Description:Title of the Invention

Smart Autonomous Tracking System with Context-Aware AI, Secure Peer-to-Peer Mesh Communication, Stealth Operation, and Adaptive Geofencing

1. Field of the Invention

[0001]
The present invention relates to the field of intelligent wireless tracking systems and context-aware communication networks. Specifically, the invention pertains to a smart, portable, low-power asset tracking system that utilizes machine learning algorithms, long-range wireless communication (such as LoRa or equivalent LPWAN technologies), short-range radios (such as Bluetooth Low Energy and Wi-Fi), secure peer-to-peer mesh routing, tamper detection, and dynamically evolving geofencing capabilities. The invention is implemented via an embedded system that performs autonomous decision-making on-device, making it particularly applicable in Internet of Things (IoT), logistics, defense, healthcare, industrial automation, consumer electronics, and emerging smart city infrastructure.

[0002]
This invention involves both hardware and software components functioning integrally to achieve a technical effect, thus satisfying the requirements under Section 3(k) of the Indian Patents Act, 1970. The claimed system is not merely a software algorithm, business method, or abstract idea but rather a technical apparatus and method solving a concrete technological problem.

[0003]
Globally, this invention is suitable for jurisdictions under the Patent Cooperation Treaty (PCT) and aligns with key filing standards of the United States Patent and Trademark Office (USPTO), European Patent Office (EPO), and China National Intellectual Property Administration (CNIPA).

2. Background of the Invention

2.1 Current State of the Art

[0004]
Conventional asset tracking systems use fixed beaconing schedules to report asset status or location. These systems rely on periodic wireless transmissions, which not only consume power continuously but also risk revealing the tracker's presence to unauthorized observers, reducing both battery life and operational security.

[0005]
While LPWAN technologies like LoRa, NB-IoT, and Sigfox are used for long-range communication, many of these systems follow star-topology architectures that depend on fixed gateways or cloud servers. Moreover, most traditional solutions lack local decision-making and depend entirely on server-side intelligence. This creates latency, restricts scalability, and fails in environments with intermittent connectivity.

[0006]
Attempts to use context-aware or sensor-based suppression methods remain primitive. For example, some luggage trackers deactivate transmissions when stationary or when airborne. However, such logic is typically hard-coded, rule-based, and not informed by adaptive learning or real-time environmental classification.

[0007]
Security is another major concern. Typical devices may encrypt payloads, but fail to authenticate tamper events or verify the chain of custody for alerts. In environments such as defense, healthcare, or secure logistics, such capabilities are inadequate.

2.2 Deficiencies in Prior Art

[0008]
Relevant prior art includes:

US 10,969,499 (Naim) – discloses a sensor-based activation of wireless signals, but lacks contextual ML adaptation, mesh networking, or cryptographic tamper proofing.

Cisco’s LoRa/BLE hybrid mesh system – switches transmission bands based on proximity signals, but lacks on-device context reasoning or stealth suppression logic.

ZaiNar’s LoRa-based multilateration systems – provide location estimation but do not employ ML-based adaptive routing or tamper-verifiable trust chains.

Smart geofencing patents (e.g., Ford, Discovery Ltd.) – describe event-triggered or vehicle-context geofencing, but not schedule-learning autonomous geofence evolution.

2.3 Technical Problems Addressed

[0009]
The invention solves the following technical problems:

Battery drain due to unnecessary transmissions

Lack of real-time, on-device intelligence

Inflexible or static mesh routing protocols

Lack of secure, verifiable alerting mechanisms for tamper detection

Static, non-adaptive geofence boundaries that ignore behavioral patterns

Inability to function in infrastructure-less environments or adversarial networks[0010]

[0009A]
The following table summarizes key limitations in existing solutions and how the present invention overcomes them:

2.4 Indian Market Relevance

[0010]
India’s logistics, agriculture, defense, and smart infrastructure sectors demand resilient, cost-effective, power-efficient tracking systems. Existing solutions are often dependent on 4G/5G cellular infrastructure or gateway placement, which is not feasible in many rural or conflict-prone regions. This invention fills that critical gap by:

