Description:Title of the Invention

“Decentralized System and Method for Secure Biometric Identity Management, Context-Aware Wellness Monitoring, and Federated Learning Using LoRa-Enabled Devices”

Field of the Invention

This invention relates generally to decentralized health and wellness monitoring systems, and more particularly to a biometric authentication-enabled, AI-powered, and privacy-preserving system for real-time personal wellness management using Low Power Wide Area Networks (LPWANs) such as LoRaWAN. The invention resides at the intersection of embedded systems, wireless communication, edge AI, federated learning, cybersecurity, and biometric identity management.

The invention is applicable to multiple technical domains, including but not limited to:

Wearable technology, Smartphones and consumer health electronics

Internet of Things (IoT) and Industrial IoT (IIoT)

Secure multi-user environments (e.g., smart homes, smart offices)

Telemedicine and digital therapeutics

Behavioral and mental wellness platforms

Smart city infrastructure and mobile edge computing

Remote healthcare monitoring in rural and low-bandwidth environments

AI-assisted preventive healthcare systems

This invention further extends to applications in data privacy engineering, context-aware systems, cross-device synchronization protocols, and long-range low-power communication technologies.

The field thus integrates hardware-software co-design to address limitations in centralized wellness platforms, ensuring improved data sovereignty, personalized context analysis, and resilient connectivity in diverse geographies and usage contexts, especially where traditional broadband is infeasible.

Background of the Invention

[0001] The global proliferation of wearable devices, fitness trackers, and health sensors has enabled unprecedented access to physiological and behavioral data. However, these devices typically rely on centralized servers for data aggregation, user authentication, and AI-based analytics, leading to serious concerns around latency, privacy, network dependency, and infrastructure scalability—especially in low-connectivity regions.

[0002] Biometric-based security systems—such as fingerprint, iris, or heart-rate recognition—are increasingly integrated into consumer electronics and smart environments. However, most implementations require that biometric templates be transmitted to or verified by cloud servers, exposing users to potential breaches and regulatory non-compliance (e.g., GDPR or India’s Personal Data Protection Act). Moreover, low-power devices lack the computational resources and battery capacity for heavy cryptographic and AI operations.

[0003] Simultaneously, context-aware systems are evolving to provide intelligent behavioral cues and personalized wellness recommendations. But prevailing approaches suffer from generic, non-adaptive alerts that fail to account for user context, emotional state, or environmental stimuli, resulting in low engagement and limited efficacy.

[0004] Federated learning has emerged as a privacy-preserving solution for training AI models across distributed devices without transferring raw data. However, federated learning systems often presume the availability of high-bandwidth Internet (e.g., Wi-Fi or 4G), which is not suitable for rural, remote, or industrial deployments where power and connectivity constraints prevail.

[0005] Low-Power Wide-Area Networks (LPWANs) such as LoRaWAN provide long-range, energy-efficient wireless communication well-suited for battery-powered devices in such environments. LoRa’s typical range (2–15 km) and low energy footprint make it ideal for non-invasive health and safety monitoring, environmental sensing, and infrastructure management. However, LoRa has not been effectively integrated with real-time biometric authentication, cross-device identity sync, or federated AI in existing systems.

[0006] No existing solution offers a unified platform that combines:

Hardware-enforced biometric privacy through on-device secure enclaves,

Contextual, AI-powered wellness management using embedded machine learning,

Encrypted cross-device synchronization of user identity tokens, and

Federated learning frameworks over resource-constrained LoRa networks.

[0007] Moreover, India faces specific infrastructural challenges such as:

Intermittent connectivity in Tier-2 and rural areas

Low device replacement cycles

Higher demand for battery-efficient solutions

Regulatory constraints around biometric data movement (e.g., Aadhaar-linked systems)

This invention addresses these gaps through a technically novel, scalable, and privacy-respecting architecture that combines:

Secure edge authentication

Context-sensitive AI

Federated model training

LoRaWAN-based identity propagation and device interoperability

[0008] Accordingly, there exists a strong need for a decentralized, scalable, and secure wellness management platform that protects sensitive user data, operates efficiently across constrained devices, and remains resilient in bandwidth-limited environments—all while delivering meaningful, adaptive wellness support to users in real time.

