Adaptive Telemetry Sampling for Lower Data Collection Cost
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Solution Overview
Problem
The proliferation of telemetry data leads to increased costs related to networking, computing, and data storage, necessitating a need for improved systems and methods to analyze telemetry data with reduced computational effort without compromising insights.
Innovation Solution
An adaptive telemetry sampling system that identifies logical groups of telemetry data for reduced sampling rates, maintaining a guaranteed error margin and estimating cost savings, while inferring metric values as if full data collection had occurred.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If fixed-rate telemetry sampling is used, then system simplicity is maintained, but power consumption increases and useful data is lost during low-activity periods
Solution Approach 1:
The telemetry system dynamically adjusts the sampling rate based on the operational state of the implantable medical device. When the device is in a low-power state with no therapeutic deliveries, the sampling rate is reduced to conserve battery power. When therapeutic deliveries are occurring or imminent, the sampling rate increases to capture clinically relevant data. This dynamic adaptation resolves the contradiction by making power consumption variable rather than fixed.
Solution Approach 2:
The system changes the sampling rate parameter based on operational conditions. A rate determination module monitors device state and adjusts the sampling rate accordingly - using a first (lower) sampling rate during low-activity periods and a second (higher) sampling rate during high-activity periods. This parameter adjustment directly addresses the energy loss issue while managing system complexity through controlled variable adjustment.
2Loss of information
If fixed-rate telemetry sampling is used, then data collection is simple, but data efficiency decreases when no therapeutic deliveries are occurring
Solution Approach 1:
The sampling rate parameter is changed based on operational context. During periods when no therapeutic deliveries are occurring, a lower sampling rate is applied, reducing power consumption while still capturing sufficient data for device monitoring. When therapeutic deliveries occur or are scheduled, the sampling rate increases to ensure complete data capture, thus preventing useful information loss while optimizing energy usage.
Solution Approach 2:
The system transitions from static fixed-rate sampling to dynamic variable-rate sampling. The telemetry system continuously monitors device operational state and adapts the sampling rate in real-time, increasing sampling during therapeutic deliveries to capture all clinically relevant data, and decreasing sampling during idle periods to conserve battery power, thereby optimizing both data efficiency and energy consumption.
3Loss of information
If higher sampling rate is used during therapeutic deliveries, then data completeness is improved, but power consumption increases
Solution Approach 1:
The sampling rate parameter is increased specifically during therapeutic delivery events to ensure complete and accurate data capture. The rate determination module detects when therapeutic deliveries are occurring or scheduled and temporarily increases the sampling rate to a higher level. This targeted parameter change ensures data completeness during critical events while limiting increased power consumption to only the necessary duration and scope.
Solution Approach 2:
The system applies higher sampling rates periodically during therapeutic delivery events rather than continuously. The telemetry system monitors for specific operational conditions (therapeutic deliveries) and activates high-rate sampling only during these periodic events. This approach ensures data completeness when needed while minimizing overall power consumption by using high-rate sampling only during brief, specific time windows rather than continuously.
Data Source
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AI summary
A data processing system implements adaptive telemetry sampling by obtaining first telemetry data from a plurality of telemetry data sources, analyzing the first telemetry data to identify a subset of telemetry data sources for which a reduced sampling rate may be implemented, determining a reduced sampling rate for each event type of the plurality of event types, selecting a subset of the event types for which the reduced sampling rate is to be applied, obtaining second telemetry data from the subset of telemetry data sources at the reduced sampling rate associated with each event type of the subset of event types, analyzing the second telemetry data to determine one or more estimated metric values for one or more metrics, and generating a report comprising the one or more estimated metric values and an estimated total cost saving based on an estimated cost saving associated with each event type.