Self-Organizing Mobile Data Collection for Industrial Sensor Networks
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Solution Overview
Problem
Industrial environments face challenges in collecting and utilizing data from multiple sensors due to varying computing resources, network capabilities, and harsh conditions, leading to inefficient monitoring, control, and optimization of operations.
Innovation Solution
The implementation of methods and systems for data collection and processing that include continuous ultrasonic monitoring, self-organizing data marketplaces, on-device sensor fusion, and AI training based on industry-specific feedback, along with augmented reality and virtual reality interfaces for improved data utilization and real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected continuously from multiple sensors in industrial environments, then monitoring capability is improved, but data collection efficiency deteriorates due to varying computing resources and network capabilities
Solution Approach 1:
The patent segments the data collection system into multiple autonomous mobile data collector units, each capable of independently collecting and processing sensor data. This segmentation allows the system to scale efficiently by adding individual units without overwhelming central computing resources, thereby maintaining monitoring capability while improving data collection efficiency through distributed processing.
Solution Approach 2:
Mobile data collector units are equipped with autonomous capabilities including self-navigation, self-organization, and self-management of data collection tasks. These units can independently adapt to varying computing resources and network conditions at different locations, eliminating the need for centralized control and improving overall system efficiency while maintaining reliable monitoring.
2Area of stationary object
If mobile data collector units are deployed to multiple locations, then data coverage is improved, but system complexity increases due to self-organization requirements
Solution Approach 1:
The patent implements dynamic self-organization algorithms that allow mobile data collector units to automatically adapt their distribution and behavior based on real-time environmental conditions, data collection needs, and resource availability. This dynamic approach enables extensive data coverage across multiple locations while keeping system complexity manageable through adaptive rather than predetermined coordination.
Solution Approach 2:
The system changes operational parameters such as data collection frequency, transmission intervals, and unit distribution patterns based on environmental conditions and resource availability. This parameter adaptation allows the system to expand coverage area without proportionally increasing system complexity, as units adjust their behavior to match local conditions rather than requiring complex centralized coordination.
3Productivity
If sensor data is processed in real-time, then operational optimization is improved, but computing resource requirements increase
Solution Approach 1:
The patent applies partial processing at the edge devices (mobile data collector units) and selective transmission of only critical or processed data to central systems. This partial action approach enables real-time operational optimization for time-sensitive parameters while reducing overall computing resource requirements by not processing all data at maximum capacity continuously.
Solution Approach 2:
The mobile data collector units serve as intermediary devices between sensors and central processing systems. They perform preliminary data processing, filtering, and aggregation locally, reducing the volume of data requiring centralized processing while maintaining real-time optimization capabilities for critical operations. This intermediary role distributes computing load and reduces total resource requirements.
Data Source
AI summary
The present disclosure describes systems and methods for data collection in an industrial environment having self-organization functionality. A method can include analyzing a plurality of sensor inputs, sampling data received from the sensor inputs, and self-organizing at least one of (i) a storage operation of the data, (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs. The method may further include receiving instructions directing a mobile data collector unit to operate sensors at a target, transmitting a communication to one or more other mobile data collector units regarding the instructions, and self-organizing a distribution of the mobile data collector unit.