 302-n can include a Digital Signal Processor (DSP) 304 having an information extraction engine 306, and a spectral signature generation engine 308. In an embodiment, each load device 302 can be processed by an ADC based on one or more electrical/non-electrical analog signal parameters as shown in Table-1, wherein post ADC processing, the information extraction engine 306 can measure the digital signal parameters by means of a measurement transducer 310 and undertake one or more additional actions such as scaling 312, normalizing 314, and digital filtering 316, wherein the filtered output can then be sent to the spectral signature generation engine 308 to process/generate the multi -variable FFT at 318, and interpret/classify the load device 302 across one or more parameters such as type of converter (buck, boost, buck-boost, SEPIC, Cuk, Full bridge, Forward, among others), power level (low, medium, high), load profile (switching frequency, harmonic content, frequency of use (ON/OFF cycles, duration per hr/day/week)), and nature of load (resistive, res-capacitive, res-inductive, non-linear, and switching) at block 320. Generated unique frequency spectrum can then be stored in a cloud database 322 through wireless communication as shown in FIG. 2.

Exemplary Electrical Parameters for Load Exemplary Non-Electrical Parameters for

Devices Load Devices

Input Voltage Variation Temperature Profile

Input Current Variation Time Stamping

Input Frequency Variation GPS Coordinates

Inductor Current (II) Variation - Ripple Radiation/Emission Profile

First Derivative of II Variation I2R Heat Dissipation-Reflected Power Drain Voltage of Fs Variation

Capacitor Current Variation

Capacitor Voltage (Vc) Variation

First Derivative of Vc Variation- Ripple

Input Power Factor

Average Input Power

Average Output Power

Efficiency

Table- 1

[00062] FIG. 3B illustrates generation and storage of unique frequency spectral signatures for networked battery-enabled IoT sensors in accordance with an aspect of the present disclosure. As shown, each sensor 352-1, 352-2, 352-n can include a Digital Signal Processor (DSP) 354 having an information extraction engine 356 and a spectral signature generation engine 358. In an embodiment, each load device 352 can be processed by an ADC based on one or more electrical/non-electrical analog signal parameters as shown in Table-2, wherein post ADC processing, the information extraction engine 356 can measure the digital signal parameters by means of a measurement transducer 360 and undertake one or more additional actions such as scaling 362, normalizing 364, and digital filtering 366, wherein the filtered output can then be sent to the spectral signature generation engine 358 to process/generate the multi-variable FFT at 368, and interpret/classify the sensor 352 across one or more classes/parameters such as type of battery or Super-cap (Li-ion, Ni-cad, Load Acid, among others), A-h or power level (low, medium, high), load profile (switching frequency, harmonic content, frequency of use (ON/OFF cycles, duration per hr/day/week)), and nature of load/type of sensor (resistive, res-capacitive, res-inductive, non-linear, and switching) at block 370. Generated unique frequency spectrum can then be stored in a cloud database 372 through wireless communication as shown in FIG. 2.

Devices Load Devices

Input Voltage Variation Temperature Profile

Input Current Variation Time Stamping

Boost Charging Profile GPS Coordinates Trickle Charging Profile Radiation/Emission Profile

Discharge Profile I2R Heat Dissipation-Reflected Power

Normal to Stand-by Switching Profile

Stand-by to Normal Switching Profile

Efficiency

[00063] FIG. 4A shows receiving of communication messages (having spectrum signatures) from one or more load devices by a controller and/or another load device to decipher the identity of load devices by comparing received spectrum signatures with ones stored in the cloud/server. As shown, the exemplary matching architecture comprises receiving spectral signatures 402-1, 402-2, 402-n from one or more load devices that are sending communication messages. The received encrypted communication messages having the spectral signatures can be received by DSP 404 of the control device/load device and undergo reverse MVFFT decryption process at 406. The signal parameters can then undergo scaling at 408, normalization at 410, and filtering at 412 to then enable comparison (at 414) of the spectrum signature with that stored in the cloud 418 and then classify/authenticate the load device accordingly at 416.

[00064] FIG. 4B shows receiving of communication messages (having spectrum signatures) from one or more sensors by a controller and/or another load device/sensor to decipher the identity of sensors by comparing received spectrum signatures with ones stored in the cloud/server. As shown, the exemplary matching architecture comprises receiving spectral signatures 452-1, 452-2, 452-n from one or more sensors that are sending communication messages. The received encrypted communication messages having the spectral signatures can be received by DSP 454 of the control device/load device/sensor and undergo reverse MVFFT decryption process at 456. The signal parameters can then undergo scaling at 458, normalization at 460, and filtering at 462 to then enable comparison (at 464) of the spectrum signature with that stored in the cloud 468and then classify/authenticate the load device accordingly at 466.

[00065] FIG. 5 illustrates an exemplary representation 500 showing generation of spectrum signature in accordance with an embodiment of the present disclosure. FIG. 5 illustrates an exemplary DSP configuration/architecture that can be incorporated in a load device, and can be configured to receive analog signals (such as current, voltage, and temperature), and implement an MVFFT algorithm to generate the frequency output spectrum for the one or a combination of signal, which can spectrum(s) can be processed to generate a spectrum signature that is unique to each load device.