Operating in low-connectivity and infrastructure-less zones

Supporting mesh-based peer communication

Functioning entirely on-device without real-time internet

Providing tamper-verified alerts for mission-critical deployment

3. Summary of the Invention

[0011]
The invention discloses a smart autonomous tracking device and system, comprising:

A long-range wireless transceiver (e.g., LoRa)

A short-range radio interface (BLE and/or Wi-Fi)

Environmental sensors, including accelerometers and tamper switches

An embedded microcontroller with flash memory

A secure cryptographic element for digital signatures

An on-device machine learning module that classifies context based on sensor data

[0012]
The device operates by default in a low-power stealth mode, wherein no transmissions occur unless a qualified trigger is detected (motion, tamper, or authenticated ping). When activated, the embedded AI classifier determines the operational context and dynamically adjusts parameters such as:

Transmission frequency

Output power

Routing path

Encryption level

[0013]
Devices form a decentralized mesh network, learning performance metrics of peer nodes and making routing decisions based on real-time or historical feedback. The invention includes a hybrid radio protocol wherein BLE/Wi-Fi scans are used to modulate LoRa behavior. Proximity to known devices can suppress or activate transmissions, further conserving power or improving privacy.

[0014]
A tamper alert protocol is implemented wherein a digitally signed alert is relayed through multiple peers, each appending their signature and timestamp to form a cryptographically verifiable trust chain. This creates a forensically valid record of device interaction.

[0015]
Geofencing is handled via dynamic, behavior-informed boundaries. The system learns user routines and tightens or relaxes boundaries based on contextual understanding.

[0016]
The invention is hardware-anchored, exhibits clear technical effects, and solves long-standing issues in wireless tracking and LPWAN mesh reliability. It is suitable for patent protection under Indian law (compliant with Sections 2(1)(j), 2(1)(ja), and avoids 3(k), 3(d), and 3(n)), and is globally adaptable.

4. Brief Description of the Drawings

[0017]
FIG. 1 – Hardware architecture block diagram of the smart tracking device, showing integration of LoRa module, BLE/Wi-Fi chip, sensor array, secure element, MCU, and power subsystem.

[0018]
FIG. 2 – Operational state machine illustrating transitions between stealth, active, and tamper modes.

[0019]
FIG. 3 – AI-driven context classification model block, showing sensor inputs, radio scan data, and output control variables.

[0020]
FIG. 4 – Peer-to-peer mesh routing model showing metric-based hop selection and path learning.

[0021]
FIG. 5 – Cryptographic tamper alert sequence with signature chaining across multiple mesh nodes.

[0022]
FIG. 6 – Adaptive geofencing logic tree derived from behavior history and dynamic thresholds.

[0023]
FIG. 7) – Optional BLE-only fallback mesh mode for bandwidth-constrained deployments.

[0024]
FIG. 8 – Cloud interface for receiving and verifying aggregated tamper alert logs.

[0024 A]

FIG. 9 – Flowchart representing trigger matrix logic and dynamic radio interface selection.

5. Detailed Description of the Invention

5.1 Device Architecture and Integration

[0025]
The smart tracking device comprises the following integrated hardware components (as shown in FIG. 1):

[T1] Long-Range Transceiver Module (LoRa/LPWAN): Operates in ISM bands (e.g., 868 MHz or 915 MHz), configured for ultra-low power with configurable spreading factor and duty cycle.

[T2] Short-Range Radio Module (BLE/Wi-Fi): Enables contextual awareness and peer discovery. BLE 5.0 preferred for low-energy broadcast and connectionless data exchange.

[T3] Sensor Suite: Includes 3-axis accelerometer, gyroscope, barometer, and magnetic reed tamper switch. Additional optional sensors may include temperature, humidity, or magnetic proximity.

[T4] Microcontroller (MCU): Low-power processor (e.g., ARM Cortex-M4 or M33) with onboard RAM and flash. Example MCU: ARM Cortex-M33 (e.g., STM32L562) or Nordic nRF52840 with BLE stack and flash >= 512KB, RAM >= 64KB. Device selection is governed by TinyML inference compatibility and OTA support.

