Description:FIELD OF THE INVENTION
The present invention relates to a secure and efficient electricity management adapter (SEEMA) that provides real-time monitoring, analysis and management of electricity consumption through seamless integration with Internet of Things (IoT) platforms. More particularly, the present invention relates to a smart, low cost and scalable IoT-enabled electricity management adapter capable of securely, accurately, non-invasively and efficiently measuring, processing and transmitting electricity consumption data for enhanced energy management.

BACKGROUND OF THE INVENTION
Electricity management adapters and smart meters provide a fascinating leap on how we interact with and control energy usage. These technologies are designed to monitor, regulate and optimize the flow of electricity in homes, businesses and across grids. Smart meters are advanced devices that go beyond the analog meters by digitally tracking electricity consumption in real time. They use two-way communication systems often relying on wireless networks like radio frequency or cellular signals to send usage data to utilities and, in some cases, receive commands back which allows for dynamic adjustments like shifting power loads during peak times. The increasing demand for smart electricity management systems and devices has led to the development of smart electricity meters designed to provide real-time monitoring of energy consumption.
Traditional smart meters require periodic sensor recalibration to maintain accuracy, particularly when used across devices of varying power ratings. This lack of consistency in data measurement further limits their effectiveness in electricity management systems. While the use of IoT technologies is becoming increasingly popular for smart electricity management, most existing smart meters are not designed to seamlessly integrate with IoT networks. This lack of compatibility limits their ability to provide real-time monitoring, reporting and remote accessibility, thereby reducing their effectiveness in large-scale deployments.

The conventional smart meters currently available in the market, face several limitations that restrict their widespread adoption, particularly in developing countries such as India. The Government of India, under schemes such as the Revamped Distribution Sector Scheme (RDSS), the Smart Cities Mission and Atmanirbhar Bharat, aims to accelerate the adoption of smart energy monitoring systems. However, despite these efforts, existing smart meters continue to fall short in meeting the desired standards of affordability, data security, accuracy, scalability and compatibility with IoT frameworks.

Reference is made to the Indian patent application no. IN20232102407 disclosing an IoT based electricity consumption monitoring device to track daily energy consumption with the appropriate load. It consists of a current sensor to detects and converts current to a measurable output voltage, closed-loop sensor with a coil that is actively pushed to supply a magnetic field that opposes the field produced by using the current being sensed, an ESP8266 Controller to host the application or to offload Wi-Fi networking functions from another application processor; a relay module that is operated via an electromagnet using a separate low-energy signal from a micro controller; a LCD Display Shield is a flat-panel display or other electronically modulated optical device that uses the light-modulating properties of liquid crystals combined with polarizer’s; an I2C Module with a inbuilt PCF8574 I2C chip that converts I2C serial data to parallel data for the LCD display and a BLYNK application for the internet of things to control hardware remotely with open-source Arduino software program (IDE) adapted to makes it easy to write down code and add it to the board.
One of the primary issues associated with existing smart meters is their high cost making them financially impractical for large-scale deployment, particularly in domestic and small-scale industrial sectors. This cost factor significantly hinders efforts to improve energy efficiency and management across various applications.
Another major limitation is the lack of robust data security mechanisms. Most of the currently available smart meters either employ basic or outdated encryption techniques or in some cases do not incorporate any form of data security at all. This lack of adequate security leaves smart meters vulnerable to a wide range of cyber threats, including data tampering, unauthorized access and denial-of-service (DoS) attacks. Insecure data transmission can lead to privacy breaches, revenue losses and compromised system integrity, making it imperative to implement robust security measures to ensure reliable operation.
Therefore, there is a pressing need for a cost-effective, accurate, secure, scalable and IoT-enabled smart electricity management system that addresses the limitations of conventional smart meters and capable of real-time monitoring, processing, encryption and secure transmission of the data, while ensuring compatibility with evolving IoT platforms.
ADVANTAGES OF THE INVENTION OVER THE EXISTING STATE OF ART:
The secure and efficient electricity management adapter of the present invention is significantly cheaper compared to traditional smart meter. It utilizes the advanced NRG Morph encryption algorithm, incorporates tamper detection and HMAC integrity checks for robust data protection as compared to basic or absent encryption in conventional meters. The present invention addresses the limitation in the existing state of art by providing a comprehensive, low cost, smart, secure and efficient energy management adapter for monitoring and managing electricity consumption applicable to both domestic and industrial settings, thereby promoting the efficient use of energy resources and contributing to national and global energy-saving initiatives.

