Neural Sensor Quality Assessment for Runtime Sensor Selection
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Solution Overview
Problem
In multi-sensor environments, it is challenging to assess and compare the characteristics of redundant sensors to select the best device for accurate and resource-efficient sensory inferences due to varying sensor quality, model accuracy, spatiotemporal coverage, and runtime behavior, which leads to redundant computations and inefficient energy usage.
Innovation Solution
A system that utilizes a neural network to process sensor data from multiple devices, determining the suitability of each sensor for a task by applying input sub-networks and fully connected layers to generate a quality assessment, dynamically selecting the best sensors for data collection based on their suitability, and iteratively adjusting sensor activation to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are deployed to ensure redundancy and reliability, then system reliability is improved, but computational redundancy and energy consumption increase
Solution Approach 1:
The system dynamically adjusts sensor activation based on runtime quality assessment. Sensors are selectively activated or deactivated depending on their current performance characteristics, allowing the system to maintain reliability when needed while reducing energy consumption when redundancy is not required. This dynamic adaptation resolves the contradiction between maintaining high reliability and reducing energy consumption from redundant sensors.
Solution Approach 2:
The system changes operational parameters by modifying which sensors are active based on assessed quality metrics. By monitoring sensor performance parameters in real-time and adjusting activation status accordingly, the system optimizes the balance between reliability (having multiple sensors available) and energy consumption (activating only necessary sensors).
2Reliability
If multiple sensors are deployed to ensure redundancy, then system reliability is improved, but computational redundancy increases
Solution Approach 1:
The system dynamically adjusts computational workload by selectively activating sensors based on their current quality assessment. When sensors are assessed as low-quality, the system deactivates them to reduce computational redundancy. This dynamic adjustment maintains system reliability when high-quality sensors are available while improving computational efficiency when redundancy is excessive.
Solution Approach 2:
The system extracts or removes redundant computational processing by deactivating sensors that are assessed as low-quality. This extraction of unnecessary computational elements reduces overall computational redundancy while preserving system reliability through the remaining high-quality sensors.
3Adaptability or versatility
If sensor quality varies across devices, then device versatility is improved, but difficulty in selecting the best device increases
Solution Approach 1:
The system employs self-service through automated quality assessment mechanisms that continuously monitor and evaluate sensor performance. This self-assessment capability simplifies the complexity of varying sensor qualities by providing standardized, automated evaluations, making it easier to select the best devices without manual intervention or complex analysis.
Solution Approach 2:
The system uses feedback loops where sensor performance is continuously measured and fed back into the selection process. This feedback mechanism simplifies the complexity of device versatility by providing real-time information about sensor quality, enabling easier and more informed device selection based on current performance data.
Data Source
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.