[T5] Secure Element/TPM: Cryptographic processor (e.g., ATECC608A) stores private keys and performs ECC/DSS signing for tamper alerts.

[T6] GNSS Module (Optional): Supports high-accuracy geolocation for geofencing and asset telemetry.

[T7] Power Subsystem: Battery (e.g., coin cell or Li-ion), voltage regulator, and energy-efficient wake timer. In one implementation, the system may be powered by a 3V CR2477 coin cell rated at 1000 mAh, offering multi-year life due to deep sleep modes and sub-5 µA quiescent current.

In a further embodiment, the power subsystem includes a programmable wake timer and deep sleep controller drawing less than 5 µA. This allows multi-year operation on coin cell batteries, making the system viable for ultra-low-power field deployments.

[0025A]
The tracking unit is enclosed within a rugged, magnetically shielded housing constructed from tamper-evident polymer. The casing features a snap-fit design for tool-free assembly, suited for field deployment, wearable form factors, or embedded installation.

5.2 Stealth Mode Operation

[0026]
The default operating state is a stealth mode defined as a non-transmitting, sub-5µA power consumption state wherein all wireless radios are disabled, and the MCU periodically polls for trigger events via sensors or cryptographically authenticated wake commands.

Poll sensors for motion or tamper

Check for authenticated encrypted pings from authorized controllers

Stealth mode” → “stealth mode (defined as a non-transmitting low-power state with radio modules disabled and periodic sensor polling)

[0027]
Upon any qualified trigger, the device transitions to active mode and executes behavior based on context classification. Power consumption in stealth mode is <5 µA, enabling multi-year operation on a coin cell.

[0028]
FIG. 2 illustrates the state transitions:

Stealth → Triggered → Context-Classified Active

Stealth ← Inactivity Timer ← Active

[0028A]
To avoid false positives in high-vibration environments (e.g., air cargo, construction sites), the tamper detection circuit is augmented by an adaptive noise filter which correlates accelerometer data with historical vibration profiles. Tamper alerts are only triggered when anomalies exceed dynamically tuned thresholds over a specified window.

[0028B]

In one embodiment, the device behavior is governed by a trigger matrix that assigns weights to events like tamper detection, motion profile deviation, and loss of trusted proximity. This logic determines whether to escalate alerts, increase transmission frequency, or delay relay based on historical behavior data.

The term “trigger matrix” refers to a configurable logic evaluation table that assigns weighted priorities to inputs such as tamper detection, motion profile deviation, BLE proximity loss, and battery level. The output of the matrix determines whether to transmit, delay, suppress, or escalate communication activity.

5.3 On-Device Context Classification

[0029]
As shown in FIG. 3, the embedded AI model uses inputs from:

Accelerometer (e.g., standard deviation, FFT)

BLE/Wi-Fi scan results (MAC IDs, RSSI)

GNSS-derived movement vectors (if enabled)

Time-of-day, schedule history

[0030]
Context classes include:

C1: Stationary – Indoor

C2: In Transit – Human/Vehicle

C3: In Known Proximity (e.g., smartphone nearby)

C4: Unknown Motion/Unattended

[0031]
Model is trained offline on sample data and deployed in embedded inference mode. In one embodiment, decision trees or TinyML-compatible neural nets (e.g., TensorFlow Lite Micro) are used. OTA updates of models may be delivered using BLE or LoRa multicast.

In alternate low-power configurations, the classification logic may be implemented via a rules engine consisting of Boolean conditions mapped to pre-programmed sensor thresholds. This mode eliminates the need for AI inference where memory or CPU budgets are constrained.

C3: Known Proximity” is defined by BLE MAC address matching and signal strength thresholds (e.g., RSSI > –65 dBm from a trusted device), indicating that the asset is likely attended and safe, and suppresses alerts and transmissions accordingly.

[0032]
Output of classification governs:

Whether to transmit

Transmit interval

Power level

Encryption level

Choice of routing path

For instance, a TinyML neural model (e.g., 1 hidden layer with 32 neurons) compiled with TensorFlow Lite Micro may occupy less than 30KB of flash and operate within a 10KB RAM budget. This ensures feasibility on ultra-low-power MCUs.