OBJECT OF THE INVENTION
In order to obviate the drawbacks in the existing state of the art, the main object of the present invention is to provide a secure, efficient, cost-effective and smart electricity management adapter capable of accurately measuring and monitoring real-time electricity consumption data in domestic, commercial and industrial environments.

Another object of the present invention is to provide an electricity management adapter configured with sensors including ZMPT-101 voltage sensor and SCT-013 current sensor for accurate and non-invasive measurement of electricity consumption.

Yet another object of the present invention is to provide an electricity management adapter with ESP32 microcontroller configured to continuously collect, preprocess, calibrate, compute, encrypt and securely transmit electricity consumption data, ensuring real-time and accurate monitoring of electricity consumption.

A further object of the present invention is to provide an electricity management adapter capable of transmitting the electricity consumption data through an NRG Morph encryption algorithm providing robust protection against data breaches and unauthorized access.

Yet another object of the present invention is to provide secure integrity verification of transmitted data through a cryptographic hash function using a Hash-based Message Authentication Code (HMAC) algorithm integrated within the microcontroller, ensuring data authenticity and reliability.

Yet another object of the present invention is to provide an electricity management adapter with seamless integration with Internet of Things (IoT) platforms enabling secure and real-time remote monitoring, management and analysis of electricity consumption data.

Yet another object of the present invention is to provide real-time periodic data updates on electricity consumption at predetermined periodic intervals aligning with Time-of-Day (ToD) tariff structures to enable users to optimize energy usage and cost efficiency.

Yet another object of the present invention is to provide an electricity management adapter with accurate measurement capabilities across electrical devices with varying power levels including both low-power devices and high-power devices.

Yet another object of the present invention is to provide an electricity management adapter capable of providing users with actionable insights into their electricity consumption patterns, encouraging responsible usage and reduced energy wastage.

Yet another object of the present invention is to provide a comprehensive low-cost, scalable and easily deployable electricity management system capable of widespread adoption in residential, commercial and industrial settings.

Yet another object of the present invention is to provide a method for a secure and efficient electricity management system capable of securely collecting, processing, encrypting, transmitting and analyzing consumption data for efficient electricity management.

SUMMARY OF THE INVENTION
The present invention “Secure and Efficient Electricity Management Adapter” (SEEMA) is an IoT-enabled smart device aimed at providing a comprehensive electricity management solution in residential, commercial and industrial environments. It addresses the limitations of existing systems by providing accurate, real-time monitoring of electricity consumption, secure data transmission and seamless integration with IoT platforms.
The SEEMA is a low cost, highly secure and scalable electricity management adapter equipped with sensors, a microcontroller and encryption mechanisms to ensure data integrity and confidentiality during transmission and incorporates real-time energy monitoring and encryption technologies to ensure secure data transmission. It utilizes sensors, SCT 013 for current and ZMPT-101 for voltage and an ESP32 microcontroller, which processes and encrypts data using the NRG Morph algorithm.
The ESP32 microcontroller facilitates real-time data processing, encryption using the NRG Morph algorithm and integrity verification using a Hash-based Message Authentication Code (HMAC) algorithm. The encrypted data is securely transmitted via Wi-Fi to IoT platforms, enabling advanced monitoring, analysis, optimization and management. It accurately measures the average voltage, average current, average power and accumulated energy (kWh) over predetermined periodic intervals, ensuring compatibility with Time-of-Day (ToD) tariff structures.
The present invention supports sustainable energy practices by providing actionable insights for reducing energy consumption and enhancing efficiency. The adapter can be powered either directly or via connections to the monitored electrical line or via external power sources.
The present invention is aimed at contributing to sustainable energy practices by reducing energy wastage and supporting responsible consumption.

