IoT Sensor Energy Management via Dynamic Scheduling
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Solution Overview
Problem
Existing IoT sensor energy management systems are inadequate in handling random or variable usage patterns, leading to inefficient energy consumption and quality of service (QoS) issues, especially in scenarios with diverse user access times and environmental changes.
Innovation Solution
A method and system for optimizing sensor energy management by collecting and evaluating sensor data to generate operating specifications that predict optimal operational states, using algorithms and rule-based engines to adjust sensor settings based on deviation patterns, environmental monitoring, and continuous learning to minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If fixed time intervals are used for sensor operation based on historical usage patterns, then energy consumption is optimized for predictable patterns, but the system fails to adapt to random or variable request arrivals
Solution Approach 1:
The sensor operation schedule is made dynamic rather than fixed. The system continuously learns from actual request arrival patterns and adjusts the sensor activation schedule in real-time, allowing the operational parameters to adapt to changing usage patterns while maintaining energy efficiency
Solution Approach 2:
The system implements a feedback mechanism where actual sensor request patterns are monitored and fed back into the learning model. This feedback loop enables the system to continuously improve its predictions and adjust sensor operation schedules based on observed deviations from predicted patterns
2Reliability
If sensors are kept active frequently to ensure quality of service, then QoS is maintained, but battery life is reduced
Solution Approach 1:
The system performs preliminary actions by predicting future sensor request patterns using machine learning models. Based on these predictions, it proactively schedules sensor activation in advance, ensuring the sensor is active only when requests are likely to occur, thus maintaining QoS while minimizing unnecessary activations that would drain the battery
Solution Approach 2:
The system dynamically changes operational parameters such as sensor activation time, duration, and frequency based on predicted request patterns. By adjusting these parameters according to learned behaviors, the system optimizes the balance between maintaining service quality and conserving battery energy
3Reliability
If deployment locations are chosen for environmental monitoring, then sensing capability is improved, but battery replacement becomes difficult
Solution Approach 1:
The system provides self-service by using intelligent prediction and optimization algorithms to maximize battery life through efficient scheduling. The sensor operates autonomously based on learned patterns, eliminating the need for manual battery replacement by extending operational duration through smart energy management
Data Source
AI summary
Certain example embodiments relate to Internet of Things (IoT) technology and, more particularly, to techniques for managing energy of at least one sensor. Sensor data resulting from a detection by the sensor(s) is detected. The sensor data includes sensor values specific to a characteristic to be detected by the sensor(s). The collected sensor data is evaluated. The evaluating includes generating an operating specification for the sensor(s) based on a detection of at least one deviation pattern in the sensor values of the collected sensor data that indicates a variation of the characteristic to be detected. The operating specification defines a future activity pattern for the detection by the sensor(s). At least one change of an operational setting for the detection by the sensor(s) is initiated, with the change being initiated based on the generated operating specification.