​​[0032A] In certain embodiments, devices locally train simplified models and periodically transmit encrypted parameter updates to peer nodes. These are aggregated to improve the shared context classification model without exposing raw data, thereby enabling privacy-preserving federated learning.

[0033]
In an extended embodiment, each context classification result triggers a pre-defined event-response rule set, which may be stored in a policy table on-device. For example, context "C3: In Known Proximity" may suppress alert generation; while "C4: Unknown Motion" may trigger immediate alert escalation and mesh relay. These rules are configurable via OTA command or via precompiled firmware profiles.

5.4 Peer Learning Mesh Routing Protocol

[0034]
Each device maintains a dynamic peer table indexed by:

Peer node ID

Historical latency

RSSI/SNR

Packet drop rates

Energy consumed per successful relay

[0035]
As illustrated in FIG. 4, when a message is to be sent, a heuristic or ML-based model selects the optimal next-hop peer. Over time, this leads to convergence on efficient routing paths without centralized control.

[0036]
Routing protocol supports:

Multi-hop broadcast for alerts

Unicast for periodic telemetry

Constrained TTL to avoid network flooding

[0036A] In hybrid environments, the system supports dual-path radio selection, where BLE/Wi-Fi is used for local communication and LoRa/NB-IoT for long-range relay. The routing logic dynamically selects the appropriate interface based on signal quality, peer density, and energy constraints.

[0037]
In a further embodiment, performance metrics from multiple devices in the mesh network are aggregated and used to iteratively improve routing efficiency via a federated learning mechanism. This enables nodes to locally retrain simplified routing models without sharing raw data, enhancing privacy and network intelligence.

5.5 Tamper Alert Trust Chain

[0038]
Upon detecting a tamper event, the device:

Forms a Tamper Alert Packet (TAP) including device ID, timestamp, sensor reading, and tamper flag.

Signs the TAP with its private ECC key.

Broadcasts to nearby mesh peers.

[0039]
Each peer that relays the alert:

Verifies signature

Appends its own timestamp and signature

Forwards to next hop or base station/cloud node

[0040]
As shown in FIG. 5, the result is a cryptographically verifiable audit trail, ensuring non-repudiation, authenticity, and route transparency.

[0041]
This model supports:

Offline verification

Chain-of-custody enforcement

Forensics in high-security deployments

[0042]

In one embodiment, the cryptographic module is configured to support post-quantum cryptography algorithms including but not limited to lattice-based schemes such as CRYSTALS-Kyber or Dilithium. This enables forward security against emerging quantum threats and positions the system for future cryptographic standardization.

5.6 Hybrid Sensor-Radio Fusion Logic

[0043]
BLE/Wi-Fi scans detect:

Trusted smartphones/devices (pre-registered MAC)

Environmental SSIDs or peer beacons

[0044]
This proximity data is used to:

Suppress transmission if in known proximity

Increase beacon rate if isolation is detected

Trigger alerts if trusted presence is lost

[0045]
As shown in FIG. 6, context-driven radio fusion enhances stealth and efficiency, while enabling behavioral logic.

5.7 Adaptive Geofencing

[0046]
Geofencing engine uses:

GNSS path data

Context history

Peer location triangulation (if GNSS disabled)

[0047]
Geofences are:

Time-windowed (e.g., tighter during business hours)

Dynamic (e.g., expanded during learned idle times)

Peer-informed (e.g., coordination among multiple assets)

[0048]
FIG. 7 models evolving geofence boundaries as a function of learned routine + external inputs.

[0049]

In a collaborative embodiment, peer devices within a common mesh segment may share geofencing evolution data (e.g., learned boundaries, recent deviation triggers) to improve boundary prediction and anomaly detection. This enables localized consensus-building in the absence of cloud infrastructure.

5.8 Alternate Embodiments

[0050]
The invention may be implemented in various form factors:

Wearables (e.g., for child or elderly tracking)

Vehicle-mounted

Container-embedded

Animal collar with solar charging

RFID-integrated for supply chain

In further embodiments, future communication stacks may include satellite-based LPWAN (e.g., Swarm or Lacuna protocols), 6G cellular backhaul for mobile deployments, and hybrid quantum-sensing transceivers for tamper localization or atmospheric signal shielding.

