1. A priority-aware distributed mobile data synchronization system comprising: • an edge data generation layer configured to generate heterogeneous data streams from mobile devices and IoT sensors; • a data processing layer configured to preprocess incoming data and extract relevant features; • a priority intelligence module incorporating a hybrid LSTM–Transformer model configured to classify data based on urgency and contextual relevance; • an adaptive synchronization engine utilizing reinforcement learning to determine optimal data synchronization strategies; and • a distributed storage layer configured to replicate synchronized data across edge and cloud servers for scalable storage.

2. The system of claim 1, wherein the data processing layer performs data filtering, normalization, and feature extraction to prepare data for priority classification.

3. The system of claim 1, wherein the hybrid LSTM–Transformer model combines sequential temporal learning and contextual attention mechanisms to dynamically assign priority levels.

4. The system of claim 1, wherein the priority intelligence module categorizes data into high priority, medium-priority, and low-priority classes based on urgency and contextual importance.

5. The system of claim 1, wherein the reinforcement learning engine selects synchronization actions including immediate synchronization, delayed synchronization, or packet ordering.

6. The system of claim 1, further comprising a network monitoring module configured to evaluate real-time communication parameters including bandwidth availability, latency, packet loss, and congestion levels.

7. The system of claim 6, wherein synchronization decisions are dynamically optimized based on the monitored network conditions.

8. The system of claim 1, wherein high-priority data packets are synchronized with reduced latency while lower priority data is scheduled during periods of lower network congestion.

9. A method for priority-aware mobile data synchronization, comprising: • collecting data streams from mobile devices and IoT sensors; • preprocessing and extracting features from the collected data; • classifying data priority using a hybrid LSTM–Transformer model; • evaluating real-time network conditions; • selecting synchronization actions using reinforcement learning; and • storing synchronized data across distributed edge and cloud servers.

10. The method of claim 9, wherein synchronization policies are continuously refined through feedback-driven reinforcement learning to improve latency reduction, bandwidth utilization, and system reliability.

