Dynamic Edge Computing Device Arrangement for Context-Aware Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing edge computing systems lack the ability to dynamically determine and adjust the arrangement of edge computing devices to meet the specific needs of users in various environments, leading to inefficiencies in resource utilization and performance.
Innovation Solution
A processor analyzes user data and edge computing data to predict edge computing needs based on context and location, determining the optimal arrangement of edge computing devices, including their location, orientation, and operational capacity, using artificial intelligence and IoT devices that can move to ensure adequate resources are available where needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If edge computing devices are statically arranged, then device complexity is reduced, but adaptability to user needs and locations deteriorates
Solution Approach 1:
The patent implements dynamic arrangement of edge computing devices by enabling them to move autonomously or be repositioned based on real-time user location and computing needs. The system continuously monitors user context and adjusts device positions accordingly, transforming a static infrastructure into a dynamic, adaptive network that optimizes resource delivery without requiring complex manual configuration
Solution Approach 2:
Edge computing devices are equipped with autonomous capabilities to self-position and self-configure based on environmental sensors and user data. The devices can independently determine their optimal locations and operational parameters without requiring complex external control systems, thereby achieving adaptability while maintaining relatively simple system architecture
2Adaptability or versatility
If edge computing devices are dynamically repositioned, then adaptability to user needs improves, but device complexity increases
Solution Approach 1:
The patent employs universal control mechanisms that can manage multiple edge computing devices with diverse functions through a common framework. By creating a multi-functional system where a single control architecture can handle positioning, resource allocation, and coordination of various device types, the patent reduces overall system complexity while maintaining high adaptability
Solution Approach 2:
The system achieves dynamic adaptation by changing operational parameters such as device position, orientation, and resource allocation levels based on user context. Rather than requiring complex structural modifications, the patent utilizes parameter adjustments to optimize performance, thereby improving adaptability while keeping device complexity manageable
3Productivity
If AI models are used to predict user location and needs, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent implements partial AI processing by using lightweight machine learning models for basic predictions and reserving full AI capabilities for complex decision-making scenarios. This approach achieves sufficient productivity improvement for resource allocation while significantly reducing energy consumption compared to continuous full-scale AI analysis
Solution Approach 2:
The system performs preliminary analysis using simplified algorithms to identify basic user patterns and predict near-term needs. By handling routine predictions with low-energy methods and only invoking full AI models when necessary, the patent improves resource allocation efficiency while maintaining acceptable energy consumption levels
Data Source
AI summary
A processor may receive user data associated with one or more locations of a user in an environment. The processor may receive edge computing data associated with utilization of edge computing resources by the user. The processor may analyze the edge computing data to associate a context with an edge computing resource need. The processor may analyze the user data to associate a context with a location of the user within the environment. The processor may determine a first location of the user in the environment at a first time. The processor may predict a first edge computing need of the user in the first location. The processor may determine an arrangement of one or more edge computing devices configured to meet the first edge computing need of the user at the first time.