Luminaire Spatial-Temporal Model for Anomaly Detection
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Solution Overview
Problem
The excessive data transmission from luminaires with onboard sensors can overwhelm network bandwidth, leading to limited diagnostic capabilities and wastage of computational resources, as existing systems indiscriminately collect and process data from multiple devices without predicting anomalies effectively.
Innovation Solution
Implementing a distributed processing system that uses spatial-temporal models to identify and predict hardware and network anomalies by generating variance data from sensor inputs, allowing individual luminaires to compensate for anomalies without transmitting excessive data, thereby conserving resources and improving maintenance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensors indiscriminately collect and transmit data from multiple luminaires, then comprehensive environmental monitoring is achieved, but network bandwidth is overwhelmed and diagnostic capabilities are limited
Solution Approach 1:
The system performs preliminary anomaly prediction by comparing current sensor data against historical patterns and spatial-temporal models before transmitting data to the network. This preliminary processing at the luminaire level filters out normal variations and only transmits anomaly-related information, thus achieving comprehensive monitoring while conserving network bandwidth
Solution Approach 2:
The monitoring system is segmented into distributed luminaire-level processing units that independently analyze their own sensor data using onboard processors. Each luminaire segments the data processing task, performing local anomaly detection and only transmitting relevant anomaly data to the central network, thereby reducing overall network traffic while maintaining comprehensive monitoring coverage
2Loss of information
If excessive data is transmitted from luminaires to the network, then complete diagnostic information is available, but network communications are impeded and crucial diagnostic tests are limited
Solution Approach 1:
The system extracts only the essential anomaly-related information from the complete sensor data set at the luminaire level. By using processors to analyze sensor readings against spatial-temporal models and identify deviations, the system extracts only diagnostically relevant data for network transmission, preserving diagnostic information while maintaining network communication efficiency
Solution Approach 2:
The luminaire's onboard processor acts as an intermediary between the sensors and the network. It processes sensor data locally, performing preliminary diagnostic analysis and filtering before network transmission. This intermediary processing ensures that only refined, anomaly-related information reaches the network, maintaining both diagnostic capability and communication ease
3Loss of information
If all sensor data from the network is collected and processed, then complete operational insights are obtained, but computational resources are wasted on processing non-insightful data
Solution Approach 1:
The system performs preliminary data filtering and anomaly detection at the luminaire level before data leaves the device. By comparing sensor readings against historical patterns and spatial-temporal models locally, the system identifies and flags only anomaly-related data points for further processing, eliminating the need to process normal operational data at centralized facilities and thus conserving computational resources
Solution Approach 2:
Each luminaire possesses localized processing capability with onboard processors that perform data analysis specific to its operational context. This local quality of processing allows each device to independently determine which data requires further analysis, ensuring that computational resources are applied only where and when anomalies occur rather than uniformly processing all data across the network
Data Source
AI summary
The described implementations provided herein relate to systems, methods, and apparatuses for using a network of luminaires to perform distributed computation of sensor data to identify hardware and network anomalies. In some implementations, a method is set forth as including operations such as receiving, from a first luminaire (114, 128, 216), first sensor data (122, 124) in response to a stimulus (204) affecting a network of luminaires (110), and receiving, from a second luminaire (208) in the network of luminaires, second sensor data. The method can also include determining a correlation between the first sensor data and the second sensor data, and modifying a luminaire spatial-temporal model (222) based at least partially on the correlation. The method can also include receiving subsequent sensor data from the first luminaire or the second luminaire, and providing a signal (212) to the first luminaire or the second luminaire based on a comparison of the subsequent sensor data to the luminaire spatial-temporal model.