Wi-Fi Access Point Anomaly Detection via Temporal Spatial Analysis
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Solution Overview
Problem
Large-scale Wi-Fi networks face challenges in accurately detecting anomalies due to high false positive rates and delayed detection, leading to increased costs and reduced communication performance.
Innovation Solution
A computer system that monitors access point performance metrics across multiple temporal and spatial contexts, generates anomaly events, and performs remedial actions based on insights derived from these events to identify and address network issues effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anomaly detection techniques are applied to large-scale Wi-Fi networks, then network performance monitoring capability is improved, but false positive rate increases
Solution Approach 1:
The patent segments anomaly detection into two distinct types: temporal anomalies (comparing current metrics with historical data from the same access point) and spatial anomalies (comparing current metrics with data from similar access points). This segmentation allows each detection mechanism to focus on specific patterns, reducing false positives caused by general-purpose detection algorithms.
Solution Approach 2:
The patent dynamically adjusts detection thresholds and parameters based on historical data patterns, network conditions, and the specific type of anomaly being detected. By adapting parameters rather than using fixed thresholds, the system maintains high detection accuracy while minimizing false alarms in large-scale networks.
2Measurement precision
If comprehensive anomaly detection is performed across all access points, then detection coverage is improved, but detection time increases
Solution Approach 1:
The patent divides the detection process into parallel temporal and spatial analysis streams that can be processed simultaneously. Temporal analysis handles historical comparisons for each access point independently, while spatial analysis groups access points by similarity for batch processing, enabling comprehensive coverage without sequential delays.
Solution Approach 2:
The patent implements a two-stage detection approach where a quick initial assessment identifies potential anomalies, followed by more detailed analysis only for suspicious cases. This partial action strategy maintains comprehensive monitoring coverage while reducing overall processing time by avoiding exhaustive analysis of all data points continuously.
3Measurement precision
If multiple performance metrics are monitored simultaneously, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple performance metrics (signal strength, client count, throughput, error rates) into unified temporal and spatial anomaly detection frameworks. By combining metrics within the same analytical structure rather than processing them separately, the system achieves comprehensive monitoring while reducing the complexity of managing multiple independent detection systems.
Solution Approach 2:
The patent creates a universal anomaly detection platform that handles multiple metric types through common temporal and spatial analysis mechanisms. The same detection engine processes different metrics by comparing them against historical patterns and peer group behaviors, eliminating the need for metric-specific detection algorithms and reducing overall system complexity.
Data Source
AI summary
During operation, a computer may compare values of at least one performance metric for access points in appropriate contexts to determine one or more temporal anomalies and/or one or more spatial anomalies for one or more of the access points. Then, the computer may generate one or more temporal anomaly events based at least in part on the one or more temporal anomalies and one or more spatial anomaly events based at least in part on the one or more spatial anomalies. Next, the computer may calculate one or more complex events based at least in part on two or more of the different anomalies. Moreover, the computer may evaluate the different anomalies, anomaly event and/or complex events to determine one or more insights about a problem in the network. Furthermore, the computer may perform a remedial action.