BRIEF DESCRIPTION OF DRAWINGS
Fig.1 shows Structural block diagram of Secure and efficient electricity management adapter (SEEMA)
Fig.2 shows Flowchart depicting the method for secure and efficient electricity management using secure and efficient electricity management adapter.

DETAILED DESCRIPTION OF THE INVENTION WITH ILLUSTRATIONS AND NON-LIMITING EXAMPLES
While the invention has been disclosed with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt to a particular situation or material to the teachings of the invention without departing from its scope. However, one of the ordinary skills in art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded with the widest scope consistent with the principles and features described herein.
Throughout the specification and claims, the following terms take the meanings explicitly associated herein unless the context clearly dictates otherwise. The meaning of “a”, “an”, and “the” include plural references. Additionally, a reference to the singular includes a reference to the plural unless otherwise stated or inconsistent with the disclosure herein.
A person of ordinary skill in art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the way functions are performed. It is to be noted that the drawings are to be regarded as being schematic representations and elements that are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose becomes apparent to a person skilled in the art.
The present invention provides a Secure and Efficient Electricity Management Adapter (100) that addresses various limitations in existing systems by offering a low-cost, scalable and reliable solution for real-time electricity monitoring, processing, encryption and transmission. It is designed to integrate seamlessly with Internet of Things (IoT) platforms for advanced electricity management.
Fig. 1 illustrates the structural configuration of the Secure and Efficient Electricity Management Adapter (SEEMA). It provides a detailed representation of the components and how these components are integrated and interconnected to form a compact and efficient energy monitoring device.
The Secure and Efficient Electricity Management Adapter (SEEMA) (100) comprises the following essential components:
1. Voltage Sensor (101):
o The voltage sensor is a ZMPT-101 sensor configured to measure the voltage accurately from a monitored electrical line.
o It provides precise analog signals proportional to the voltage being monitored, which are transmitted to the microcontroller for processing.

2. Current Sensor (102):
o The current sensor is an SCT-013 sensor configured to measure current accurately and non-invasively.
o It provides analog signals corresponding to the current flowing through the monitored electrical line, which are transmitted to the microcontroller for processing.

3. ESP32 Microcontroller (103):
o The microcontroller is the central processing unit responsible for managing all operations of the SEEMA adapter.
o It continuously collects real-time voltage and current data from the voltage sensor (101) and the current sensor (102) at a predetermined sampling frequency.
o It preprocesses the collected data by applying filtering techniques and calibration constants to ensure accuracy.
o The microcontroller computes the following metrics:
 instantaneous power (product of instantaneous voltage and current).
 average voltage.
 average current.
 average power consumption.
 accumulated energy (kwh).
o It encrypts the processed data using the NRG Morph Algorithm, which employs a block cipher mechanism with a 256-bit encryption key and a 16-byte initialization vector (IV).
o It generates a cryptographic hash using a Hash-based Message Authentication Code (HMAC) algorithm to ensure data integrity.
o The microcontroller transmits the encrypted data along with the HMAC via an integrated Wi-Fi communication module.
Encryption Algorithm:
The present invention employs the advanced NRG Morph encryption al-gorithm for robust data protection, unlike the basic or absent encryption mechanisms used in conventional meters. The NRG Morph algorithm ensures data integrity and confidentiality by encrypting data with a 256-bit encryption key and a 16-byte initialization vector (IV). This robust en-cryption mechanism prevents unauthorized access, data tampering and injection attacks. It provides enhanced protection against Denial of Ser-vice (DoS) attacks by ensuring secure and reliable transmission of data over wireless networks, thereby overcoming one of the major drawbacks associated with existing smart electric meters.
4. Resistor (104):
o A 100-ohm resistor is used for signal stabilization.
5. Capacitor (105):
o A 10µF capacitor is used for energy storage and signal stabilization.
6. Connection Wires (106):
o Electrical wires interconnecting all components, including sensors, microcontroller, resistor and capacitor.
7. Printed Circuit Board (PCB) (107):
o The PCB integrates all components and provides a compact, organized layout.
o It enables efficient signal processing and transmission.
8. Power Supply:
o The adapter can be powered either by connections to the monitored electrical line itself or by any suitable external power supply source.

