Sensor Network Self-Healing via Closed-Loop Performance Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor networks face challenges in maintaining reliability and adaptability due to frequent hardware and software faults, leading to deteriorating knowledge and inferences, which current self-healing frameworks struggle to address effectively.
Innovation Solution
A network device with a multi-agent programming framework and machine learning capabilities autonomously evaluates performance, determines adjustments to sensor devices and aggregation nodes, and triggers corrective actions to improve knowledge gathering and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If self-healing frameworks are implemented to detect hardware and software faults, then reliability is improved, but device complexity increases
Solution Approach 1:
The system implements self-healing capabilities where the sensor network automatically detects faults, evaluates performance deterioration, and adjusts its own operation without external intervention. The self-healing framework monitors sensor data quality, identifies hardware and software faults, and triggers corrective actions autonomously, allowing the system to service itself and maintain reliability without adding significant operational complexity
Solution Approach 2:
The patent implements a feedback mechanism where sensor data is continuously evaluated against performance criteria, and the results feed back into system adjustments. The framework monitors the quality of sensor data, compares it with expected performance levels, and uses this feedback to trigger appropriate corrective actions, creating a closed-loop system that automatically responds to faults while maintaining manageable complexity through structured feedback processes
2Adaptability or versatility
If the network dynamically adjusts sensor devices and aggregation nodes to compensate for faults, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts the operation of sensor devices and aggregation nodes based on real-time performance evaluation. When faults are detected or performance deterioration is identified, the framework automatically modifies sampling rates, data aggregation strategies, and processing priorities, allowing the network to adapt to changing conditions while maintaining manageable complexity through rule-based dynamic adjustment mechanisms
Solution Approach 2:
The patent segments the sensor network into independent sensor devices, aggregation nodes, and a central framework, each with specific responsibilities. This segmentation allows dynamic adjustments to be made at different levels of the hierarchy without requiring system-wide reconfiguration, improving adaptability while containing complexity within manageable modular components
3Measurement precision
If frequent performance evaluations are performed to detect knowledge deterioration, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The framework performs performance evaluations periodically rather than continuously, assessing sensor data quality at defined intervals based on the evaluation criteria. This periodic evaluation approach maintains measurement precision by regularly checking for knowledge deterioration and performance faults while significantly reducing energy consumption compared to continuous monitoring, as the system can enter lower-power states between evaluation cycles
Data Source
Figure 1
Figure 2
Figure 3
AI summary
It is provided a method for adjusting a network of sensor devices. The network also comprises an aggregation node for aggregating sensor data from at least two of the sensor devices to processed data and an application node configured to approach an application goal based on the processed data. The method is performed in a network device. The method comprises repeatedly performing the steps of: receiving an evaluation of performance of the application node in relation to the application goal; determining that the evaluation indicates that an adjustment is needed; determining an operation adjustment of at least one of the aggregation node and the sensor devices; and triggering the adjustment.