Dynamic Power Management of Sensors and Devices Using Predicted Behaviors, Contexts, Triggers, Associated Risks, Machine Learning, and/or Artificial Intelligence (AI)

A computer-implemented method for context-based sensor power management in a computing device, the method comprising:

• determining a current context state and a predicted future context state of a user associated with the computing device based on sensor data from one or more active sensors;
• for each sensor in a set of available sensors, computing an importance score indicative of the sensor's relevance to determining the current context state and the predicted future context state, wherein the importance score is derived from contextual attributes including a plurality of temporal, locational, social, activity, and behavioral factors;
• estimating a power consumption requirement cost for each sensor at one or more operational modes, including varying sampling rates or activation states;
• predicting a recharge likelihood for the computing device within a predefined and/or dynamically determined time horizon based on historical user behavior patterns, current location, and temporal data;
• adjusting an effective available power budget based on a current battery level and the predicted recharge likelihood;
• retrieving a historical utility value for each sensor from a database, the historical utility value representing the sensor's effectiveness in similar past context states;
• calculating a utility metric for each sensor as a function of the importance score, the historical utility value, and the normalized power consumption cost;
• optimizing sensor usage by selecting operational modes for each sensor to maximize an aggregate utility metric while constraining total power consumption within the adjusted power budget and using an optimization technique;
• applying the selected operational modes to control activation, deactivation, or sampling rate adjustments of the sensors;
• and
• updating the historical utility database with feedback on sensor performance in the current context state.

A system for context-based sensor power management in a computing device, comprising a processor and memory configured to execute instructions for:

• a context determination component that identifies current and predicted future context states;
• a sensor evaluation component that computes importance scores, power costs, historical utilities, and utility metrics for each sensor;
• a recharge prediction component that estimates recharge likelihood and adjusts power budgets;
• an optimization component that selects sensor operational modes to maximize utility under power constraints;
• and
• a control component that applies the selected modes and updates historical data.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for context-based sensor power management, the operations comprising:

• identifying, for each sensor, a contribution metric representing the extent to which the sensor aids in determining a temporal contextual element (“when”) and at least one other element selected from locational (“where”), social (“who”), activity (“what”), executional (“how”), or motivational (“why”);
• assessing an importance metric for each involved contextual element based on its role in current and predicted future context inference;
• computing a power adjustment factor as a function of the contribution metric and importance metric;
• altering the power consumption of the sensor in accordance with the power adjustment factor;
• and
• periodically recalibrating the metrics using feedback from context inference outcomes to refine future power management decisions.