Trend Envelope Data Filtering for Event-Driven Transmission
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Solution Overview
Problem
Existing data transmission methods, particularly those using predefined schedules, often result in unnecessary data transmission, wasting bandwidth and battery resources, as they transmit data regardless of changes, leading to delays and increased costs, and may not capture significant events in a timely manner.
Innovation Solution
A system and method that utilize data analytics to define a trend envelope and transmit only significant data points outside this envelope, along with their gradients and history points, optimizing data transmission by sending relevant data when necessary and minimizing unnecessary transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is transmitted at fixed periodic intervals, then timing certainty is provided, but unnecessary data transmission occurs wasting bandwidth and battery resources
Solution Approach 1:
The system dynamically changes the transmission parameter (data selection criteria) from fixed periodic transmission to conditional transmission based on trend envelope analysis. Data points are evaluated against upper and lower trend bounds, and transmission occurs only when significant changes are detected, optimizing both timing responsiveness and resource efficiency.
Solution Approach 2:
The remote monitoring device autonomously determines which data points require transmission by comparing current data against the trend envelope stored in its memory. The device self-manages the transmission decision-making process without requiring continuous external scheduling, reducing unnecessary communications while maintaining timing certainty for significant events.
2Loss of time
If data is transmitted at fixed periodic intervals, then timing certainty is provided, but significant events may be delayed until the next scheduled transmission
Solution Approach 1:
The system implements feedback by continuously monitoring data points against the trend envelope and immediately triggering transmission when significant changes are detected. This feedback mechanism ensures that critical events are reported promptly rather than waiting for the next scheduled interval, improving reliability while maintaining timing certainty for important data.
Solution Approach 2:
The transmission system transitions from static fixed-interval scheduling to dynamic event-driven transmission. The system adapts its behavior based on the actual state of the monitored parameter, increasing transmission frequency only when significant changes occur outside the trend envelope, thus improving timely reporting without unnecessary transmissions during stable periods.
3Loss of information
If all data points are transmitted, then complete data availability is achieved, but bandwidth and battery resources are wasted
Solution Approach 1:
The system extracts only the essential data points that require transmission by comparing current data against the stored trend envelope. Only data points that fall outside the upper or lower trend bounds are selected for transmission, along with relevant context data. This extraction approach maintains data completeness for significant events while eliminating unnecessary transmissions of redundant data within normal operational ranges.
4Productivity
If transmission bandwidth is increased through more physical media, then data transmission capacity is improved, but cost increases
Solution Approach 1:
The system applies partial action by transmitting only the necessary portion of data (points outside the trend envelope) rather than all collected data. This selective transmission approach achieves adequate data transmission capacity for monitoring purposes while minimizing bandwidth consumption and associated costs, avoiding the need for expensive infrastructure upgrades.
Data Source
AI summary
A system and method for data filtering and transmission management are provided. In particular, disclosed is a method of transmission management for data acquired by a remote monitor having a sensor. The method comprises the steps of: defining an initial trend envelope having a window around a forecast trend gradient, the window defined by an initial upper bound and an initial lower bound; and processing a set of data points acquired by the sensor, to identify any data points outside the initial trend envelope. When a point is identified outside the initial trend envelope, the method: (i) transmits an event data packet to a central server; and (ii) identifies a subsequent trend envelope based on a trend gradient derived from a preceding set of data points, said preceding set of points including an identified point from the event data packet.