Description:Abstract
The rapid growth of edge computing, Internet of Things (IoT) devices, and mobile applications has
increased the demand for efficient data synchronization across distributed systems. Traditional
synchronization techniques often rely on static scheduling policies that fail to adapt to dynamic network
conditions and varying data priorities, leading to increased latency and inefficient bandwidth usage.
This paper presents SyncMind, an adaptive edge data synchronization framework that integrates a
Dual-Stream Attentional Hybrid Network with Deep Reinforcement Learning. The hybrid model
analyzes temporal patterns and contextual relationships within data streams to assign dynamic priority
levels. A reinforcement learning agent then determines optimal synchronization strategies by observing
real-time network parameters such as bandwidth, latency, and congestion. The proposed approach
prioritizes time-sensitive data while improving network resource utilization. SyncMind provides a
scalable and intelligent solution for distributed edge environments including IoT networks, smart
healthcare systems, and real-time edge computing platforms.
IP202631020432 - We present a monolithic, radiation-hardened geostationary satellite system-on-chip
(SoC) featuring adaptive PV power management, high-resolution telemetry aggregation, and artificial
intelligence inference. The SoC features a bank of digitally controlled MPPT regulators with high
efficiency GaN switches, a quad-core RISC-V control cluster, and convolutional and transformer
accelerators that produce 512 GFLOPS under 5 W. A multi-channel telemetry fabric collects data from
temperature, radiation, voltage, and current sensors at 50 kSps. The data is then sent to AI accelerators,
which employ pre-trained neural networks to classify anomalies, predict solar-array deterioration, and
predict eclipses. Over the course of a 15-year mission, inference findings are used to adjust PV string
operating points in real-time, guaranteeing > 98% power-tracking efficiency. By removing ground
station analytics latency, housekeeping down-link bandwidth is reduced by more than 90%. Radiation
robustness is provided by FD-SOI body-bias techniques rated to 125 krad (Si) total-ionizing dose, triple
modular-redundant logic, and periodic configuration-memory cleaning. A hardware root-of-trust is
used to authenticate over-the-air firmware and model upgrades, ensuring safe mission profile and solar
cycle adaptation. The concept is a major enabler for next-generation high-throughput GEO satellites
since it integrates edge AI and adaptive power regulation in a single die, lowering the number of printed
circuit boards, the subsystem bulk by 1.8 kg, the launch cost, and the operating costs.
IP202641019111 - The suggested framework is implemented by following a number of steps once the
dataset is preprocessed to manage data quality anomalies. The first step is to identify the quality
anomaly that needs fixing and then pick out the relevant attributes that are associated with it. Then, we
choose the rows where the specified feature does not show any quality anomalies. After that, we use a
filtering method to keep just the records that are most closely related to the quality dimension that we
are addressing. After that, the XGBoost model is trained using the chosen rows. At startup, the XGBoost
model is configured with the aforementioned parameters, including the normalization method and
booster type. Training the model entails fine-tuning its parameters while making use of XGBoost's
robust prediction capabilities to ascertain dataset correlations and trends. The whole dataset, comprising
the rows containing quality anomalies, is used to train the model when the training phase is over. The
XGBoost model uses the imputed information and attributes to do prediction-based quality anomaly
correction or approximation, whichever is appropriate for the given quality anomaly. This method
improves the dataset's correctness and dependability by fixing or compensating for data quality
shortcomings.
IP202641018084 - An autonomous artificial intelligence–driven monitoring and display unit is
disclosed that enables real-time, low-latency monitoring and intelligent decision support at the network
edge. The unit comprises a processing controller executing an artificial intelligence inference engine
for signal conditioning, feature extraction, pattern recognition, and anomaly detection, a sensor interface
module for acquiring multi-source data from external sensors or networked equipment, a display
module for rendering real-time status indicators, alerts, and historical trends, a memory module for
storing device profiles, adaptive threshold rules, and event logs, and a communication module providing
wired and/or wireless connectivity with optional cloud synchronization. The system autonomously
adapts monitoring parameters based on learned operating patterns and provides prioritized alerts and
recommended actions, making it suitable for industrial, medical, and enterprise monitoring
environments. Accompanied Drawing [FIGS. 1-2]
IP202641018306 - The present disclosure relates to a system (102) and a method (200) for securing
data communication in an access network (126). The system (102) includes a client identity module
(114) to generate identity information linked to user devices (128-1 to 128-N), an authentication server
(122) to manage authentication of the user devices (128-1 to 128-N), a first database node (132-1) and
a second database node (132-2) to store the authentication records, and an arbitration module (118) to
compare the stored authentication records. The system (102) receives the identity information from the
client identity module (114), stores the identity information in a storage module (116) by transmitting
the authentication records to the first database node (132-1) and the second database node (132-2),
compares the authentication records and establishes, by a secure access module (120), a protected data