Summary of the Invention

[0009] The present invention provides a decentralized, context-aware, secure wellness and identity management system that integrates biometric authentication, edge-based artificial intelligence (AI), profile synchronization, and privacy-preserving federated learning, all enabled through Low Power Wide Area Networks (LPWANs) such as LoRaWAN. The invention is directed to technical improvements in how personal health and identity data are securely processed, analyzed, and shared across multiple devices without centralized dependence, thereby reducing latency, enhancing privacy, and increasing energy efficiency.

[0010] Each device in the system—be it a wearable, smartphone, or IoT sensor module—is equipped with:

One or more biometric input devices (e.g., fingerprint sensor, PPG-based heart rate sensor, or iris scanner),

A secure processing enclave (e.g., ARM TrustZone or equivalent Trusted Execution Environment),

An on-device context detection module running a lightweight machine learning model (e.g., a quantized neural network),

A LoRa communication transceiver.

These components together enable local authentication, context inference, and inter-device coordination via encrypted communication.

[0011] Upon biometric authentication, the system generates a cryptographic identity token within the secure enclave. This token is securely transmitted via LoRa to other paired devices, enabling profile synchronization across heterogeneous environments (e.g., transitioning from home to office, or between medical and recreational modes). This eliminates the need for a centralized cloud server to maintain user preferences or settings, and no raw biometric or sensor data is ever transmitted or stored centrally.

[0012] In addition, the invention incorporates a federated learning mechanism over the LoRa network. Each device performs local model training using freshly collected user and environment data. At scheduled intervals, encrypted model updates (e.g., gradients or weights) are transmitted to a federated learning aggregator node, which may reside in the cloud, on a server gateway, or at the network edge. The aggregated model is then redistributed back to devices, thus continuously improving performance without violating data sovereignty or user privacy.

[0013] Each device can support multiple user profiles, distinguished by distinct biometric signatures. In certain configurations, automated context triggers—such as geolocation, environmental sensing, or time-of-day—may further switch device behavior or user modes, providing seamless operation for individuals or shared family environments.

[0014] The invention achieves technical advantages such as:

Localized, on-device decision-making with real-time latency,

Secure identity management without transmitting sensitive raw data,

Adaptive personalization based on individual behavioral and biometric context,

Compatibility with low-power networks and battery-operated environments,

Extensibility to rural, industrial, healthcare, and enterprise IoT domains,

Scalability to support multi-user, multi-device, and geographically distributed deployments.

[0015] By coupling hardware-bound security mechanisms, edge AI capabilities, and low-power decentralized communication, the present invention offers a robust, privacy-compliant, and scalable platform for smart wellness monitoring, identity synchronization, and decentralized AI coordination. These features collectively ensure broad patent-eligible subject matter under Indian law (overcoming Section 3(k)), while also aligning with global patentability criteria (e.g., inventive step, enablement, clarity, and industrial applicability).
The invention also introduces novel mechanisms such as entropy-based biometric token generation, trust-graph-based identity propagation, model drift detection, and enclave-anchored blockchain logs, offering additional layers of privacy, adaptability, and post-quantum security.

Brief Description of the Drawings

[0016] FIG. 1 – System Architecture Overview:
A block-level system diagram showing the interconnection of LoRa-enabled devices (smartphones, wearables, environmental sensors) with LoRaWAN gateways and network servers. The figure highlights bidirectional communication, secure token exchange, and federated learning coordination.

[0017] FIG. 2 – Device Architecture:
A schematic of a typical LoRa-enabled edge device, illustrating its biometric sensors (e.g., fingerprint, PPG), secure enclave processor, edge inference engine, LoRa transceiver, memory, and optional auxiliary components such as BLE modules or geolocation sensors.

[0018] FIG. 3 – Federated Learning Workflow:
A flowchart showing local model training using device-specific data, encryption of parameter updates, LoRa-based transmission to an aggregator, model averaging at the aggregator, and redistribution of the updated global model to devices.

