Quantum Optimization Engine for Intelligent Sensor Data Transfer
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Solution Overview
Problem
The increasing number of network-enabled devices competing for network bandwidth and resources creates a need for an improved data transfer system that can intelligently optimize caching and processing of data between sensor devices and a receiving platform layer application.
Innovation Solution
A system comprising a memory device with computer-readable program code, a communication device, and a processing device that collects sensor data, combines it with contextual data, generates data transfer rules, and calculates a data configuration flow to control data transfer from user devices to an application server, utilizing a quantum optimization engine and machine learning engine for efficient prioritization and grouping of data transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of network-enabled devices increases, then more sensors and actuators can be deployed for conditional functions, but network bandwidth and resources become insufficient for efficient data transfer
Solution Approach 1:
The system segments network traffic into different priority levels (first priority for actuator commands and sensor data, second priority for application data) and routes them through different pathways. High-priority traffic receives guaranteed bandwidth through quality of service (QoS) policies, while lower-priority traffic uses available capacity, thereby efficiently managing network resources despite increasing device quantities.
Solution Approach 2:
A network controller acts as an intermediary device that monitors network conditions, enforces QoS policies, and dynamically manages data flow between edge devices and the cloud platform. This intermediary optimizes bandwidth allocation by prioritizing time-sensitive traffic and preventing network congestion, thus maintaining efficiency as device numbers grow.
2Productivity
If data transfer priority is not optimized, then all devices compete equally for network resources, but data transfer latency increases and network efficiency decreases
Solution Approach 1:
The system dynamically adjusts data transfer priorities based on real-time network conditions and device requirements. The network controller continuously monitors traffic patterns and modifies QoS parameters adaptively, ensuring that time-sensitive communications (actuator commands, sensor data) maintain high priority while less urgent traffic (application data) uses available bandwidth, thereby minimizing latency and maximizing overall transfer efficiency.
Solution Approach 2:
The network controller implements feedback mechanisms by monitoring network performance metrics (latency, bandwidth utilization, packet loss) and adjusting QoS policies accordingly. This closed-loop control ensures that priority assignments remain optimal as network conditions change, preventing congestion and maintaining efficient data transfer across the network.
3Loss of energy
If intelligent optimization is implemented for data transfer, then network resource allocation improves, but system complexity increases
Solution Approach 1:
The network controller serves as a centralized intermediary that handles the complexity of intelligent optimization, isolating this complexity from individual edge devices. The controller implements QoS policies, monitors network conditions, and makes optimization decisions centrally, allowing simple edge devices to benefit from intelligent resource allocation without requiring complex local processing or decision-making capabilities.
Solution Approach 2:
The system uses feedback loops where the network controller monitors performance metrics and automatically adjusts data transfer parameters. This automated feedback mechanism reduces the need for manual configuration and complex user intervention, allowing the system to maintain optimal performance through adaptive control while keeping the user-facing interface simple.
Data Source
AI summary
A system for intelligent data transfer is provided. The system is configured to: collect sensor data from a plurality of user devices, the plurality of user devices being connected to a device gateway in an edge layer of the network; combine the collected sensor data with contextual data stored in a contextual device database, wherein the contextual data comprises device usage data and user data; generate a data transfer rule set for governing data transfer from the plurality of user devices over the network based on the combined data; calculate a data configuration flow for the plurality of user devices based on the data transfer rule set; and execute the data configuration flow to control a flow of the sensor data transferred from the device gateway to an application server in a platform layer.