State-Machine Pattern Matching for Low-Latency Industrial IoT
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Solution Overview
Problem
Existing cloud-based computing systems for industrial IoT face challenges with connectivity issues, high latency, bandwidth limitations, and cost-prohibitive data transmission, making real-time decision-making and predictive maintenance impractical for industrial machines.
Innovation Solution
An efficient state-machine-based pattern matching technique processes tokens in an input queue without backtracking, enabling real-time dataflow programming at the edge of the network, using a state stack and state table to identify patterns in sensor data streams, and integrating edge intelligence with local analytics and cloud management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is transmitted to cloud storage, then data can be stored and analyzed, but bandwidth requirements increase, latency increases, and costs become prohibitive
Solution Approach 1:
The patent segments the data processing function by deploying state machines at edge devices (segment 1) and cloud data centers (segment 2). Edge state machines perform local pattern matching on sensor data, while cloud state machines handle aggregated results or fallback processing. This segmentation allows critical real-time decisions to be made locally without transmitting all raw sensor data to the cloud, reducing bandwidth consumption while maintaining data storage and analysis capabilities.
2Loss of time
If data is processed in real-time at the edge, then latency is reduced and immediate responses are enabled, but device complexity increases
Solution Approach 1:
The patent implements self-service by enabling edge devices to autonomously execute state machines and perform pattern matching on sensor data locally. The state machines are designed to be self-contained with transition tables and state stacks that operate independently, allowing edge devices to make real-time decisions without constant cloud intervention. This autonomy reduces latency while the modular state machine architecture keeps device complexity manageable through standardized, reusable components.
3Productivity
If cloud infrastructure is used for economies of scale, then system efficiency improves, but connectivity requirements increase and real-time decision-making is impaired
Solution Approach 1:
The patent applies dynamics by making the data processing architecture adaptive and flexible. State machines can dynamically switch between operating modes: edge-only mode for immediate real-time decisions, cloud-assisted mode for non-critical analysis, or hybrid mode combining both. The system dynamically allocates processing tasks based on connectivity availability, data criticality, and computational requirements, allowing it to leverage cloud infrastructure efficiency when available while maintaining real-time decision-making capability when needed.
Data Source
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AI summary
An efficient state-machine-based pattern matching technique processes tokens in an input queue and identifies patterns in the sequence of tokens that match one or more predetermined input patterns without backtracking. Tokens can include data or no data and a time component. The tokens can be a stream of data generated by a sensor, which transforms a physical property into a digital quantity. The pattern matching technique processes the input queue in a single direction, and does not examine any previously examined token. In an implementation, specific patterns to be matched are specified using a state machine, where the state machine is specified in a state table and operates using a state stack.