[0019] FIG. 4 – Cross-Device Identity Token Synchronization:
A flowchart illustrating the sequence of biometric authentication on a first device, token generation, encrypted token transfer over LoRa, token decryption by a second device, and secure profile synchronization based on the received token.

Detailed Description of the Invention

System Overview

[0020] The present invention discloses a decentralized system and method for user authentication, wellness monitoring, and AI model training using a distributed network of LoRa-enabled devices, such as smartphones, wearables, environmental sensors, and industrial IoT modules. The system emphasizes technical integration of hardware (e.g., secure enclaves, biometric sensors) and software (e.g., context inference engines, federated learning clients), to ensure privacy-preserving, low-latency, and personalized services across diverse environments.

[0021] In one embodiment, the system comprises a plurality of edge devices, each equipped with a secure biometric processing unit, a context-aware AI module, and a LoRa transceiver. These devices communicate wirelessly via a LoRaWAN infrastructure, which includes one or more gateways and optionally a centralized or distributed network server. Data exchange among devices is secured using symmetric and/or asymmetric encryption protocols, implemented at the hardware level via Trusted Execution Environments (TEEs).

[0022] The key components of each device may include:

Biometric Sensors: Fingerprint scanner, photoplethysmographic (PPG) heart-rate sensor, electrocardiogram (ECG) sensor, iris/retina scanner, or skin conductance sensor.

Secure Enclave: A physically isolated co-processor (e.g., ARM TrustZone, Intel SGX) responsible for cryptographic operations, biometric template storage, and token generation.

Context Sensing Module: Inertial Measurement Unit (IMU), barometer, thermometer, light sensor, noise sensor, gas sensor, or GPS module.

Edge AI Engine: A low-power processor (e.g., Cortex-M4/M33, RISC-V) executing lightweight ML models for context classification (e.g., posture, activity, emotional state).

Wireless Module: LoRa transceiver with Class A operation and AES-128 hardware support; optionally Bluetooth Low Energy (BLE), Zigbee, or Wi-Fi for local synchronization.

Biometric Authentication and Token Generation

[0023] Each user device performs biometric authentication entirely on-device, in contrast to traditional cloud-based methods. A fingerprint or heart-rate scan is captured by the biometric sensor and routed directly to the secure enclave. The enclave executes a template-matching algorithm internally, ensuring that raw biometric data is never exposed to other parts of the device or network.

[0024] Upon successful matching, the secure enclave generates a cryptographic identity token. This token may be a hash-based message authentication code (HMAC) derived from the biometric template and a device-specific secret key, or a digital signature using an asymmetric key pair generated within the enclave.

[0025] This token functions as a non-reversible, non-transferable representation of the authenticated identity and is stored temporarily for propagation to other authorized devices.

[0025A] In one embodiment, token validity may be dynamically determined by real-time bio-signal entropy, such as heart-rate variability (HRV) or galvanic skin response entropy. This entropy-derived freshness ensures that tokens are biologically unique to a moment in time and cannot be reused even if intercepted. This temporal uniqueness enhances resilience against replay and spoofing attacks.

Cross-Device Synchronization

[0026] The invention supports automatic profile synchronization across devices owned by the same user. When the identity token is generated, it is transmitted over the LoRa network to nearby trusted devices. The receiving device verifies the authenticity and freshness of the token using its own enclave and then activates or merges the corresponding user profile.

[0027] For example, a user authenticates via fingerprint on a smartwatch at home. The watch sends an encrypted token over LoRa to the user’s smartphone at the office. Upon token receipt, the smartphone automatically switches to the user’s personalized interface (e.g., preferred brightness, app layout, or notification settings).

[0027A] Token propagation among devices may also utilize a dynamically evolving trust graph. Each device maintains a lightweight trust ledger, incrementally updated based on successful prior authentication events, enclave attestation receipts, and token usage history. This ledger adjusts the priority and acceptance confidence for tokens received from peer devices, providing resistance against rogue or cloned node injection.

[0028] This synchronization happens without transmitting raw biometric or personal data, satisfying privacy and data sovereignty requirements in jurisdictions such as India, the European Union (GDPR), and the U.S. (HIPAA, CCPA).

