Multi-Sensor Runtime Assessment for Selective Sensor Activation
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Solution Overview
Problem
In multi-sensor environments, it is challenging to assess and compare the quality of redundant sensors for accurate and resource-efficient inferences due to varying sensor qualities, model accuracies, and runtime behaviors, leading to redundant computations and inefficient energy usage.
Innovation Solution
A system utilizing a neural network to process sensor data from multiple devices, determining the suitability of each sensor for a task, and selectively activating the best sensors based on their quality assessment, with a dynamic execution plan to optimize sensor usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to monitor device status and predict failures, then maintenance reliability is improved, but false alarms and incorrect predictions reduce operational efficiency
Solution Approach 1:
The system continuously monitors sensor data, compares it against threshold values, and adjusts maintenance schedules based on actual device behavior patterns. This feedback loop allows the system to learn from past predictions and refine future assessments, reducing false alarms while maintaining high reliability in detecting actual failures.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and threshold values based on device operating conditions and historical data. By adapting sensitivity levels and evaluation criteria over time, the system optimizes the balance between detecting early failure signs and avoiding false positives, thereby improving operational efficiency.
2Measurement precision
If continuous monitoring of sensor data is performed, then device status is accurately tracked, but computational resources and energy consumption increase
Solution Approach 1:
Instead of continuous processing, the system evaluates sensor data at strategically determined intervals based on device operational state and change detection. When parameters remain stable within acceptable ranges, monitoring frequency is reduced; when anomalies are detected, frequency increases. This periodic action maintains measurement precision while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The system performs preliminary data filtering and anomaly detection at the sensor level before transmitting data for comprehensive analysis. By pre-processing and identifying only the most critical data points, the system reduces the computational burden on central processing units and lowers overall energy consumption while maintaining accurate device status tracking.
3Measurement precision
If multiple sensors are deployed to improve monitoring coverage, then device status detection is enhanced, but system complexity and cost increase
Solution Approach 1:
The system employs a unified multi-functional sensor framework where individual sensors serve multiple purposes through flexible data interpretation and contextual analysis. By implementing adaptive evaluation algorithms that can interpret the same sensor data in different contexts, the system achieves enhanced monitoring coverage without proportionally increasing the number of physical sensors, thereby controlling system complexity.
Solution Approach 2:
The system combines data from multiple sensors through integrated analysis algorithms that synthesize information into unified device status assessments. By merging sensor inputs and creating a cohesive view of device health, the system achieves comprehensive monitoring with fewer discrete sensor components, reducing overall system complexity while maintaining detection precision.
Data Source
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AI summary
This relates to the use of sensor evaluation in a multi-sensor environment. In a first aspect, this specification describes apparatus comprising: at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: receive sensor data from a plurality of sensors collected during a first time period; process the received sensor data through a plurality of layers of a neural network to generate an output indicative of the sensing quality of each of the plurality of sensors for a task; and cause a subset of the plurality of sensors to collect data during a second time period based on the output indicative of the suitability of each of the plurality of sensors for the task.