communication session for the user devices (128-1 to 128-N).
IP202641018566 - The present invention discloses an artificial intelligence–enabled edge Internet of
Things system for low-latency intelligent processing in distributed environments. Distributed IoT
sensor nodes collect real-time data and transmit data to edge computing nodes for local artificial
intelligence inference and analytics. A centralized orchestration platform manages machine learning
models and coordinates distributed intelligence across edge and cloud environments. A decision
orchestration engine converts AI-driven insights into automated control actions and system adaptations.
The system supports continuous learning, security, privacy, and fault tolerance, enabling real-time
intelligent processing with minimal latency and enhanced reliability.
IP202641018725 - ABSTRACT: Title: A Smart Adaptive System for Secure Data Processing and Real
Time Decision Support Using Distributed Computing The present invention discloses a smart adaptive
system for secure data processing and real-time decision support in distributed computing
environments. The system comprises interconnected computing nodes, a secure data intake module, a
workload analysis engine, an adaptive resource allocation module, and a decision support engine.
Incoming data is authenticated, encrypted, and classified based on priority and security constraints
before being distributed for processing. The system dynamically allocates computational resources
based on real-time workload intensity, node performance metrics, and predefined security policies to
optimize efficiency and reduce latency. Continuous monitoring enables automatic redistribution of tasks
in response to overload, failure, or anomalous conditions, thereby ensuring reliability and fault
tolerance. Embedded security mechanisms including encryption, access control, and anomaly detection
maintain data confidentiality and integrity throughout processing. The invention is applicable to cloud
computing platforms, enterprise systems, edge environments, and large-scale data-driven applications
requiring secure, scalable, and responsive decision-making capabilities.
IP202641018874 - Machine Learning–Driven Energy Complexity Framework for Predicting Optimal
Data Structures in Workload-Specific Execution. The invention relates to a machine-learning-driven
analytical framework for evaluating and predicting the energy complexity of data structure operations
under workload-specific execution conditions. The framework integrates an analytical module that
models operation-level characteristics with a machine-learning engine trained on labelled energy data
to generate accurate energy predictions. By analysing workload parameters such as operation
frequencies, access patterns, and dataset properties, the system predicts energy consumption using
classification and regression models. Feature-importance analysis identifies the most influential
workload attributes, while energy-per-operation scaling curves support prediction stability across
varying dataset sizes. The decision component combines analytical descriptors and machine-learning
outputs to recommend the data structure expected to yield the lowest energy consumption for the given
workload. The framework continuously improves predictive accuracy by incorporating historical
execution behaviour, enabling adaptive and energy-efficient data structure selection. [FIG. 1]
IP202641019040 - AN ADAPTIVE IOT FRAMEWORK FOR SMART DECESION MAKING
USING MACHINE LEARNING ABSTRACT: The Internet of Things (IoT) is proliferating swiftly,
with billions of interconnected devices globally, requiring comprehensive security measures to
safeguard these systems. The Internet of Things (IoT) is rapidly increasing, necessitating secure
networks to counter diverse cyber threats. The swift expansion of the Internet of Things (IoT) has
resulted in its extensive use across diverse sectors, facilitating improved efficiency and effective
services. The integration of IoT technologies with current enterprise application platforms has grown
prevalent. This integration requires the reassessment and modification of existing Enterprise
Architecture (EA) models and Expert Systems (ES) to include IoT and cloud technologies.
Organizations must embrace a comprehensive perspective and automate multiple facets, encompassing
operations, data management, and technological infrastructure. Machine Learning (ML) is a potent tool
for IoT and intelligent automation inside Enterprise Architecture (EA). This paper tackles the
constraints of multi-class attack detection in IoT devices and introduces novel lightweight ensemble
approaches based on machine learning that leverage its robust architecture.
IP202631018973 - ABSTRACT Real-Time Multimodal Exhaustion Monitoring System Using
Embedded Wearable Biosensors and Deep Learning Techniques. Methods The present invention
discloses an intelligent wearable Exhaustion detection and classification system particularly suited for
industrial workers engaged in physically strenuous and cognitively demanding occupational tasks. The
system comprises a custom-designed printed circuit board (PCB) integrated with a microcontroller unit
based on the Arduino Nano RP2040 Connect platform, wherein the PCB is operatively interfaced with
a plurality of physiological, cognitive, and environmental sensing modalities. In one embodiment, the
sensing modalities include a helmet-integrated dual-channel electroencephalogram (EEG) sensor
configured for acquisition of prefrontal neural activity at Fp1 and Fp2 locations, a shirt-integrated three
lead electrocardiogram (ECG) sensor configured in a standard Einthoven triangle arrangement (RA,
LA, LL), a galvanic skin response (GSR) sensor, a body temperature probe, a sound sensor for ambient
noise monitoring, a dust sensor for particulate exposure measurement, a photoplethysmography (PPG)
sensor for estimation of blood oxygen saturation (SpO₂) and pulse characteristics. The system is