On-Device Context Inference

[0029] The system enables real-time context inference using embedded machine learning models. Each device continuously captures signals from motion, physiological, and environmental sensors. These inputs are processed locally by a quantized neural network, support vector machine, or decision tree ensemble, depending on memory and energy constraints.

[0030] Use cases include:

Posture Correction: Classifying slouched vs. upright sitting using gyroscope and accelerometer data.

Stress Detection: Inferring user stress levels from heart-rate variability and skin conductance.

Sleep Detection: Combining motion inactivity and ambient noise level to determine sleep onset and duration.

Ergonomic Prompting: Providing subtle vibration or audio cues when poor posture or prolonged inactivity is detected.

[0031] The inference models are optimized for low-resource operation and may be implemented using frameworks such as TensorFlow Lite Micro, TinyML, or CMSIS-NN, depending on the device’s architecture.

[0031A] The context inference module includes a drift detection mechanism based on entropy shift analysis, model prediction confidence drop, and deviation from baseline behavior metrics. If drift is detected, the device flags the model for re-personalization via federated learning, ensuring long-term relevance of AI inference.

Federated Learning Over LoRa

[0032] To enhance model accuracy over time, the invention employs a federated learning framework wherein each device trains a local copy of the context model on collected sensor data. At scheduled intervals (e.g., nightly or during idle/charging phases), the device encrypts its model update and transmits it over LoRa to a central or distributed federated aggregator.

[0033] The update can include:

Full model weights (e.g., for small neural networks)

Compressed gradient deltas (e.g., for larger models)

Differential privacy noise masks to obfuscate individual contributions

[0034] The aggregator performs secure model fusion using techniques like FedAvg, Secure Aggregation, or Homomorphic Encryption, ensuring that no raw data ever leaves the device.

[0035] The updated global model is then re-broadcast to the devices, allowing each one to enhance its inference capabilities while preserving user privacy.

Profile Switching via Context Triggers

[0036] In addition to biometric authentication, devices may switch user profiles based on contextual triggers. These include:

Geofencing: Switching from personal to professional profile when entering an office.

Time-of-day: Switching to “sleep mode” at bedtime.

Environmental Cues: Switching to “outdoor mode” based on UV or noise levels.

[0037] Profile switching can also be combined with biometric scans to fine-tune personalization, e.g., different behavior profiles for the same user based on day vs. night usage.

Security Architecture

[0038] Security is a core component of the invention. All cryptographic operations (key generation, encryption, signature verification) are executed inside the secure enclave. Communication over LoRa is encrypted using AES-128 at the physical and MAC layer as per the LoRaWAN standard. Application-layer security can be enhanced via:

TLS over IP (for LoRa gateways connected to the internet)

Session-based symmetric keys

Public key infrastructure (PKI) for multi-device authentication

[0039] In case of BLE fallback or auxiliary channels, equivalent encryption protocols are used (e.g., BLE Secure Connections with Elliptic Curve Diffie-Hellman).

[0040] Even in the event of physical device compromise, user identity and biometric templates remain protected due to hardware isolation of the secure enclave.

Device Architecture Options

[0041] Multiple hardware configurations are supported, including:

Wearables: Wristbands with PPG, accelerometer, and LoRa modules.

Smartphones: Android devices with embedded fingerprint sensors and LoRa modules in USB/SoC form.

Stationary Nodes: Fixed environmental sensors for industrial monitoring.

Medical Devices: Portable diagnostic tools with pulse oximetry, ECG, and context adaptation.

[0042] Each configuration supports modular integration, allowing manufacturers to select components based on deployment environment, user population, and use-case scenario.

[0042A] Devices may include optional wake-up radios that operate at ultra-low power to listen for identity handoff signals. These radios remain active while the main processor is idle and activate the secure enclave only upon receiving a trusted identity handoff packet, conserving energy during prolonged inactive periods. Optionally, energy harvesting modules (e.g., piezoelectric or solar) may assist long-term battery-free deployment.