[00066] FIG. 6 illustrates an exemplary set of steps 600 to convert an input parameter (such as current) to MVFFT to generate unique frequency spectrum based signature for a load device. As shown, the steps include a load at 602 to be processed through a current transducer 604, and then through an anti-aliasing filter 606, to finally convert from analog mode to digital mode through A/D converter 608 to generate the MVFFT output 610. One should appreciate that the above construction is completely exemplary in nature any other construction can be incorporated to generate a frequency spectrum for one or more load devices.

[00067] FIG. 7 illustrates exemplary representations of frequency spectrums (FIG. 7A showing 700 for Motor 1, and FIG. 7B showing 750 for motor 2) for two identical motors 1 and 2, wherein input signals are phase currents drawings from 2 Permanent Magnet Synchronous Motors (PMSM) motors and captured on ADC. Both the motors were configured to run on the same speed setting and hence had same input voltages. As can be seen from the below Tables3 and 4, the motors had difference in the side lobe frequency as well as in strength, which difference can be harnessed to create unique frequency spectrum based signature for each load device. Although the number of points taken for the experiment were 512, representation of the MVFFT values for the two motors 1 and 2 are being shown with respect to the top 20 points.

Parameter Motor 1 Motor 2

Frequency 312.5 Hz 312.5 Hz

Sampling Frequency 20000 20000

Points 512 512

Bin Resolution 39.0625 39.0625

Table 3

Point/Sr. No. MVFFT Value for Motor 1 MVFFT Value for Motor 2

0 0 5

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0 6 0 0

7 1 1

8 138 141

9 1 1

10 0 0

11 0 0

12 0 0

13 0 0

14 0 0

15 0 0

16 0 0

17 0 0

18 0 0

19 0 0

Table 4

[00068] In an aspect, various techniques and sequences can be used for MVFFT signature generation and device identification. In an exemplary implementation, following sequence of operations can be carried out in order to generate the MVFFT signature using various parameters and further to classify the device for precise identification. At step 1, real time data can be gathered and FFT can be computed. For instance, sample sensor data such as voltage (V), current (I), and temperature (T) can be gather at various load conditions, say at 10% increments, and peaks can be determined for each load condition and average can be computed to then perform FFT on each of these individual parameters (V, I, T) at these loads. In an exemplary implementation, two-dimensional matrices can be used for control and management as below:

A. V(peak, load), V(avg, %load)

B. I(peak, load), I(avg, %load)

C. T(peak, load), T(avg, %load)

In another aspect, three-dimensional matrices can be used to access/security and control (if required)

D. V(weight, bin, %load)

E. I(weight, bin, %load)

F. T(weight, bin, %load) [00069] At step 2, data packets can be generated, wherein, in an exemplary instance, the Load can be classified into 11 categories (0, 10, 20 ... 100). 4 bits can be reserved for %age of load. For example:

a. No load - 0000

b. 10% load - 0001

c. 20% load - 0010 and so on up to

d. 100% load - 1011

e. Over load - 1100 to 1111

In an aspect, parameter indicator can be assigned 2 bits to start with and extended later. Some parameters can be generated using the individual FFTs. For example:

a. Parameter A = FFT(V, bin) X FFT(I, bin) - indicates power - 00

b. Parameter B = FFT(V, bin) / FFT(I, bin) - indicates impedance - 01

c. Parameter C = FFT(V, bin) v/s FFT(I, bin) trajectory - 10

d. Parameter D = FFT(I, bin) v/s FFT(T, bin) trajectory - 11

In

an aspect, it is to be noted that some of these parameters are only indicators and the actual value may not mean anything since these are FFT weighted numbers. But, these should be good to differentiate between loads.

In an aspect, position of the bits in the message determines the harmonic or bin number - need not be explicitly mentioned to save data length and memory.

From the FFT at different loads, each frequency component can be classified as low, medium or high. For example:

a. If component is not present - 00

b. If component is present and of low weight (dB) - 01

c. If component is present and of medium weight (dB) - 10

d. If component is present and of high weight (dB) - 11

Hence, 2 bits for every frequency component - we can go up to the 10^th harmonic - will require total 20 bits. Actual value of the frequency component may not be very significant when we have FFTs operated on more than one parameter, therefore can be ignored and just assigning weights should be good. [00070] At step 3, classification of loads can be done based on the MVFFT signature, wherein, in an exemplary implementation, a 3 -dimensional image (or table) can be created to classify the loads based on:

a. MVFFT signature - gives data about the frequency content/harmonics of the load b. Peak Value - gives information on the switching and transient behavior of the load c. Average Value - gives information on the steady state behavior of the load

[00071] FIG. 8 depicts a 3 -dimensional image of the classification of loads using the three matrices described above namely MVFFT signature, peak value and average value on each of the three axes. This is an imaginative representation that can be in the form of a 3- dimensional table as well. This MVFFT information can be restricted to about 12 bytes, and stored along with the other parameters in a cloud database for comparison in real time during actual device identification. FIG. 9 illustrates an exemplary representation of MVFFT signature having 96 bits, wherein the arrangement of 12 bytes in the MVFFT signature is depicted in FIG. 9.