configured to acquire synchronized multimodal data in real time, perform preliminary signal
conditioning, store timestamped sensor readings through an onboard SD card module, and transmit the
acquired parameters wirelessly via Bluetooth Low Energy and/or Wi-Fi communication to a companion
Android-based dashboard application. The dashboard interface provides continuous visualization of
each sensor output through dedicated graphical waveform and trend plotsGround truth Exhaustion
labeling is established using a validated psychometric assessment tool comprising the Multidimensional
Exhaustion Inventory (MFI-20), and the acquired multimodal dataset is utilized for training and
deployment of supervised machine learning algorithms including Support Vector Machines, Random
Forest, Gradient Boosting, and XGBoost classifiers, along with an LSTM-based deep learning network
specifically optimized for temporal physiological sequence modeling and embedded directly on the
Arduino Nano RP2040 processor for real-time Exhaustion inference. The system further generates alert
notifications when predicted Exhaustion thresholds exceed predefined occupational safety limits. The
present invention thereby provides a low-cost, scalable, portable, and field-deployable IoMT-based
solution for continuous Exhaustion monitoring and preventive occupational safety intervention in high
risk industrial and occupational environments.
IP202641018563 - The present invention discloses an AI-integrated Internet of Things architecture
configured for continuous learning and predictive intelligence. The architecture integrates IoT sensing
devices, edge intelligence, cloud-based analytics, continuous learning frameworks, and decision
orchestration modules to enable adaptive and autonomous operation. Machine learning models analyze
real-time and historical data to predict system states, optimize resources, and detect anomalies.
Continuous learning mechanisms update models in response to changing conditions, while federated
learning preserves data privacy. The invention enables proactive decision-making, improved efficiency,
and scalable intelligent operation across distributed IoT environments.
IP202641018561 - The present invention discloses a machine learning-driven Internet of Things
mechanism for dynamic system optimization. Distributed IoT devices continuously sense system
parameters and transmit data to edge computing units and centralized analytics platforms. Machine
learning models analyze historical and real-time data to predict system behavior and compute optimized
control actions. An optimization engine dynamically adjusts system parameters through autonomous
control mechanisms. The invention supports continuous learning, context-aware decision-making, and
secure operation, enabling improved efficiency, reliability, and adaptability in complex systems.
Introduction
Edge computing and distributed systems generate continuous streams of data that must be synchronized
across multiple devices and servers. Efficient synchronization is essential to maintain data consistency
and enable real-time decision-making.
Traditional synchronization methods such as FIFO scheduling or periodic updates treat all data equally.
These approaches fail to prioritize critical information and cannot adapt to fluctuating network
conditions. As a result, important data may experience unnecessary delays.
To address these limitations, intelligent synchronization mechanisms are required. Machine learning
techniques can analyze data patterns and network conditions to make better synchronization decisions.
This paper introduces SyncMind, an adaptive synchronization framework that integrates dual-stream
neural networks and reinforcement learning to improve data prioritization and synchronization
efficiency in edge environments.
Background
Data synchronization ensures consistency between distributed systems by replicating data across
multiple nodes. Conventional synchronization techniques include periodic replication, centralized
scheduling, and rule-based priority systems.
However, these methods face several limitations:
• Lack of adaptive decision-making
• Poor prioritization of critical data
• Limited awareness of network conditions
• Inefficient bandwidth utilization
With the increasing complexity of edge networks, synchronization systems must become more
intelligent and adaptive.
Recent advances in deep learning and reinforcement learning enable systems to analyze large data
streams and optimize decision-making dynamically. These technologies provide the foundation for
designing smarter synchronization frameworks.
Proposed Methodology
The SyncMind framework integrates three main components to achieve adaptive synchronization.
Dual-Stream Attentional Hybrid Network
The hybrid neural model processes incoming data using two streams.
The first stream captures temporal patterns to understand how data importance changes over time.
The second stream analyzes contextual relationships using attention mechanisms.
By combining these outputs, the model assigns dynamic priority scores to data packets.
Network State Monitoring
A monitoring module continuously collects network parameters including:
• Available bandwidth
• Communication latency
• Packet loss rate
• Network congestion
These metrics provide real-time awareness of the communication environment.
Deep Reinforcement Learning Engine
A reinforcement learning agent observes system states and selects synchronization actions such as:
• Immediate transmission
• Delayed scheduling
• Packet ordering
• Edge server selection
The reward function encourages lower latency, efficient bandwidth usage, and reliable data delivery.
Architecture Diagram
The SyncMind architecture consists of five layers:
1. Edge Data Generation Layer – Mobile devices and IoT sensors generate data streams.
2. Data Processing Layer – Data is preprocessed and features are extracted.
3. Priority Intelligence Layer – The hybrid neural network assigns priority scores.
