IoT State Machine Engine Context-Aware Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current IoT devices lack full automation and adaptability, requiring manual user or manufacturer input to initiate actions and transition between states, as they are not intuitively intelligent to adapt to different scenarios without external intervention.
Innovation Solution
A state machine engine that models the behavior of multiple IoT devices within an environment, using contextual information such as temporal and spatial attributes, and crowdsourcing data to automate transitions between states, providing an adaptive and context-aware framework for IoT device management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual user or manufacturer input is used to control IoT devices, then device behavior can be programmed and controlled, but the devices lack full automation and adaptability to different scenarios
Solution Approach 1:
The system enables IoT devices to automatically determine and transition between states using contextual information from sensors and environmental data, eliminating the need for continuous manual user input or pre-programmed manufacturer behavior. The state machine engine autonomously processes contextual attributes (location, time, activity) to drive device state transitions, making the system self-acting and adaptive to different scenarios without external intervention.
2Adaptability or versatility
If contextual information and crowdsourcing data are processed to predict state transitions, then adaptability and intelligence of IoT devices are enhanced, but system complexity increases
Solution Approach 1:
The state machine engine is segmented into distinct functional components: contextual information processing module, state determination module, and state transition execution module. Each component handles a specific aspect of the overall function, processing contextual attributes separately and combining them to determine device states. This modular segmentation reduces system complexity by breaking down the complex task of state prediction into manageable, independent processing stages.
3Measurement precision
If multiple contextual attributes are analyzed to determine device states, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
Contextual attributes (spatial location, temporal time, activity state) are continuously pre-processed and stored in ready-to-use formats by sensor modules and data collection systems. The state machine engine receives pre-processed contextual information that has already been filtered and organized, eliminating the need for real-time complex analysis during state determination. This preliminary processing of contextual data maintains high prediction accuracy while significantly reducing the computational time required for state transitions.
Data Source
AI summary
Examples herein relate to determining a next state in which to transition multiple IoT devices within an environment. Examples disclose determining, via operation of a state machine, a current state of the multiple IoT devices within the environment. The state machine receives contextual information. Based on the current state and the contextual information, the state machine determine a next state of the multiple IoT devices in which to transition of the multiple IoT devices within the environment.