Edge Computing Interpreting Software for Dynamic IoT Query Execution
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Solution Overview
Problem
Conventional IoT systems face limitations in flexibility, performance, scalability, and usability due to static designs, limited hardware resources, and the need for deep technical knowledge for deployment, development, and maintenance, especially when dealing with legacy hardware and real-time data processing.
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 dynamic data processing, calculation, and communication across a network, and an interactive GUI for visual query definition and management, enabling flexible functionality and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static IoT systems are used, then system stability is maintained, but flexibility and adaptability of functionality deteriorate
Solution Approach 1:
The system transitions from static configuration to dynamic reconfigurability through the ability to load, unload, and switch software modules at runtime. Edge devices can dynamically adapt their functionality by loading new software modules that modify their behavior, allowing the system to evolve without physical hardware changes while maintaining operational stability through controlled module activation.
Solution Approach 2:
Edge devices are designed with universal capabilities to execute multiple different software modules, allowing a single hardware platform to perform diverse functions. The interpreting software function can load different modules to provide various functionalities such as data collection, processing, and communication, making the system adaptable to multiple use cases without requiring dedicated hardware for each function.
2Productivity
If more hardware resources are allocated at each edge device, then real-time data processing performance improves, but device complexity and cost increase
Solution Approach 1:
The system segments computational tasks between edge devices and central servers. Edge devices handle local real-time processing of sensor data using lightweight interpreting software, while more complex analytics and data aggregation are performed centrally. This segmentation allows edge devices to maintain simple hardware while still achieving real-time processing for critical functions.
Solution Approach 2:
A communication protocol acts as an intermediary between edge devices and central servers, enabling efficient data exchange. The protocol allows edge devices to send processed results and receive configuration updates without requiring high-bandwidth connections, reducing the need for complex communication hardware at the edge while maintaining real-time processing capabilities.
3Ease of operation
If specialized programming knowledge is required for deployment, then system functionality can be precisely controlled, but ease of operation and usability deteriorate
Solution Approach 1:
The system uses template-based software modules that can be copied and deployed across multiple edge devices. Pre-configured modules provide common functionalities that can be instantiated without customization, allowing non-programmers to deploy systems by simply copying and configuring parameters of existing modules rather than writing code from scratch.
Solution Approach 2:
The interpreting software function provides self-service capabilities by automatically parsing configuration files and generating executable code from high-level descriptions. Users can define system behavior using simple configuration parameters, and the system automatically generates the necessary software configurations, eliminating the need for users to write or understand complex programming code.
4Speed
If data is processed and stored locally at each edge device, then real-time response is achieved, but memory requirements and data management complexity increase
Solution Approach 1:
The system extracts and removes processed data from edge device memory after it has been used for real-time processing. Results are transmitted to central servers for long-term storage, allowing edge devices to maintain small local memory buffers while still achieving real-time processing. This extraction approach separates temporary working memory needs from permanent storage requirements.
Data Source
AI summary
System comprising several edge computing devices (ECDs), each equipped with a sensor, a memory, a Central Processing Unit (CPU), and a digital communication interface, enabling the ECD to communicate across a digital communication network. Each ECD is equipped with an interpreting software function, designed to execute on the CPU and interpret computer code received via the digital communication interface and stored in the memory. This is based on a query language with a predetermined syntax that defines queries sent from a particular requesting ECD to one or multiple responding ECDs. The system also includes an interactive Graphical User Interface (GUI) that allows a user to visually view and interactively change computer code stored in various ECDs using a visual notation system. The GUI communicates updated computer code to any concerned ECDs reflecting changes made by the user and receives computer code stored in the ECDs to provide a visual view.