The secure and efficient electricity management adapter comprises of a voltage sensor (101) ZMPT-101 sensor configured to measure voltage accurately from a monitored electrical line. This sensor provides analog signals proportional to the voltage being monitored, which are transmitted to the microcontroller for processing. The adapter further comprises of a SCT-013 current sensor (102) configured to measure current accurately and non-invasively. This sensor provides analog signals corresponding to the current flowing through the monitored electrical line, which are also transmitted to the microcontroller for processing.
The core processing unit of the SEEMA is an ESP32 microcontroller (103), which manages all operations of the adapter. It continuously collects real-time voltage and current data from the sensors ZMPT-101 (101) and SCT-013 (102) at a predetermined sampling frequency of 10 Hz. The collected data is then preprocessed by the microcontroller through the application of filtering techniques and calibration constants to ensure accuracy. After preprocessing, the microcontroller computes various metrics, including instantaneous power, average voltage, average current, average power consumption and accumulated energy (kWh). These computations are performed at predetermined periodic intervals of thirty minutes to ensure compatibility with Time-of-Day (ToD) tariff structures.
The processed data is encrypted by the microcontroller using the NRG Morph Algorithm, which employs a block cipher mechanism configured with a 256-bit encryption key and a 16-byte initialization vector (IV). The encrypted data is then subjected to an additional layer of security through the generation of a cryptographic hash using a Hash-based Message Authentication Code (HMAC) algorithm integrated within the microcontroller. This cryptographic hash ensures the integrity of the data during transmission by verifying its authenticity and detecting any unauthorized modifications. Once the data is processed, encrypted and authenticated, it is transmitted along with the HMAC via an integrated Wi-Fi communication module to a designated IoT platform (200) for decryption and analysis.
The SEEMA further comprises of various auxiliary components that contribute to its overall functionality. A 100-ohm resistor (104) is included for signal stabilization, while a 10µF capacitor (105) is used for energy storage and signal stabilization. The various components of the adapter are interconnected by electrical wires (106) to ensure proper data flow and integration. All these components are mounted on a printed circuit board (PCB) (107), which provides a compact and organized layout, enabling efficient signal processing and transmission. The adapter can be powered either through direct connections to the monitored electrical line or via an external power supply source, enhancing usability and installation convenience.
The working of the SEEMA involves a series of operations designed to ensure secure and accurate data processing. The voltage and current sensors continuously collect real-time data from the monitored electrical line, which is then fed to the microcontroller for preprocessing. The preprocessing step involves applying noise filtering and calibration using predefined constants to enhance the accuracy of the collected data. Once the data has been preprocessed, the microcontroller computes the desired metrics, including instantaneous power, average voltage, average current, average power and accumulated energy.
After computation, the processed data is encrypted using the NRG Morph Algorithm,
a 16-byte initialization vector (IV). The encrypted data is further secured by generating an HMAC, ensuring data integrity during transmission. The data, along with the HMAC, is then transmitted via Wi-Fi to an IoT platform (200), where it is received and authenticated. The IoT platform (200) verifies the received hash using the same HMAC algorithm used by the microcontroller. If the authentication process is successful, the encrypted data is decrypted and made available for further analysis and reporting. This methodology ensures that the transmitted data remains secure, intact and confidential throughout the entire process.
The NRG Morph Algorithm serves as a critical component of the invention by enhancing data security through robust encryption mechanisms. It utilizes a block cipher mechanism designed to safeguard data during transmission, making it resistant to unauthorized access or tampering. The HMAC Generation Module ensures the integrity of transmitted data by generating cryptographic hashes that can be verified at the receiving end.
The adapter performs the data collection, processing, encryption and transmission operations, while the IoT platform (200) receives, authenticates, decrypts and stores the data for subsequent analysis and management. This architecture provides a scalable, low-cost solution for real-time energy monitoring, secure data transmission and integration with IoT platforms for enhanced electricity management. The invention further supports sustainable energy practices by providing users with actionable insights into their electricity consumption patterns, thereby encouraging responsible energy usage and enhancing overall efficiency.