4. Adaptive Synchronization Layer – Reinforcement learning selects synchronization
strategies.
5. Distributed Storage Layer – Data is replicated across cloud or edge servers.
These layers work together to enable efficient and adaptive synchronization.
System Components and Implementation
Core Components
• Mobile Data Generator
• Dual-Stream Attentional Hybrid Network
• Network Monitoring Module
• Deep Reinforcement Learning Engine
• Distributed Database Cluster
• Feedback Optimization Module
Implementation Details
The system can be implemented using:
• Python for overall system development
• TensorFlow or PyTorch for deep learning models
• Reinforcement learning frameworks for training the synchronization agent
• REST APIs for communication between edge nodes and servers
• MongoDB or PostgreSQL clusters for distributed data storage
Simulation experiments can be performed using synthetic datasets representing high-priority, normal,
and background data streams to evaluate synchronization performance under different network
conditions.
Use Cases
SyncMind can be deployed in:
• Smart healthcare monitoring systems
• IoT sensor networks
• Smart city infrastructure
• Disaster management communication platforms
• Financial transaction processing systems
• Edge-cloud synchronization environments
In healthcare scenarios, for example, emergency alerts are prioritized and transmitted immediately,
while routine medical logs are synchronized during low network congestion.
Advantages
The SyncMind framework offers several benefits:
• Intelligent priority-based synchronization
• Reduced latency for critical data
• Efficient bandwidth utilization
• Adaptive response to network changes
• Scalable distributed architecture
Summary
This paper introduces SyncMind, an intelligent and priority-aware data synchronization framework
designed to overcome the limitations of traditional static synchronization methods. The system
combines a hybrid LSTM–Transformer model for identifying data priority with a reinforcement
learning engine that dynamically selects the best synchronization strategy.
The architecture includes multiple layers such as mobile data generation, AI-based priority analysis,
network monitoring, adaptive synchronization control, and distributed storage. By analyzing temporal
patterns and current network conditions, SyncMind adjusts synchronization decisions in real time.
This approach improves synchronization efficiency, reduces latency for critical data, and uses
bandwidth more effectively. The framework is suitable for applications such as healthcare monitoring,
IoT systems, smart cities, and edge–cloud computing environments where reliable and timely data
delivery is essential.
Conclusion
This paper presented SyncMind, an adaptive edge data synchronization framework that combines dual
stream attentional neural networks with deep reinforcement learning. The proposed system
intelligently prioritizes data and dynamically adjusts synchronization strategies based on real-time
network conditions. Compared to traditional static synchronization approaches, SyncMind improves
the timely delivery of critical data and enhances network efficiency. The framework is suitable for
modern distributed environments such as IoT networks, smart healthcare systems, and edge-cloud
infrastructures. Future work may focus on large-scale deployment and real-world performance
evaluation. , Claims:Claims
1. A priority-aware distributed mobile data synchronization system comprising:
• an edge data generation layer configured to generate heterogeneous data streams from mobile
devices and IoT sensors;
• a data processing layer configured to preprocess incoming data and extract relevant features;
• a priority intelligence module incorporating a hybrid LSTM–Transformer model configured
to classify data based on urgency and contextual relevance;
• an adaptive synchronization engine utilizing reinforcement learning to determine optimal data
synchronization strategies; and
• a distributed storage layer configured to replicate synchronized data across edge and cloud
servers for scalable storage.
2. The system of claim 1, wherein the data processing layer performs data filtering,
normalization, and feature extraction to prepare data for priority classification.
3. The system of claim 1, wherein the hybrid LSTM–Transformer model combines sequential
temporal learning and contextual attention mechanisms to dynamically assign priority levels.
4. The system of claim 1, wherein the priority intelligence module categorizes data into high
priority, medium-priority, and low-priority classes based on urgency and contextual
importance.
5. The system of claim 1, wherein the reinforcement learning engine selects synchronization
actions including immediate synchronization, delayed synchronization, or packet ordering.
6. The system of claim 1, further comprising a network monitoring module configured to
evaluate real-time communication parameters including bandwidth availability, latency, packet
loss, and congestion levels.
7. The system of claim 6, wherein synchronization decisions are dynamically optimized based on
the monitored network conditions.
8. The system of claim 1, wherein high-priority data packets are synchronized with reduced
latency while lower priority data is scheduled during periods of lower network congestion.
9. A method for priority-aware mobile data synchronization, comprising:
• collecting data streams from mobile devices and IoT sensors;
• preprocessing and extracting features from the collected data;
• classifying data priority using a hybrid LSTM–Transformer model;
• evaluating real-time network conditions;
• selecting synchronization actions using reinforcement learning; and
• storing synchronized data across distributed edge and cloud servers.
10. The method of claim 9, wherein synchronization policies are continuously refined through
feedback-driven reinforcement learning to improve latency reduction, bandwidth utilization,
and system reliability.