Alternative Communication Modes

[0043] While LoRa is the primary communication protocol, the system may optionally support:

BLE for short-range identity handoff

NB-IoT or LTE-M for backup cellular communication

Wi-Fi Direct in urban deployments

Satellite LPWAN (e.g., Lacuna, Swarm) in remote regions

This enables the invention to adapt to variable infrastructure without altering its core decentralized identity and wellness features.

Multi-User and Multi-Device Use Cases

[0044] The system supports multiple authenticated users on the same device, each with:

Distinct biometric templates

Personal settings/preferences

Context-triggered profiles

[0045] For example, a single wearable used in a shared household can authenticate different users based on fingerprint or heart-rate pattern, apply personalized wellness prompts, and upload model updates tagged pseudonymously.

[0046] Additionally, a single user operating multiple devices (e.g., smartphone, smartwatch, desktop monitor) will benefit from seamless identity propagation and synchronized behavior across platforms.

Compliance with Section 3(k) of Indian Patent Act

[0047] This invention is a technical solution to a technical problem, as required by Indian jurisprudence on Section 3(k). It:

Integrates hardware (secure enclaves, biometric sensors)

Employs system-level architecture (LoRa LPWAN + federated AI)

Solves latency and privacy issues in real-world, power-constrained settings

The invention does not claim computer programs per se, but rather the functionality and technical effects of a hardware-enabled system with embedded intelligence.

Summary of Technical Contribution

[0048] The invention addresses the technical challenge of decentralized user identity management in wellness ecosystems where bandwidth, energy, and privacy constraints prevent cloud-based operation.

[0049] It further provides adaptive, real-time feedback based on individualized context inference, using models refined across a federated learning mesh without raw data sharing.

[0050] It enables scalable deployment in heterogeneous settings (home, rural clinics, industrial plants) with low power demands and no dependence on persistent internet connectivity.

Detailed Description of the Invention

Hypothetical Use Cases and Applications

[0051] The invention is versatile and adaptable across multiple domains. The following hypothetical use cases illustrate how the system functions under real-world constraints and variable conditions while preserving user privacy and delivering context-specific outcomes.

[0052] Use Case 1 – Rural Health Monitoring:
A community healthcare worker in rural India distributes wearables to diabetic and hypertensive patients. The devices authenticate users locally using fingerprint or heart-rate signals and collect activity and stress data throughout the day. In the absence of cellular coverage, LoRaWAN gateways at the health center aggregate encrypted model updates overnight and synchronize treatment suggestions via context-aware prompts (e.g., hydration alerts, rest cycles).

[0053] Use Case 2 – Industrial Worker Wellness:
In a factory, all workers wear LoRa-enabled helmets with embedded PPG sensors and accelerometers. The system infers stress and fatigue levels and prompts breaks via localized audio signals. A factory supervisor's tablet synchronizes worker profiles using LoRa tokens and receives only aggregated wellness summaries. Federated learning continuously improves detection models specific to factory noise and vibration.

[0054] Use Case 3 – Elderly Care in Smart Homes:
A smart home for elderly residents deploys LoRa-connected devices with iris-based biometric authentication. When a resident transitions from their bed to a common area, their token is passed to the local HVAC and lighting controller, which adjusts the environment to pre-set preferences. The same token enables personalized medication reminders at appropriate intervals via wearables.

[0055] Use Case 4 – Educational Mental Wellness System:
In an urban school, students are provided with smartbands that detect stress or agitation through GSR and heart-rate variability. When elevated stress is detected during exams, context-aware interventions (like guided breathing via mobile notifications) are triggered. Teachers receive aggregated insights (without identifying data) on classroom wellness levels.

[0056] Use Case 5 – Disaster-Resilient Health Tracking:
During a flood scenario in a remote region, battery-powered LoRa-enabled health monitors continue functioning without network infrastructure. Users authenticate locally, and their encrypted model updates are cached in edge nodes mounted on drones. Once backhaul connectivity is re-established, all updates are batch-fused at the cloud.

Proposed Additional Figures

[0057] Use Case 6 – Military Forward Post Health Intelligence:
In field-deployable scenarios for the armed forces, soldiers wear biometric-enabled gear that transmits vital signs (e.g., fatigue, dehydration) via LoRa to command tents or drone-mesh relay stations. Real-time status assessments are conducted locally using edge AI, and model updates are batch-synced securely. Intermittent communication and zero internet dependence make this critical for mission endurance and survivability.