Fig. 2 illustrates the method for secure and efficient electricity management using secure and efficient electricity management adapter. It outlines the step-by-step procedure starting from real-time data collection by the voltage sensor (101) and current sensor (102), followed by preprocessing and calibration, encryption and transmission performed by the microcontroller (103).
The method comprises of the following steps:
1. Initialization of Algorithmic Parameters
The method begins with basic parameters that include device-specific identifiers and calibration constants. The environment for real-time data collection regarding voltage and current is set up through the algorithm at a predetermined sampling frequency.

2. Data Collection:
 The sensors (101) and (102) continuously collect real-time voltage and current data from the monitored electrical line.
 The data is sampled at a predetermined sampling frequency and transmitted to the microcontroller (103).
3. Preprocessing & Calibration:
 The microcontroller preprocesses the raw data by applying noise filtering and calibration using predefined constants.
4. Computation of Metrics, data collection and analysis:
 The microcontroller calculates instantaneous power, average voltage, average current, average power and accumulated energy.
 The instantaneous power is calculated from voltage and current measurements through integration by the algorithm.
 These measurements are further integrated over the sampling interval to produce average values of voltage, current and power. These average values will then be used in calculating energy.
5. Energy audit:
 The said value then, using mean values for power, calculates the energy present in said 30 minutes. And further it was added to general account that could keep an energetic tally to put energy at its usage place.
 Computations are performed at predetermined periodic intervals (30 minutes) to align with Time-of-Day (ToD) tariff structures.
6. Data Arrangement for Communication:
- The algorithm returns a formatted string of average voltage, average current, average power consumption and average energy along with an identifier of the SM.

7. Data Encryption (NRG Morph Algorithm):
 The processed data is encrypted using a block cipher mechanism with a 256-bit encryption key and a 16-byte initialization vector (IV).
 It results in the plaintext form of a body of data transformed into ciphertext to protect it from unwarranted access.
 This ensures data confidentiality and prevents unauthorized access.
8. HMAC Generation (Data Integrity verification):
 The algorithm computes a cryptographic hash known as an HMAC. Which act as an identifier of the encrypted information and assists with the verification of authenticity and integrity of the transmitted data. upon its reception at the end
 The HMAC is attached to the encrypted data before transmission.
9. Data transmission preparation
 The encrypted data, along with the HMAC, is prepared for transmis-sion over a wireless communication module, such as Wi-Fi or cellular networks. The algorithm schedules periodic transmission to ensure real-time updates.
10. Data Transmission:
 The encrypted data along with the HMAC is transmitted to an IoT platform (200) via the integrated Wi-Fi communication module.
11. IoT Platform Integration:
 The electricity management adapter is integrated with an IoT platform (200) which receives the encrypted data and performs authentication by verifying the HMAC.

