Autonomous Telemetry Orchestration for IoT Transaction Verification
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Solution Overview
Problem
The increasing complexity and sophistication of digital interactions in IoT device transactions pose challenges to ensuring authenticity and security, exacerbated by malfeasance and deep fake technology, compromising customer trust.
Innovation Solution
An autonomous telemetry orchestration engine leveraging cognitive AI analyzes geolocation, device ID, device intelligence, and SSL to generate a calculated score, halting transactions or issuing alerts if thresholds are breached, ensuring secure and verified interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous telemetry orchestration with cognitive AI analysis is implemented, then transaction security and integrity are improved, but system complexity increases
Solution Approach 1:
The patent introduces an autonomous telemetry orchestration engine as an intermediary system that sits between IoT devices and transaction processing systems. This engine aggregates telemetry data from multiple sources (device sensors, network information, location data) and performs cognitive AI analysis to generate security scores, thereby mediating the complexity between raw data collection and security decision-making without requiring end systems to handle all analytical complexity directly
Solution Approach 2:
The telemetry orchestration engine operates autonomously, self-managing the collection, aggregation, and analysis of telemetry data without requiring manual intervention. The cognitive AI components automatically process data streams, adjust analysis parameters, and generate security assessments independently, reducing the operational burden despite the system's analytical complexity
2Measurement precision
If multiple telemetry parameters are analyzed in real-time, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing telemetry data before transactions occur. Telemetry parameters such as device sensors, location information, and network characteristics are aggregated and pre-analyzed in advance, so that when a transaction needs verification, the data is already prepared and structured for rapid cognitive AI assessment, reducing real-time processing delays
Solution Approach 2:
The patent replaces traditional mechanical or rule-based verification systems with cognitive AI analysis. Instead of using fixed threshold checks or sequential validation rules, the system employs machine learning models that can simultaneously evaluate multiple telemetry parameters and their interrelationships, achieving high measurement precision through intelligent pattern recognition rather than exhaustive mechanical processing
Data Source
AI summary
Systems, computer program products, and methods are described herein for autonomous telemetry orchestration. The present disclosure is configured to initiate and attempt transactions using IoT devices, generate unique session tokens, and verify session details against an orchestration engine by analyzing various parameters such as IP address, device ID, location, operating system, and mobile number. The system conducts a calculated score assessment and compares the score against a predefined threshold to determine transaction legitimacy. Transactions proceed if the score is below the threshold, otherwise, they are halted and alerts are issued. The system dynamically adjusts assessment models using machine learning algorithms based on historical data, employs blockchain technology for unique session tokens, and generates alerts via messaging services for suspicious activities.