[0058] Use Case 7 – High-Altitude Workforce Safety:
Workers at wind farms, mountainous mines, or offshore platforms use embedded biometric wearables that switch safety protocols (e.g., fall detection, UV exposure alert) based on context models. LoRa ensures range, while fallback via BLE allows cabin hubs to synchronize profiles even in shielded structures.

[0059] Use Case 8 – High-Security Facilities Access Control:
Access terminals in nuclear plants or R&D labs authenticate users via local iris scan. The system verifies identity via secure enclave-generated tokens without central servers. Context (e.g., location or radiation index) may further restrict access by triggering elevated authentication thresholds.

Device-to-Device Communication and Routing

[0060] The invention supports peer-to-peer synchronization of identity tokens and model weights. Devices dynamically form local routing clusters, electing a leader (e.g., gateway or highest-capacity node) to aggregate updates before upstream transmission. Token propagation is governed by a trust model based on biometric source authenticity, key freshness, and physical proximity.

[0061] Fallback mechanisms are included to allow BLE or Wi-Fi direct-based identity propagation when LoRa is unavailable. Devices detect ambient signals, handshake securely using elliptic curve Diffie–Hellman (ECDH), and re-initiate token sync. Upon LoRa availability, changes are pushed upstream for global federation consistency.

Advanced Encryption and Cryptography

[0062] To future-proof against emerging security threats, the system supports pluggable cryptographic layers:

Symmetric Encryption: AES-128 or ChaCha20 for payloads.

Asymmetric Encryption: ECC (P-256, Curve25519) or RSA-2048 for key exchange.

Quantum-Resistant Options: Lattice-based algorithms (e.g., Kyber) supported via enclave-upgradable firmware modules.

[0062A] The use of post-quantum cryptographic primitives such as CRYSTALS-Kyber and Dilithium future-proofs the system against emerging threats from quantum computing, ensuring the invention remains secure beyond current encryption standards.

[0062B] The invention also supports quantum-safe blockchain anchoring of enclave-generated attestation logs. These logs, containing cryptographic hashes of enclave integrity states, provide audit trails that are tamper-evident, ensuring post-event verifiability for security-critical use cases such as defense and regulated healthcare deployments.

[0063] Token freshness and revocation are ensured by time-limited signatures or one-time-use session keys, with replay protection enforced by nonce-based challenge-response routines. In deployments with blockchain integration, hash fingerprints of model updates can be anchored on a distributed ledger to enable auditability and tamper detection.

AI Model Management and Versioning

[0064] To accommodate heterogeneity across devices, the system uses model version control, ensuring each device operates on a compatible model version. Devices with limited RAM may use distilled or pruned models. Metadata about training epochs, accuracy, and contributor identity are attached (optionally pseudonymized) to model updates before federation.

[0065] Model rollbacks and rollback prevention are handled by:

Enclave-validated model hashes

Version stamps embedded in tokens

Distributed ledger-backed model history

Adaptability to Future Communication Technologies

[0066] The system is designed to be communication-protocol agnostic, with LoRa as a baseline and modular protocol stacks for:

6G compatibility

Edge-to-cloud handoff using Zero Trust architectures

Interoperability with AI accelerators over Wi-Fi 7, Bluetooth 5.3, and mesh networks

[0067] Such modularity allows the system to remain relevant as global network infrastructure evolves, particularly in regions prioritizing low-carbon, high-efficiency connectivity.

[0067A] Forward-Looking Technological Compatibility
The invention is intentionally designed with protocol abstraction layers, allowing adaptation to emerging technologies such as:

Quantum satellite LPWAN

Graph neural networks for distributed edge learning

AI hardware accelerators on-chip (e.g., RISC-V AI cores)

Zero-trust identity models over blockchain-integrated meshes

Quantum satellite LPWAN

Graph neural networks for distributed edge learning

AI hardware accelerators on-chip (e.g., RISC-V AI cores)

Zero-trust identity models over blockchain-integrated meshes

Modular Design and Upgradability

[0068] Each device is built using a modular firmware stack, divided into:

Secure Identity Layer

Contextual AI Engine

Communications Handler

User Interface Logic

[0069] Firmware modules can be over-the-air (OTA) updated via LoRa or BLE using encrypted payloads validated inside the secure enclave. This ensures field deployment upgradability even in isolated locations.

