Sensor Signal Feature Profiling for Hybrid Local-Remote Processing
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Solution Overview
Problem
Existing sensor-enabled devices face challenges in efficiently processing heterogeneous and complex sensor signal data, leading to difficulties in reliable event detection and increased costs, due to limited hardware and software resources, and the inefficiencies of remote data processing.
Innovation Solution
A processor-controlled method that generates a feature profile from sensor data, matches it against predetermined profiles, and performs operations locally or remotely based on the evaluation, using a hybrid approach that balances local and remote processing to optimize resource usage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor signal data is processed locally by the device, then response time and reliability are improved, but processing complexity and resource consumption increase
Solution Approach 1:
The patent segments the data processing task into two parts: local processing of simple, routine sensor data by the device itself, and remote processing of complex data requiring advanced algorithms. This segmentation allows the device to maintain high reliability for common events while avoiding the burden of processing all complex data locally, thus resolving the contradiction between reliability and processing complexity.
Solution Approach 2:
The system dynamically adjusts the processing location based on the complexity of the sensor data and the type of event detected. For routine events, processing remains local; for complex events, the system transitions to remote processing. This dynamic approach optimizes the balance between reliability and processing complexity in real-time.
2Measurement precision
If heterogeneous sensor data from multiple sensors is processed, then detection accuracy is improved, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing heterogeneous sensor data into different processing categories: routine data processed locally and complex multi-sensor data processed remotely. This allows the system to maintain high detection accuracy for complex events involving multiple sensors while avoiding the resource burden of processing all heterogeneous data locally.
Solution Approach 2:
The system introduces an intermediary remote processing system that handles complex heterogeneous sensor data. This intermediary receives data from multiple sensors, performs sophisticated analysis, and returns results to the device, thereby improving detection accuracy without increasing the processing complexity burden on the device itself.
3Speed
If all sensor data is processed locally, then response time is improved, but device power consumption and hardware costs increase
Solution Approach 1:
The patent segments data processing between local and remote systems based on event type and data complexity. Routine events are processed locally ensuring fast response times, while complex events are processed remotely reducing local power consumption. This segmentation resolves the contradiction by optimizing the response-time-power consumption tradeoff.
Solution Approach 2:
Instead of processing all sensor data locally, the system applies partial processing locally and remote processing for complex cases. This partial action approach maintains fast response for critical routine events while avoiding excessive power consumption that would result from processing all data locally.
4Device complexity
If remote processing is used for complex sensor data, then processing capacity requirements are reduced locally, but response time and network dependency increase
Solution Approach 1:
The patent segments processing tasks by urgency and complexity: time-critical routine events are processed locally without remote dependency, while non-urgent complex events are processed remotely. This segmentation minimizes response time delays by ensuring critical operations remain local while still benefiting from remote processing capacity for complex analysis.
Solution Approach 2:
The system dynamically determines whether to process data locally or remotely based on event characteristics, data complexity, and network availability. This dynamic decision-making optimizes the balance between local processing capacity and response time, switching to local processing when speed is critical and to remote processing when complex analysis is needed and time is less constrained.
Data Source
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AI summary
A method (10) of and a device (50) for performing an operation based on sensor signal data obtained from at least one sensor. From the sensor signal data, by a processor (54) of the device (50), a feature profile is generated that is matched (12) to a set of predetermined feature profiles. The operation is performed (13) by the device (50) based on the sensor signal data, if the generated feature profile matches at least one of the set of predetermined feature profiles, and the operation is performed (14) by the device (50) based on remote processing (40) of the sensor signal data (40) if the generated feature profile does not match at least one of the set of predetermined feature profiles. With the disclosed method, balance in processing power, processing time, and reliable operation by the device (50) is achieved without incurring extra cost for upgrading the device (50).