Edge Computing Query Interpreter for Flexible IoT Data Processing
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Solution Overview
Problem
Conventional IoT systems face limitations in flexibility during design, deployment, and maintenance, particularly with existing and legacy hardware, and struggle with performance and scalability when handling large amounts of data in real-time, requiring deep technical knowledge for operation and development.
Innovation Solution
A system comprising edge computing devices with interpreting software functions that execute computer code in a query language with a predetermined syntax, allowing for flexible functionality, data processing, and communication across a network, enabling edge devices to perform calculations based on sensor measurements and communicate results, with an interactive GUI for visual query management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional IoT systems use fixed functionality designed during initial deployment, then system stability is maintained, but flexibility and adaptability during design, deployment, and maintenance are reduced
Solution Approach 1:
The system transitions from static functionality to dynamic functionality by allowing edge devices to interpret and execute queries at runtime. The query language enables functionality to be modified and adapted without changing the underlying hardware or software architecture, making the system dynamically configurable during deployment and maintenance phases.
Solution Approach 2:
A query language serves as an intermediary layer between the user and the edge device functionality. This intermediary enables flexible functionality specification without requiring users to understand complex technical details, while the edge device interpreter translates high-level queries into executable operations.
2Productivity
If edge devices process large amounts of measured data in real-time, then measurement precision and response time are improved, but available hardware resources at each edge component are exceeded
Solution Approach 1:
The system extracts heavy computational processing from edge devices and relocates it to centralized servers. Edge devices only perform lightweight query interpretation and data collection, while complex data processing, analysis, and storage are performed centrally, reducing hardware resource consumption at the edge while maintaining high productivity.
Solution Approach 2:
The system segments processing tasks between edge devices and centralized servers. Edge devices handle local data collection and basic query execution, while centralized servers handle comprehensive data processing, enabling the system to scale without overloading individual edge components.
3Ease of manufacture
If the system requires deep knowledge in programming and technical details for deployment and maintenance, then manufacturing precision and control are improved, but ease of operation and usability are reduced
Solution Approach 1:
The query language acts as an intermediary that abstracts complex technical details from users. Users can specify functionality and configure systems using high-level query expressions without needing deep programming knowledge, while the interpreter ensures precise control and accurate execution of configured operations.
Solution Approach 2:
The system uses query language specifications as abstract copies or representations of actual device configurations and operations. Users work with these high-level representations rather than direct hardware controls, making deployment and maintenance easier while maintaining precise control through the interpretation layer.
Data Source
AI summary
A system includes several edge computing devices. Each edge computing device includes a a sensor, a memory, a central processing unit (CPU), and a digital communication interface, for communication across a network. Each edge computing device has a respective interpreting software function, arranged to execute on the CPU and to interpret computer code, received via the digital communication interface and stored in the memory, according to a query language having a predetermined syntax. The syntax defines queries the results of which are streams of data. A first software function poses a first query to a second edge computing device. A second software function generates and communicates, in response thereto, a second stream of data. The first software function preprocesses the second stream so that it adheres to a predefined global data ontology, and performs a first calculation using the second stream.