[00072] FIG. 10 illustrates an exemplary flow diagram for generation of MVFFT signature for a load device in accordance with an embodiment of the present disclosure. At step 1002, the load can be started after reset, and at 1004, user defined constants such as sampling frequency, FFT co-efficient, baud rate, among others can be incorporate to enable oscillator to be initialized to desired frequency. At step 1006, input and output ports can be configured for IOPS board and module, and at step 1008, high speed PWM peripheral can be configured and initialized. At step 1010, high speed ADC can be peripheral can be configured, and at step 1012, high USART peripheral can be configured and initialized. At step 1014, Bluetooth Enable Radio can be configured and advertised, and can also be re- advertised if the connection is lost. At step 1016, it can be checked if a data request is received, wherein if the request is received, MVFFT generation can be performed at step 1018 (and the MVFFT signature can be stored in storage), followed by BLE data transfer step at 1020 that involves retrieving the MVFFT data, wherein at step 1022, data is sent to mobile application for display. At step 1024, it can be checked if the connection is lost, in which case if the connection is lost, the method can go back to step 1014 else to 1016.

[00073] FIG. 11 A illustrates a flow diagram showing configuration of high speed ADC peripherals, wherein at step 1102, input voltage and load current can be converted by Pair 0 analog channels, and at step 1104, SMPS current and BLE module current can be converted by Pair 1 analog channels. At step 1106, Pair 2 analog channels can be used to convert temperature, and finally at step 1108, digital parameter can be stored.

[00074] FIG. 11B illustrates a flow diagram showing configuration of BLE radio, wherein at step 1132, BLE radio can be reset to factory settings, and at step 1134, the radio can be configured for user defined profile and peripheral based on user defined constants such as baud rate, and various identifiers for various attributes. At step 1136, private service can be cleaned and UUID can be generated, and at step 1138, device attributes can be transferred, and at 1140, BLE radio can be rebooted.

[00075] FIG. l lC illustrates a flow diagram showing MVFFT generation in accordance with an embodiment of the present invention, wherein at 1162, digital data from ADC can be scaled and normalized. At step 1164, digital filters can be applied for required bandwidth and frequency of interest. At step 1166, FFT can be generated for multiple FFT digital parameters, and at step 1168, 3D signature can be generated and data can be interpreted to classify loads and sensors. At step 1170, load taxonomy can be stored on cloud memory.

[00076] FIG. 1 ID illustrates a flow diagram showing BLE data transfer in accordance with an embodiment of the present invention, wherein at 1182, arrays in packet format can be re-arranged, and at 1184, MVFFT 3D signature packet can be generated, and at 1186, the proposed system can be used to broadcast advertisement or request message, wherein the message can include attributes such as average kWh, kVARh, and FFT trajectory.

[00077] The above description represents merely an exemplary embodiment of the present invention, without any intention to limit the scope of the present invention thereto. Various equivalent changes, alterations or modification based on the present invention are all consequently viewed as being embraced by the scope of the present invention.

[00078] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean "communicatively coupled with" over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.

[00079] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C ... .and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Claims

CLAIMS We Claim:

1. A load device comprising a digital signal processor (DSP) that is configured to receive one or more input analog parameters and, using a multi-variable FFT mechanism, generate a unique frequency spectrum signature for said load device, wherein said unique frequency spectrum signature is used by any of a control device or a second load device to uniquely identify and control said load device.

2. The load device of claim 1, wherein energy consumption of said load device is managed by any or a combination of said control device or said second load device using the spectrum signature.

3. The load device of claim 1, wherein said load device is selected from one or a combination of lighting device, motor, connected vehicle components, solar inverters, mobile devices, wearable devices, wireless chargers, and loads that are integrated with a network communication protocol.

4. The load device of claim 1, wherein said load device is networked with a plurality of load devices, and wherein said load device is Internet of Things (IoT) load device.

5. The load device of claim 1, wherein said one or more input analog parameters comprise one or a combination of electrical and non-electrical parameters, wherein said electrical parameters comprise voltage, current frequency, power based parameters, and wherein non-electrical parameters comprise temperature, timestamp, GPS coordinates, radiation, and heat dissipation based parameters.

6. The load device of claim 1, wherein said DSP is configured to implement a multi- variable FFT mechanism to generate said unique frequency spectrum signature for said load device.

7. The load device of claim 1, wherein said unique frequency spectrum signature for said load device is stored in a repository to enable said control device or said second load device to receive signature from said load device in real-time and compare the same with said stored unique frequency spectrum signature to match if said load device is authentic.

8. The load device of claim 1, wherein said control device comprises one or a combination of laptop, tablet PC, mobile phone, smart phone, and a smart device.

9. The load device of claim 1, wherein the load device is a sensor.

10. A controller device operatively coupled with one or more networked load devices, wherein at least one of said load devices comprises a digital signal processor (DSP) that is configured to receive one or more input analog parameters and use a multi- variable FFT mechanism to generate a unique frequency spectrum signature, wherein said unique frequency spectrum signature is used by said controller device to uniquely identify and control said load device.