Dynamic IoT Action Chains via Dependency Graphs
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Solution Overview
Problem
Existing IoT command architectures are poorly equipped to handle complex, multiple-step, and dynamic sequences of actions across various IoT devices, leading to inefficiencies and potential mistakes in process pipelines.
Innovation Solution
The implementation of a dynamic chain of actions system that ties sequences of operations across multiple IoT devices, utilizing an IoT dependency graph to specify workflows and automate processes, allowing for controlled and parallel execution of actions based on event triggers and device capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual commands are sent to IoT devices, then device control is simple, but complex or dynamic sequences of actions cannot be handled effectively
Solution Approach 1:
The patent segments complex actions into atomic actions that can be independently defined and composed. Each action is a discrete unit with specific parameters, allowing complex sequences to be built from simple, manageable segments. This enables the system to handle complex workflows while maintaining operational simplicity through standardized atomic units.
Solution Approach 2:
The patent introduces dynamic action chains where the sequence of actions can be automatically adjusted based on event conditions. The system transitions from static, pre-defined command sequences to dynamic, condition-based action chains that adapt in real-time. This allows the system to handle complex scenarios while maintaining ease of operation through automated adaptation.
2Ease of manufacture
If fixed command sequences are used, then implementation is straightforward, but dynamic adjustments based on events cannot be made
Solution Approach 1:
The patent defines action chains in advance with predetermined sequences and conditions. The system prepares multiple action sequences that can be triggered based on events, allowing dynamic responses to be implemented without requiring real-time decision-making complexity. This maintains ease of implementation while enabling dynamic adaptability.
Solution Approach 2:
The patent incorporates event-driven feedback mechanisms where the system monitors device states and automatically adjusts action chains based on observed conditions. This feedback loop enables dynamic adjustments to fixed sequences, allowing the system to adapt to changing conditions while maintaining straightforward implementation through automated responses.
3Reliability
If multiple-step sequences are implemented, then comprehensive process control is achieved, but system complexity increases
Solution Approach 1:
The patent divides comprehensive process control into discrete atomic actions, each with clear definitions and parameters. This segmentation reduces the cognitive complexity of managing multi-step sequences while maintaining comprehensive control. Each atomic action is independently manageable, making the overall system more reliable yet less complex.
Solution Approach 2:
The patent introduces an action chain management layer that acts as an intermediary between high-level process requirements and low-level device commands. This intermediary layer abstracts the complexity of coordinating multiple steps, providing a simplified interface for process control while managing the underlying complexity of sequence coordination across multiple devices.
4Extent of automation
If automated action chains are implemented, then process automation is improved, but error prevention requires additional control mechanisms
Solution Approach 1:
The patent incorporates feedback mechanisms at each action step where the system verifies completion and conditions before proceeding to the next action. This feedback loop automatically prevents errors by validating assumptions and stopping execution when conditions are not met, maintaining high automation while ensuring reliability through automated verification.
Solution Approach 2:
The patent includes error handling and validation mechanisms built into the action chain definitions before execution occurs. By defining expected conditions and validation rules in advance, the system prepares cushioning mechanisms that prevent errors from propagating through the automation chain, maintaining both automation and reliability.
Data Source
AI summary
Various examples are disclosed for performing dynamic chains of action for Internet-of-Things (IoT) devices. In some aspects, IoT event data is received from an IoT device. An IoT dependency graph is identified based on an initial IoT event specified by the IoT dependency graph. An IoT event subsequent to the initial IoT event is identified according to the IoT dependency graph. A command is transmitted to an IoT device to perform the IoT event.