Integration with Third-Party Ecosystems

[0070] The system is designed to integrate with:

Electronic Health Records (EHRs)

Personal Health Dashboards

Enterprise Security Platforms

Digital Twins in Industrial IoT

[0071] An SDK enables third-party developers to create plugins that subscribe to context events (e.g., stress alert triggers, authentication events) while maintaining sandbox-level access without raw data exposure.

Regulatory and Data Protection Considerations

[0072] The invention is designed with compliance in mind for:

India's Personal Data Protection Act (PDPA)

General Data Protection Regulation (GDPR)

California Consumer Privacy Act (CCPA)

Health Insurance Portability and Accountability Act (HIPAA)

[0073] All biometric data is stored only within hardware enclaves and never transmitted. Federated learning exchanges are encrypted, anonymized, and pseudonymized to ensure differential privacy when needed.

Industrial Applicability

[0074] This invention is industrially applicable across:

Healthcare: Smart hospitals, rural clinics, telemedicine.

Manufacturing: Worker fatigue prevention, shift analysis.

Defense: Resilient biometric systems for forward bases.

Agriculture: Stress monitoring for laborers in field conditions.

Smart Cities: Infrastructure sensing, identity access management.

[0074A] Industrial and Regulatory Scope Across Jurisdictions
This invention is designed for universal applicability across regulated domains:

Medical devices (e.g., CE Mark, FDA 21 CFR Part 820)

Defense and aerospace systems (e.g., MIL-STD-810)

Industrial IoT and worker safety compliance (e.g., OSHA, ISO 45001)

GDPR-compliant consumer wearables

Technical Effects and Advantages

[0075]

Eliminates dependence on centralized cloud servers.

Ensures ultra-low-power operation suitable for month-long deployment.

Enables on-device AI, enhancing responsiveness and reducing data exposure.

Offers scalable multi-user, multi-device synchronization.

Maintains identity and wellness management in bandwidth-limited environments.

Enablement and Sufficiency of Disclosure

[0076] The invention is fully enabled through disclosure of:

Hardware components (biometric sensor types, LoRa modules)

Software architecture (ML inference pipeline, token encryption)

Communication protocols (LoRaWAN layers, fallback BLE stack)

Data flow from authentication to learning and back

[0076A] Furthermore, the invention discloses sufficient technical details including communication protocol configurations, token structures, machine learning models, encryption mechanisms, and biometric integration logic. These enable a person skilled in the art to realize and implement the invention.

[0076B] The system is implemented with secure enclaves, quantized AI inference engines, and modular network handlers, demonstrating full enablement and industrial applicability in compliance with Sections 10(4) and 2(1)(ac) of the Indian Patent Act, 1970.

[0077] Persons skilled in the art will be able to construct and operate the invention using publicly available components and software libraries (e.g., STM32 microcontrollers, TensorFlow Lite Micro, LoRaMAC-node)

Novelty and Inventive Step (Section 2(1)(j) & 2(1)(ja))

[0078] The combination of biometric authentication, LoRa communication, and federated learning is novel. Existing systems rely on cloud-based identity or high-bandwidth data aggregation and do not integrate decentralized learning or secure enclave-based identity propagation.

[0079] The inventive step lies in the coordinated use of:

On-device AI,

Enclave-derived tokens,

Federated learning over LPWAN,

Context-triggered profile switching,

Quantum-resilient cryptography in embedded wellness devices.

[0080] Together, these innovations form a robust, modular, and future-proof wellness ecosystem suitable for real-world deployment in energy-constrained, privacy-sensitive, and infrastructure-poor environments globally and in India.

Glossary of Key Terms:

Secure Enclave: A hardware-based execution zone with isolated memory, used for cryptographic and identity operations.

