Protocol-Agnostic IoT Risk Aggregation for Real-Time Warnings
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Solution Overview
Problem
Existing IoT systems lack a comprehensive risk management system that integrates data from diverse sensors and autonomous entities to identify and mitigate risks in real-time, leading to issues such as vehicle accidents, traffic jams, and inefficiencies, with unclear liability and public acceptance.
Innovation Solution
A protocol-agnostic data aggregator system that processes data from IoT-capable sensors and autonomous entities using artificial intelligence to identify patterns, send real-time notifications, and provide inter-device communications for risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from diverse IoT sensors and autonomous entities is integrated and processed in real-time using AI, then risk identification and mitigation capability is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex risk management functionality into distinct modular components: data aggregation layer (protocol-agnostic aggregator), processing layer (AI/ML engines for pattern recognition), analysis layer (risk assessment modules), and response layer (notification and mitigation systems). This segmentation allows each component to be developed, maintained, and scaled independently, reducing overall system complexity while maintaining comprehensive risk identification capabilities.
Solution Approach 2:
The patent introduces intermediary components including protocol-agnostic data aggregators that mediate between diverse IoT sensor sources and the core processing system, and AI/ML-based pattern recognition engines that act as intermediaries between raw data and risk assessment logic. These intermediaries standardize data formats, filter noise, and prepare information for downstream analysis, thereby managing complexity at the system architecture level.
2Speed
If real-time data processing and analysis is implemented across multiple sensors and autonomous entities, then risk detection speed is improved, but computational resources and energy consumption increase
Solution Approach 1:
The system performs preliminary data processing and filtering at the edge devices and data aggregation layer before transmitting data to central processing systems. Pre-processing steps include data validation, format standardization, and initial anomaly detection, which reduce the volume and complexity of data requiring intensive computational analysis, thereby lowering overall energy consumption while maintaining real-time detection capabilities.
Solution Approach 2:
The patent implements selective real-time processing where only data streams and sensor inputs relevant to current risk conditions are processed at full speed, while other data undergoes batch processing or lower-priority analysis. The system dynamically adjusts processing intensity based on risk levels, traffic conditions, and resource availability, applying computational resources partially rather than continuously across all data streams.
3Measurement precision
If comprehensive sensor data aggregation from multiple manufacturers and device types is implemented, then data coverage and risk analysis accuracy are improved, but protocol compatibility challenges and integration complexity increase
Solution Approach 1:
The patent implements protocol-agnostic data aggregation capabilities that enable a single system architecture to interface with multiple IoT sensor protocols and communication standards from different manufacturers. The system employs universal data models, standardized message formats, and adaptive protocol handlers that can dynamically adjust to various input formats, allowing comprehensive multi-source data aggregation without requiring separate integration systems for each protocol type.
Solution Approach 2:
The system dynamically adjusts data processing parameters, aggregation intervals, and analysis thresholds based on the specific protocol characteristics and data quality metrics of each sensor source. This parameter adaptation allows the system to optimize risk analysis accuracy for each device type while maintaining a unified processing framework, effectively managing protocol diversity through flexible parameter configuration rather than rigid protocol-specific code paths.
4Loss of time
If real-time notifications and inter-device communications are implemented for risk warnings, then response time to risks is improved, but communication overhead and network traffic increase
Solution Approach 1:
The system implements localized risk response where notifications and alerts are targeted specifically to devices and entities directly affected by detected risks, rather than broadcasting to all connected devices. The notification system determines spatial and contextual relevance of risk events, sending warnings only to autonomous entities within affected zones or those with direct exposure to identified hazards, thereby reducing unnecessary network traffic and communication overhead.
Data Source
AI summary
A risk management system that includes an internet of things (IoT) integrated logic engine connected to IoT-capable sensors and devices and autonomous entity sensors and devices. The logic engine processes and analyzes in real-time the data from the plurality of IoT-capable sensors and the autonomous entity sensors. The logic engine further identifies novel patterns and pre-defined data patterns in the data from the plurality of IoT-capable sensors and the autonomous entity sensors to determine that a risk is occurring or imminent. The logic engine further sends real-time notifications to a set of subscribers of the risk management system about the risk that is occurring or imminent and provide inter-device communications to provide real-time warnings between one or more of the plurality of sensor-enabled devices and the autonomous entity devices.