IoT Sensor Engagement Profiles for Event Coverage and Energy Saving
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Solution Overview
Problem
Battery-operated IoT devices in remote areas face energy consumption challenges due to continuous charge cycles, which reduce battery capacity over time, necessitating efficient energy management for event coverage.
Innovation Solution
A method and system that utilize engagement profiles to activate only necessary sensors in a sensor network for event coverage, using machine learning to identify anomalies and movement patterns to optimize sensor activation and deactivation, thereby minimizing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensors are continuously activated to ensure complete event coverage, then event detection reliability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts sensor activation states based on real-time event detection needs. Instead of static continuous operation, sensors are activated only when events are detected in their vicinity, creating a dynamic coverage model that adapts to changing conditions while conserving energy.
Solution Approach 2:
The sensor network is segmented into multiple independent sensor nodes, each with its own engagement profile. This allows selective activation of individual sensors or groups based on local event conditions, rather than activating the entire network, thereby reducing overall energy consumption while maintaining coverage reliability.
2Use of energy by moving object
If sensors are activated based on engagement profiles to conserve energy, then energy consumption is reduced, but event detection coverage may be compromised
Solution Approach 1:
The system uses feedback from event detections and anomaly analyses to continuously refine engagement profiles. When events are detected, the system learns from these patterns and adjusts future sensor activation decisions, ensuring that energy-saving measures do not compromise detection coverage while improving efficiency over time.
Solution Approach 2:
Engagement profiles are created in advance based on historical event data and sensor performance. These pre-computed profiles guide sensor activation decisions before events occur, allowing the system to proactively activate only the necessary sensors for expected event scenarios, thus conserving energy while maintaining coverage.
3Productivity
If machine learning algorithms are used to identify anomalies and movement patterns, then sensor activation optimization is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential features from sensor data needed for engagement profile creation and anomaly detection, rather than processing complete raw datasets. This selective extraction reduces computational complexity while maintaining the ability to identify meaningful patterns for sensor activation optimization.
Solution Approach 2:
The machine learning algorithms process only a subset of sensor data that is most relevant for creating engagement profiles, rather than analyzing all available data continuously. This partial processing approach reduces computational burden while still achieving effective sensor activation optimization.
Data Source
AI summary
A method for sensor event coverage and energy conservation includes receiving device sensor data for a plurality of sensors in a sensor network. The method further includes identifying one or more anomalies in the device sensor data that indicate one or more sensors from the plurality of sensors were acquiring data during an event for a specific point in time and identifying movement patterns for the plurality of sensors based on the one or more anomalies. The method further includes responsive to updating base engagement profiles for the plurality of sensors based on the one or more anomalies and the movement patterns, activating based on the updated base engagement profiled, a first sensor from the plurality of sensors.