Edge Computer Continuum for IoT Latency and Reliability
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Solution Overview
Problem
Current IoT systems face challenges with data synchronization and processing due to reliance on remote cloud storage, leading to increased costs, latency, and reliability issues, especially in scenarios requiring real-time data access and offline support.
Innovation Solution
Implementing an Edge Computer Continuum that distributes computing infrastructure across multiple layers of devices, allowing devices to request data from a single known source and receive responses from a hierarchy of distributed servers, reducing reliance on cloud connectivity and optimizing data storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If applications are run on remote cloud servers, then data storage capacity and security are improved, but communication cost and latency increase
Solution Approach 1:
The system segments the centralized cloud architecture into a hierarchical edge computing structure with multiple layers (edge devices, edge servers, cloud). Data and applications are distributed across these segments, allowing local processing at the edge while maintaining secure cloud storage for sensitive data, thus reducing latency for time-critical operations while preserving security.
Solution Approach 2:
Edge servers and edge devices act as intermediaries between end-user devices and the central cloud. These intermediaries cache data and run applications locally, reducing the need for direct cloud communication and thereby decreasing latency while maintaining the security benefits of cloud-based data storage.
2Reliability
If data is stored and processed in centralized cloud, then data security is improved, but communication cost and internet dependency increase
Solution Approach 1:
The centralized cloud system is segmented into a distributed edge computing hierarchy. Non-sensitive data and applications are processed locally at edge devices and servers, reducing the volume of data that needs to be transmitted to the cloud and thereby lowering communication costs and energy consumption.
Solution Approach 2:
Edge devices and servers are equipped with local processing capabilities that allow them to autonomously handle data processing and application execution without constant cloud connectivity. This self-service capability reduces dependency on internet connections and minimizes communication costs while sensitive data remains secured in the cloud.
3Adaptability or versatility
If multiple hops are used to connect user to cloud server, then system scalability is improved, but connection reliability and response time worsen
Solution Approach 1:
The single-hop cloud connection model is segmented into a multi-layer hierarchical structure with edge devices, edge servers, and cloud servers. This segmentation creates multiple direct connection paths closer to users, reducing the number of hops required for data transmission and improving connection reliability and response time while maintaining system scalability.
4Device complexity
If applications run remotely in cloud, then device complexity is reduced, but offline functionality and reliability worsen
Solution Approach 1:
Edge servers act as intermediaries that host applications and data locally in proximity to end devices. These intermediaries enable devices to access applications and data offline without requiring constant cloud connectivity, improving reliability while keeping device complexity low as devices can rely on the edge infrastructure for processing.
Solution Approach 2:
The system segments functionality between cloud, edge servers, and end devices. Edge servers handle application hosting and data processing, allowing simple devices to operate offline by accessing locally cached applications and data from edge servers, thus maintaining device simplicity while enabling offline functionality.
Data Source
AI summary
The present invention relates to IoT devices existing in a deployed ecosystem. The various computers in the deployed ecosystem are able to respond to requests from a device directly associated with it in a particular hierarchy, or it may seek a response to the request from a high order logic/data source (parent). The logic/data source parent may then repeat the understanding process to either provide the necessary response to the logic/data source child who then replies to the device or it will again ask a parent logic/data sources for the appropriate response. This architecture allows for a single device to make one request to a single known source and potentially get a response back from the entire ecosystem of distributed servers.