12. Receiver-Side Integrity Check and Decryption:
 Once received, the received hash is authenticated against the data us-ing the same HMAC.
 Upon successful authentication, the encrypted data is decrypted and made available for further analysis and reporting.
13. Algorithm Iteration
The algorithm then continues with this loop cycle; it gets new readings, recalibrate, calculates the new metrics and then sends encrypted data so as not to miss one minute in its constant monitoring of energy consumption.
The method for secure and efficient electricity management using secure and efficient electricity management adapter (100) starts with the collection of real-time voltage and current data from the monitored electrical line using the voltage sensor ZMPT-101 (101) and the current sensor SCT-013 (102). These sensors continuously gather analog signals, which are sampled at a predetermined sampling frequency and transmitted to the ESP32 microcontroller (103) for further processing. The microcontroller serves as the central processing unit responsible for handling all incoming data, which is subjected to a preprocessing phase where noise filtering and calibration are applied using predefined constants to ensure accuracy.
Once preprocessing is completed, the microcontroller proceeds to compute various metrics essential for monitoring energy consumption. These metrics include instantaneous power, average voltage, average current, average power and accumulated energy (kWh). The computations are carried out at predetermined periodic intervals of thirty minutes to align with Time-of-Day (ToD) tariff structures, allowing users to effectively monitor energy consumption and optimize usage based on the applicable tariff.
After the computations are performed, the processed data is subjected to an encryption process utilizing the NRG Morph Algorithm. This algorithm is a block cipher mechanism configured with a 256-bit encryption key and a 16-byte initialization vector (IV). The purpose of the NRG Morph Algorithm is to ensure data confidentiality by encrypting the processed data before it is transmitted to external platforms. The encrypted data is then further secured by generating a cryptographic hash using a Hash-based Message Authentication Code (HMAC) algorithm integrated within the microcontroller. The HMAC serves as an integrity verification mechanism, ensuring that the transmitted data has not been altered or tampered with during transmission.
Once the encryption and HMAC generation processes are completed, the encrypted data along with the generated HMAC is transmitted via an integrated Wi-Fi communication module to an IoT platform (200). The transmission of data is carried out securely to prevent unauthorized access or tampering. Upon receiving the encrypted data, the IoT platform (200) performs an authentication process by verifying the HMAC using the same algorithm employed by the microcontroller. This authentication step ensures the integrity of the data and confirms that it has been transmitted from a legitimate source. If the authentication process is successful, the IoT platform (200) proceeds to decrypt the encrypted data, converting it back to its original structured form for analysis and reporting. The decrypted data is then made available for further processing, such as energy monitoring, reporting and optimization based on various analytical frameworks.
The SEEMA adapter, functioning as part of a comprehensive electricity management system, performs all tasks related to data collection, processing, encryption and transmission. The IoT platform (200) acts as a receiver of the encrypted data, verifying its authenticity, decrypting the data and making it available for subsequent analysis and management. This architecture provides a robust framework for accurate, real-time energy monitoring while ensuring the confidentiality and integrity of the transmitted data.
The NRG Morph Algorithm plays a crucial role in securing the data by providing a robust encryption mechanism that prevents unauthorized access during transmission. The HMAC Generation Module further enhances data security by generating cryptographic hashes that can be verified at the receiving end, ensuring the data's integrity. Together, these components form a comprehensive security framework that allows the SEEMA adapter to operate as a low-cost, scalable and reliable solution for real-time electricity monitoring and efficient energy management.
Algorithmic implementation every 30 minutes:
Iteration 1: First 30 minutes
Smart Meter ID: SMART_METER_001
Average Voltage (V): 228.35
Average Current (A): 0.04
Average Power (W): 9.07
Accumulated Energy (kWh): 0.18
Encrypted Data:
f4669ebb62549c7c9c7f07b958d4cecb55aa6fdea7c153ff5d640acbdf74275cdcaf5e17ff6e8cd08c0a1249f64c799a1e77a8547c76c5dff4402d5b0ea30d5d6c8b8d6c57a4da4f8de8b23b0b313116afed
HMAC:
f4669ebb62549c7c9c7f07b958d4cecb55aa6fdea7c153ff5d640acbdf74275cdcaf5e17ff6e8cd08c0a1249f64c799a1e77a8547c76c5dff4402d5b0ea30d5d6c8b8d6c57a4da4f8de8b23b0b313116afed
Iteration 2: Next 30 minutes
Smart Meter ID: SMART_METER_001
Average Voltage (V): 228.35
Average Current (A): 0.04
Average Power (W): 8.11
Accumulated Energy (kWh): 0.18
Encrypted Data:
a31e5bb3a31e5bb38a88210c07238210c0723cd693b669cd693b669e4b6d36c0db6d36c0d7346ff87c743ff34e447338f35e0b0a763fcd1939843b32bdf55c4c30f49c82e2b523ca
HMAC: a31e5bb3a31e5bb38a88210c07238210c0723cd693b669cd693b669e4b6d36c0db6d36c0d7346ff87c743ff34e447338f35e0b0a763fcd1939843b32bdf55c4c30f49c82e2b523ca

