Sensor Telemetry Detection via Traffic Analysis Service
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Solution Overview
Problem
The lack of standardization in wireless sensor tags leads to proprietary data formats, making it difficult for applications and devices from different manufacturers to extract and utilize sensor measurements, resulting in low network visibility and limited cross-vendor interoperability in IoT networks.
Innovation Solution
A traffic analysis service that uses machine learning to form payload signatures and classify sensor measurements within packets, allowing for the detection and identification of sensor telemetry across various manufacturers and data formats, independent of vendor-specific formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If proprietary data formats are used by sensor tags from different manufacturers, then device functionality and manufacturer-specific optimization are improved, but cross-vendor interoperability and network visibility deteriorate
Solution Approach 1:
The patent introduces a traffic analysis service as an intermediary component that sits between sensor tags and applications. This service receives packets from diverse sensor tags, analyzes their proprietary data formats, extracts sensor measurements, and makes them available to applications without requiring applications to understand vendor-specific formats. The intermediary absorbs the complexity of multiple data formats while providing a standardized interface to applications.
Solution Approach 2:
The patent creates a virtual representation or copy of the sensor measurement data that is independent of the original proprietary data format. By copying the essential sensor measurement information into a standardized format, the system allows applications to work with a uniform data representation while the original diverse data formats continue to be used by sensor tags.
2Loss of information
If no standardization is implemented for wireless sensor tags, then manufacturer freedom and device flexibility are improved, but network visibility and data extraction capability deteriorate
Solution Approach 1:
The traffic analysis service continuously monitors network traffic from sensor tags, analyzes the data formats, extracts sensor measurements, and provides feedback about the types of sensor data being transmitted. This feedback mechanism enables the system to adapt to new data formats while maintaining network visibility, as the service learns from and responds to the actual traffic patterns observed in the network.
Solution Approach 2:
The traffic analysis service acts as an intermediary that bridges the gap between unstandardized sensor tag data and applications requiring standardized data. It receives the raw proprietary data, processes it into a standardized format, and delivers it to applications, thereby maintaining network visibility without requiring end devices to implement standardization.
3Ease of operation
If applications must understand vendor-specific data formats to extract sensor measurements, then data extraction accuracy is improved, but system complexity and implementation burden increase
Solution Approach 1:
The traffic analysis service serves as a mediator that handles all the complexity of data format understanding and sensor measurement extraction. Applications simply need to query the service for sensor data from specific sensor tags, and the service performs the complex format translation and extraction work, significantly simplifying application implementation.
Solution Approach 2:
The traffic analysis service performs self-service by automatically detecting, analyzing, and processing data formats from sensor tags without requiring applications to manually configure or understand the proprietary formats. The service autonomously handles the complex tasks of data format recognition and sensor measurement extraction.
Data Source
AI summary
In one embodiment, a traffic analysis service receives payload data from packets sent by a sensor tag in a network. The service forms a payload signature for the sensor tag, based on the payload data. The payload signature is indicative of one or more bytes in the payload that vary across the packets. The service identifies a portion of the payload data as potentially including a sensor measurement, based on the payload signature. The service uses a machine learning classifier to assign a sensor measurement type to the identified portion of the payload data.