Utility Usage Profiling for Abnormal Network Action Triggers
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Solution Overview
Problem
Existing systems lack effective methods to monitor and manage utility usage anomalies in network devices, leading to potential inefficiencies and unauthorized usage, as they fail to dynamically adapt to changes in consumption patterns.
Innovation Solution
A method involving network devices that compile historical and current usage data to generate profiles, compare them for abnormalities, and trigger actions such as deactivation or notifications based on predefined thresholds, allowing for real-time management and optimization of utility usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional utility monitoring systems are used, then basic usage tracking is maintained, but abnormal usage patterns cannot be detected and responded to
Solution Approach 1:
The system performs preliminary actions by compiling historical usage data and generating baseline profiles before actual usage monitoring begins. This pre-processing establishes reference patterns that enable subsequent detection of abnormal usage without requiring complex real-time analysis algorithms.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing current usage profiles against historical baselines and automatically triggering actions when abnormalities are detected. This closed-loop approach improves detection accuracy while maintaining manageable system complexity through automated decision-making rules.
2Productivity
If real-time usage monitoring is implemented, then abnormal usage can be detected, but system complexity and computational requirements increase
Solution Approach 1:
The monitoring system segments usage analysis into distinct phases: historical data compilation, baseline profile generation, current usage measurement, and abnormality detection. This segmentation allows each component to be optimized independently, improving overall monitoring efficiency while keeping individual processing tasks manageable.
Solution Approach 2:
The system applies partial action by focusing computational resources only on detecting deviations from established baselines rather than analyzing every usage data point in detail. This approach maintains high monitoring efficiency while reducing processing complexity by acting only when necessary.
3Reliability
If usage thresholds are set strictly, then misuse prevention is improved, but false positives and unnecessary deactivations increase
Solution Approach 1:
The system dynamically adjusts detection parameters by comparing current usage against historically-derived baselines rather than using fixed thresholds. This allows the system to adapt to legitimate usage variations while maintaining sensitivity to actual misuse, improving detection accuracy without increasing false positives.
Solution Approach 2:
The monitoring system transitions from static threshold-based detection to dynamic baseline comparison, where acceptable usage ranges evolve with historical data. This dynamic approach reduces false positives by accommodating legitimate usage changes while maintaining reliable misuse detection through continuous adaptation.
Data Source
AI summary
The present disclosure relates to using sensors and measurements from sensors to trigger actions within a network. Specifically, various techniques and systems are provided for measuring usage or measurements, using sensors, of utilities or other environmental factors, generating profiles based on the usage or measurements, and triggering actions within a network device based on the usage, measurements and profiles. Embodiments of the present invention may include, for example, compiling historical usage based on the use or measurements detected by a network device and generating a usage profile based on that use or measurements. The normal usage profile may be compared with the usage over a certain predetermined period of time to detect any abnormal use or measurements from the network device, and an action may be taken as a result of an abnormality.