Iteration 3: Next 30 minutes
Smart Meter ID: SMART_METER_001
Average Voltage (V): 228.33
Average Current (A): 0.06
Average Power (W): 12.76
Accumulated Energy (kWh): 0.36

Encrypted Data:
e0e15eb53e83670afbf68df7e306fe4552b783ab6012eb3a87e82fb869e7cd8bf20351cb5eab0fe33ec3cc44abf79be21ed0a74aa6fdad47ed5ba964687b632c413ad2abcf2f49
HMAC: e0e15eb53e83670afbf68df7e306fe4552b783ab6012eb3a87e82fb869e7cd8bf20351cb5eab0fe33ec3cc44abf79be21ed0a74aa6fdad47ed5ba964687b632c413ad2abcf2f49

The secure and efficient electricity management system (300) comprises of the secure and efficient electricity management adapter (100) and an IoT platform (200). The adapter (100) performs the data collection, processing, encryption and transmission operations. The IoT platform (200) receives, authenticates, decrypts and stores the data for subsequent analysis and management.
Cost-effectiveness and economic impact of the present invention:
The secure and efficient electricity management adapter (SEEMA) costs INR 1,345 per unit when compared to others smart meters that have been used in India and cost an average of around INR 2,500. The Government of India is targeting to install 250 million meters under RDSS by the year 2030, which could lead to an estimated savings approximately INR 28,000 crores.

