IoT Device Control via Probabilistic State Learning
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Solution Overview
Problem
Network connected devices, such as IoT devices, lack the capability for full autonomy and often require manual operation or rule-based control, and their limited processing power hinders their ability to perform complex autonomous decisions, necessitating a method to automate their functionality in a generalizable and efficient manner.
Innovation Solution
A system that utilizes machine learning to automatically control network devices by receiving inputs from various sensors, determining states with associated probabilities, and triggering rules to modify device behavior based on detected states, integrating inputs from multiple devices to build user preference models and autonomously adjust device settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual operation or rule-based control is used for IoT devices, then ease of operation is maintained, but extent of automation is insufficient
Solution Approach 1:
The system performs autonomous operations by automatically learning user preferences and behaviors through machine learning, then autonomously controlling IoT devices based on learned patterns without requiring manual rule creation or continuous user intervention
Solution Approach 2:
The system continuously monitors device states and user interactions, uses this feedback to refine machine learning models, and automatically adjusts device control strategies to improve automation while maintaining user preferences
2Extent of automation
If IoT devices perform autonomous decisions locally, then responsiveness is improved, but device complexity increases beyond available processing power
Solution Approach 1:
The system divides processing tasks between lightweight edge devices and centralized cloud infrastructure, allowing autonomous decision-making functionality to be distributed without overburdening individual IoT devices with complex processing requirements
Solution Approach 2:
A centralized learning system acts as an intermediary that performs complex machine learning computations and generates control policies, which are then deployed to simpler IoT devices for execution, separating the computational burden from the edge devices
3Adaptability or versatility
If single-device autonomous operation is implemented, then device functionality is improved, but overall system functionality is limited
Solution Approach 1:
The system combines data and functionality across multiple different types of IoT devices to create comprehensive user preference models, enabling coordinated automation that leverages information from sensors, actuators, and various device types to achieve system-wide automation capabilities
Data Source
AI summary
A first input is received from a plurality of sensors. A first state including a first location based on the first input is determined. The first state is associated with a first probability. A second input is received from the plurality of sensors. A second state including a second location is determined based on the second input associated with a second probability. It is determined that the second state corresponds to an actual state based on a transition model and the second probability. The transition model associates the first state with the second state and indicates a likelihood of a transition from the first state to the second state. A rule to change a state of at least one network connected device is triggered based on the second state.