Federated Aggregator: A central or edge node responsible for merging encrypted model updates without accessing raw data.

Contextual Trigger: A sensed environmental or behavioral variable used to autonomously switch device operation modes.

Token Freshness: A mechanism (nonce, timestamp) ensuring a cryptographic identity token is valid for a limited use/time window only.

​​Legal and Technical Advantages of the Invention

In Compliance with the Indian Patent Act, 1970

The present invention offers a technical solution to a practical problem in identity management and wellness monitoring under challenging network and privacy conditions. The key legal and technical advantages include:

Section 3(k) Compliance:
The invention is implemented as a hardware-software co-designed system that achieves technical effects like secure biometric authentication, encrypted token exchange, and real-time AI-driven context inference, beyond a mere software algorithm.

Novelty and Inventive Step:
Combines LoRa LPWAN, secure enclaves, and federated learning in a manner not disclosed in prior art. Enables decentralized biometric systems with quantum-resilient cryptography and modular communication fallback.

Industrial Applicability:
Deployable across sectors such as healthcare, smart cities, industrial wellness, and emergency resilience. Compatible with constrained environments and upgradable in the field.

Privacy and Data Protection Compliance:
Ensures biometric data never leaves the hardware enclave. All inter-device communication is encrypted and pseudonymized, supporting GDPR, HIPAA, and India's PDP Act.

Modularity and Interoperability:
Designed to interoperate with third-party ecosystems (e.g., EHR, smart home hubs). OTA firmware updates and communication protocol stacks (BLE, satellite) enhance long-term relevance.
, Claims:We Claim

Claim 1 (Integrated System, Method, and Device Claim): A decentralized wellness monitoring system comprising a plurality of user devices, each device including: (i) at least one biometric sensor configured to acquire a physiological characteristic of a user; (ii) a secure enclave configured to perform local biometric authentication and generate a cryptographic identity token; (iii) an edge machine learning module configured to infer user context from sensor data and detect model drift; (iv) a LoRa transceiver and optionally a wake-up radio for encrypted LPWAN communication and low-power standby identity reception; wherein identity tokens are transmitted to trusted peer devices using a dynamically updated trust graph, validated with entropy-derived freshness, and each device trains and encrypts local models for federated learning with secure aggregation and blockchain-based model hash anchoring.

Claim 2: The system of claim 1, wherein biometric sensors include one or more of: fingerprint scanner, PPG sensor, ECG sensor, iris scanner, accelerometer, gyroscope, ambient light sensor, temperature sensor, or skin conductance sensor.

Claim 3: The system of claim 1, wherein the secure enclave comprises a hardware-based Trusted Execution Environment (TEE) selected from ARM TrustZone, Intel SGX, or a secure element.

Claim 4: The system of claim 1, wherein the identity token includes contextual metadata such as time, location, or environmental data, and features anti-replay protection through nonces or entropy-derived freshness.

Claim 5: The system of claim 1, wherein each device supports multiple user profiles and performs automatic switching based on biometric identity or contextual triggers including geofencing, ambient conditions, or time-of-day.

Claim 6: The system of claim 1, wherein fallback communication occurs via one or more of: Bluetooth Low Energy (BLE), Zigbee, Wi-Fi Direct, NB-IoT, LTE-M, or satellite LPWAN protocols.

Claim 7: The system of claim 1, wherein the federated learning module encrypts model updates using post-quantum cryptography selected from CRYSTALS-Kyber or Dilithium and includes one or more of: gradient deltas, full weights, or pruned models.

Claim 8: The system of claim 1, wherein firmware updates are authenticated using digital signatures stored in the secure enclave and are delivered via multicast over LoRa or BLE mesh networks.

Claim 9: The system of claim 1, wherein federated aggregation is performed using techniques selected from federated averaging, differential privacy, or homomorphic encryption, and model version hashes are anchored on a blockchain ledger for auditability.

Claim 10: The system of claim 1, wherein device-to-device communication is governed by a dynamic trust graph based on prior token exchanges, biometric integrity scores, enclave attestation, and token freshness metrics.