In another embodiment, the system may operate without GNSS by relying exclusively on peer-to-peer triangulation among nearby mesh nodes using RSSI, time-of-flight, or UWB signaling. This mode provides fallback geolocation capabilities in GPS-denied zones such as underground infrastructure or shielded environments.

The embedded AI classifier may optionally be replaced with a policy-driven rules engine for ultra-constrained devices, or upgraded to support federated learning-based models on more capable processors.

5.9 Cloud Dashboard Interface

[0050A]
As illustrated in FIG. 8, a cloud-based dashboard interface receives tamper alerts from mesh devices and verifies their integrity through chained cryptographic signatures. This dashboard presents:

Tamper Alert Logs with device IDs, timestamps, hop paths, and digital signature chain visualization.

Geospatial Map View showing the alert origin and current asset location.

Context History View displaying recent transitions (stationary → motion, etc.).

Administrative Panel for secure Over-the-Air (OTA) configuration including:

Geofence update pushes

AI model revision deployment

Device policy adjustments

This interface supports forensic investigation, regulatory compliance, and remote diagnostics.

[0050B]

In a livestock management scenario, the tracking unit is embedded in a collar fitted with a solar charging panel. The device forms a BLE mesh with nearby animals and escalates alerts via LoRa when breach events or anomalies are detected.

[0051]
Possible communication stack variations:

Sub-GHz LoRa

NB-IoT (fallback)

UWB peer positioning (optional)

Zigbee, Thread, or 802.15.4 mesh extensions

[0052]
Processing can range from ultra-lightweight MCU-only (TinyML), to embedded Linux for higher AI complexity.

In modular versions, the device may include swappable radio/sensor boards within IP-rated enclosures, supporting industrial, vehicular, or wearable mounting configurations. Magnetic, screw-lock, or slide-in connectors may be used for component interchangeability without soldering.

6. Use Case Examples (India + Global)

[0053]
Example 1 (Defense): Tracker embedded in soldier equipment remains silent until separation from group triggers mesh broadcast of distress signal with full trust chain.

[0054]
Example 2 (Cold Chain Logistics): Container emits data only on route deviation or if tampering occurs. Alerts signed and timestamped for compliance verification.

[0055]
Example 3 (Smart Cities - India): Devices installed on mobile public assets (e.g., electric scooters) adapt geofences around dynamic charging hubs.

[0056]
Example 4 (Elderly Care): Wearable device identifies whether person is active, idle, or wandering. Geofence and context determine alert thresholds.

[0057]
Example 5 (Agricultural Monitoring): Devices deployed on livestock in rural India coordinate via BLE mesh and LoRa relays to report location deviations.

[0058]
Example 6 (Border Patrol): Trackers enter stealth mode until boundary breach detected, then log relay path and emit tamper alert to encrypted command node.

[0059]
Example 7 (Industrial Safety): Equipment tagging in underground mines where GPS fails. Mesh tracking with proximity beacons and peer-learning adjusts signal plan dynamically.

7. Forward-Looking Adaptability

Strong future-proofing (TinyML, Post-Quantum Crypto, OTA AI).
Call out 6G, satellite LPWAN, and quantum-sensing extensions as optional roadmap.

8. Legal and Technical Advantages of the Invention

Compliance with Indian Patent Law (Patents Act, 1970 & Rules, 2003)

Novelty [Sec 2(1)(j)]: No known system integrates stealth-mode LPWAN communication, on-device AI, tamper trust chains, and adaptive geofencing into a single embedded tracking device.

Inventive Step [Sec 2(1)(ja)]: Overcomes limitations of static transmission and cloud-reliant AI through fully autonomous, mesh-based, verifiable, on-device intelligence.

Industrial Applicability [Sec 2(1)(ac)]: Applicable in logistics, defense, consumer electronics, and smart cities in both developed and developing nations.

Section 3(k) Compliance: Not a computer program per se; the invention is embedded in and inseparable from physical hardware, producing a technical effect.

