Sensor Network Fault Signatures for Smart Building Connectivity
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Solution Overview
Problem
Smart home networks with numerous sensors and IoT devices often experience connectivity issues due to poorly designed networks, leading to increased retransmissions, power consumption, and delays, which are particularly problematic for delay-sensitive applications like URLLC.
Innovation Solution
A fault diagnostics platform that uses fault signatures generated through statistical analysis and testbed experiments to identify root causes of network degradations, employing a combination of offline lab-based and real-time online processes for fault detection and analysis, and leveraging machine learning-based network analytics frameworks for scalable data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sensors are installed in smart homes without proper site survey and network design, then device deployment is simplified and faster, but network connectivity deteriorates with increased retransmissions, power consumption, and delays
Solution Approach 1:
The system performs preliminary network quality assessment and fault detection before data applications are deployed. A fault diagnostics platform continuously monitors network conditions, identifies potential connectivity issues, and alerts users or automated systems to address problems before they critically impact URLLC or other delay-sensitive applications.
Solution Approach 2:
The patent implements a feedback mechanism where network performance data is continuously collected from sensors and IoT devices, analyzed by a fault diagnostics platform, and used to generate insights about network health. This feedback loop enables proactive identification of connectivity degradation patterns and facilitates timely interventions to maintain reliable network operation.
2Reliability
If data applications are designed to withstand multiple layer 2 retransmissions, then application robustness is improved, but network efficiency deteriorates with increased power consumption and delays
Solution Approach 1:
The fault diagnostics platform provides feedback about actual network conditions and retransmission patterns to both application developers and network operators. This enables optimization of application designs to be robust only when necessary, while maintaining high network efficiency through proactive fault detection and resolution that prevents excessive retransmissions.
Solution Approach 2:
The system monitors and analyzes network parameters such as retransmission rates, latency, and packet loss to dynamically assess network health. By tracking these parameters over time, the system can distinguish between temporary degradations and persistent faults, enabling adaptive responses that balance application robustness requirements with overall network efficiency.
3Reliability
If fault diagnostics are implemented in real-time, then network issue detection is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The fault diagnostics system is segmented into modular components: data collection modules at the network edge, a fault diagnostics platform for analysis, and a user interface for reporting. This segmentation allows real-time monitoring capabilities to be distributed across the network infrastructure, reducing the complexity burden on any single component while maintaining comprehensive fault detection capability.
Data Source
AI summary
An approach for diagnosing degradations in performance and malfunctions in sensor networks is disclosed. This approach is based on so-called “fault signatures”. Such fault signatures are generated for known fault conditions through a statistical analysis process that results in each known fault having a unique fault signature. Such unique fault signatures can then point to the root cause of a problem.