This could potentially impact global consumption by saving 10–15% i.e. approximately 2,000 TWh annually, leading to global savings of about $200 billion with an average electricity rate of $0.10 per kilowatt-hour and could cut down energy theft and billing errors by as much as 50%, with a yearly savings of approximately INR 25,000 crores just in India. By increasing energy efficiency, smart meters could contribute to long-term global economic growth. It has been estimated that a 1% increase in energy efficiency may boost global GDP by as much as $5 trillion by 2030.
, Claims:1. A secure and efficient electricity management adapter (100) comprising:
a) at least one voltage sensor (101), wherein said voltage sensor is a ZMPT-101 sensor configured to measure voltage accurately;
b) at least one current sensor (102), wherein said current sensor is an SCT-013 sensor configured to measure current accurately and non-invasively;
c) at least one microcontroller unit (103), wherein said microcontroller unit is an ESP32 microcontroller connected to said voltage sensor (101) and current sensor (102);
d) at least one resistor (104) for signal stabilization;
e) at least one capacitor (105) for energy storage and signal stabilization;
f) at least one set of connection wires (106) connecting components (101), (102), (103), (104) and (105); and
g) at least one printed circuit board (PCB) (107) configured for integrating components (101), (102), (103), (104), (105) and (106),
wherein, said microcontroller (103) is configured to:
 continuously collect real-time voltage and current data at a predetermined sampling frequency;
 preprocess and calibrate the collected data using predefined calibration constants;
 calculate instantaneous power and compute average voltage, average current, average power and accumulated energy over every predetermined periodic interval;
 encrypt processed data using an integrated NRG Morph encryption algorithm (NRG) with a 256-bit encryption key and a 16-byte initialization vector (IV);
 generate a cryptographic hash using a Hash-based Message Authentication Code (HMAC) algorithm integrated within the microcontroller (103) to ensure data integrity; and
 transmit the encrypted data along with the generated cryptographic hash via a Wi-Fi communication module.
2. The electricity management adapter (100) as claimed in claim 1, wherein said adapter (100) is configured for integration with at least one Internet of Things (IoT) platform (200), enabling secure, real-time encrypted data transmission for energy monitoring, analysis and management.
3. The electricity management adapter (100) as claimed in claim 1, wherein the microcontroller unit (103) comprises an integrated wireless communication module enabling secure and efficient transmission of encrypted data packets and cryptographic hashes (HMAC) via Wi-Fi without requiring an additional external communication module.
4. The electricity management adapter (100) as claimed in claim 1, wherein the cryptographic hash is generated using a Hash-based Message Authentication Code (HMAC) algorithm integrated within the microcontroller (103) to authenticate transmitted data and verify data integrity upon reception.
5. The electricity management adapter (100) as claimed in claim 1, wherein the NRG Morph encryption utilizes a block cipher mechanism with a 256-bit encryption key and a 16-byte initialization vector (IV).
6. The electricity management adapter (100) as claimed in claim 1, wherein the current sensor (102) is configured to be attached non-invasively to a power line thereby providing ease of installation and enhanced safety.
7. The electricity management adapter (100) as claimed in claim 1, wherein the ZMPT-101 voltage sensor (101) is configured to provide precise voltage measurements with high sensitivity for accurate energy monitoring.
8. The electricity management adapter (100) as claimed in claim 1, wherein the microcontroller (103) is configured to cyclically repeat the steps of data collection, preprocessing, encryption, cryptographic hashing and transmission and provide data updates every predetermined periodic interval aligned with Time-of-Day (ToD) tariff structures.
9. The electricity management adapter (100) as claimed in claim 1, wherein the encrypted data comprises:
 average voltage readings;
 average current readings;
 average power consumption; and
 accumulated energy (kwh).

10. The electricity management adapter (100) as claimed in claim 1, wherein the adapter (100) is powered via connections to the monitored electrical line or any suitable external power supply source.

11. The electricity management adapter (100) as claimed in claim 1, wherein said adapter (100) is configured to accurately measure energy consumption from electrical devices with varying power levels including both low-power and high-power devices.

12. A secure and efficient electricity management system (300) comprising:
 at least one secure electricity management adapter (100) as claimed in claim 1; and
 an Internet of Things (IoT) platform (200) configured to:
• receive the encrypted data and HMAC transmitted from said adapter (100);
• authenticate the received hash against the transmitted data using the same HMAC algorithm; and
• decrypt the encrypted data back to its original structured form for analysis and reporting purposes upon successful authentication,
wherein said electricity management system (200) provides users with actionable insights into their electricity consumption patterns, encouraging responsible usage and reduced energy wastage.

13. A method for secure and efficient electricity management using secure and efficient electricity management adapter (100), comprising the steps of:
a) measuring voltage and current using ZMPT-101 voltage sensor (101) and SCT-013 current sensor (102) at a predetermined sampling frequency;
b) preprocessing and calibrating the collected data;
c) calculating instantaneous power, average voltage, average current, average power and accumulated energy;
d) encrypting the processed data using the NRG Morph algorithm;
e) generating a Hash-based Message Authentication Code (HMAC) for data integrity; and
f) transmitting the encrypted data and HMAC to an IoT platform (200).
wherein,
- said transmission of the encrypted data packets and cryptographic hashes (HMAC) to said IoT platform (200) is performed using a Wi-Fi module integrated within said microcontroller (103) enabling secure and efficient transmission without requiring an additional external communication module; and
- said transmitted data is authenticated using the generated HMAC and decrypted back to its original structured form for analysis and reporting purposes upon successful authentication.