Section 3(n) Compliance: No reliance on traditional knowledge, folklore, or naturally occurring substances.

, Claims:Claim 1 (Device – Comprehensive)

A smart autonomous tracking device comprising, but not limited to:

a long-range wireless transceiver configured for low-power communication;

a short-range radio module configured for Bluetooth Low Energy (BLE) and/or Wi-Fi scanning;

one or more sensors including a motion sensor and a tamper detection switch;

a microcontroller with memory, executing a context classification engine comprising a machine-learning model;

a cryptographic module configured to digitally sign and verify messages; and

a power management subsystem including a deep sleep mode and a programmable wake timer;

wherein the device:

(a) operates by default in a non-transmitting stealth mode;

(b) transitions to an active state upon detecting a trigger condition including motion, tampering, or an authenticated command;

(c) classifies operational context locally using the context classification engine;

(d) dynamically adjusts communication parameters including frequency, transmission path, power level, and encryption method; and

(e) relays digitally signed tamper alerts across a decentralized peer-to-peer mesh network, with cryptographic signature chaining for auditability.

Claim 2 (System + Cloud Integration)

The system comprising the device of claim 1, and a cloud-based or edge-based aggregator configured to:

(a) receive relayed tamper alerts from one or more devices;

(b) verify cryptographic signature chains;

(c) reconstruct the transmission path and event timestamps; and

(d) provide a dashboard interface for alert visualization, geofence updates, policy deployment, and forensic traceability.

Claim 3 (Method)

A method for secure and context-aware tracking, comprising:

(a) maintaining the tracking device in a default stealth mode;

(b) detecting trigger events using onboard sensors or receiving authenticated external commands;

(c) classifying the device's operational context using an on-device machine-learning model;

(d) adapting communication behavior, including selecting radio interfaces and adjusting transmission parameters;

(e) relaying digitally signed tamper alerts through a peer-to-peer mesh network; and

(f) logging classified context transitions and alert events for secure upload upon network availability.

Claim 4 (Software Medium)

A non-transitory computer-readable medium containing instructions that, when executed by the device of claim 1, cause it to:

(a) remain in stealth mode until triggered;

(b) classify context using sensor and signal data;

(c) adapt radio interface and routing behavior based on classification;

(d) digitally sign tamper alerts and relay them through a cryptographically verifiable mesh path; and

(e) update geofence parameters based on learned environmental and behavioral patterns.

Claim 5

The device of claim 1, wherein:

(a) operational behavior is determined by a trigger matrix that evaluates motion, tamper events, proximity loss, and battery level;

(b) context classification is performed using either a machine-learning model deployed on a TinyML-compatible architecture trained on field-collected data or a rule-based engine in constrained deployments; and

(c) a context class “C3: Known Proximity” is defined by detection of pre-registered BLE MAC addresses within a threshold RSSI value, causing suppression of transmission activity.

Claim 6

The device of claim 1, wherein tamper alerts are digitally signed using post-quantum cryptographic algorithms including CRYSTALS-Kyber or CRYSTALS-Dilithium.

Claim 7

The device of claim 1, wherein geofences are dynamically generated using time-of-day profiles, historical movement routes, and proximity triangulation in environments where GNSS signals are unavailable or degraded.

Claim 8

The system of claim 2, wherein peer mesh nodes dynamically assume one of three roles—relay node, observer node, or aggregator node—based on battery level and link quality.

Claim 9

The device of claim 1 or the system of claim 2, wherein:

(a) over-the-air (OTA) firmware or model updates are accepted only upon verifying a digital signature issued by a trusted certificate authority; and

(b) peer devices collaboratively update context classifiers using encrypted parameter sharing in a federated learning configuration, without transmitting raw data.

Claim 10

The device of claim 1, wherein the enclosure is tamper-evident, magnetically shielded, and modularly designed to support wearable, industrial, and vehicular deployments.

Claim 11

The system of claim 2, wherein the smart device is embedded in a solar-powered livestock collar and configured to detect anomalous movement and trigger alert relays using BLE and LoRa mesh communication.