Edge Computer Continuum for IoT Latency Reduction
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Solution Overview
Problem
Existing IoT systems face challenges with data communication latency, cost, and reliability due to the need for data to travel long distances to and from cloud-based servers, which can result in slow response times and high expenses, especially when dealing with large amounts of data or numerous requests.
Innovation Solution
Implementing an Edge Computer Continuum that utilizes a hierarchy of distributed computing infrastructure, allowing devices to request and respond with data locally through a network of edge computers, reducing the reliance on cloud-based solutions by distributing data storage and processing across multiple layers of computers, such as child, parent, and grandparent nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored and processed in centralized cloud servers, then data security and management are improved, but communication latency and response time increase due to long distances and multiple network hops
Solution Approach 1:
The patent segments the centralized cloud architecture into a distributed edge computing continuum with multiple hierarchical levels (edge devices, local gateways, regional servers, cloud data centers). This segmentation allows data to be processed and stored closer to users at appropriate hierarchical levels, reducing communication distance and latency while maintaining security through distributed architecture.
Solution Approach 2:
The patent introduces a spatial dimension to data storage and processing by creating a continuum that extends from edge devices through local, regional, and national infrastructure to centralized cloud data centers. This multi-dimensional architecture allows data to be accessed from multiple locations simultaneously, reducing latency while maintaining centralized security management.
2Adaptability or versatility
If data is transmitted to and from centralized cloud servers, then centralized control and management are improved, but communication costs increase due to bandwidth consumption over long distances
Solution Approach 1:
The patent segments data traffic into different hierarchical levels based on data type, urgency, and processing requirements. Local edge devices handle routine processing, regional servers manage intermediate data, and only critical or aggregate data is transmitted to centralized cloud data centers, significantly reducing bandwidth consumption and communication costs while maintaining centralized control for important operations.
Solution Approach 2:
The patent implements partial centralized control where only essential data and control signals are transmitted to centralized cloud servers, while local edge computing handles the majority of processing tasks. This partial action approach reduces communication costs while maintaining sufficient centralized oversight for security and coordination.
3Productivity
If a hierarchy of distributed edge computers is implemented, then response time and data processing speed are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal hierarchical architecture where each edge computing node (regardless of specific location or capability) follows the same structural patterns and communication protocols. This universality allows complex distributed processing while maintaining manageable system complexity through standardized interfaces and repeatable architectural patterns across all hierarchical levels.
Solution Approach 2:
The patent introduces intermediary components at each hierarchical level that mediate between lower-level edge devices and higher-level centralized systems. These intermediaries simplify local complexity by providing standardized interfaces and handling protocol translation, making the overall distributed system easier to manage while maintaining high processing speeds.
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.