Remote Node Management Engine for Wearable Computing
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Solution Overview
Problem
Existing technologies face challenges in integrating multiple devices in real-time to provide context-aware information without explicit user input, often resulting in latency and inefficiency, particularly in computationally intense tasks.
Innovation Solution
A context-aware platform (CAP) that supports remote management of computing nodes, leveraging networked wearable devices and access points to distribute computational tasks and minimize latency, by using a remote node management engine to select and manage computing nodes in proximity to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate tools are used to achieve desired goals under changing conditions, then flexibility and adaptability are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent implements a universal management system that can manage multiple types of computing nodes (cloud-based, edge-based, wearable devices) through a single unified interface and control mechanism. The system uses standardized protocols and abstraction layers to handle diverse tools and devices, allowing them to be managed collectively rather than requiring separate management approaches for each device type.
Solution Approach 2:
The patent introduces an intermediary management layer that sits between the user and the disparate computing tools. This intermediary system provides a unified interface, handles device discovery, and manages communication protocols, thereby reducing the complexity of directly integrating multiple disparate tools while maintaining their individual functionalities.
2Productivity
If computational tasks are distributed to remote computing nodes, then processing capacity and scalability are improved, but communication latency and coordination overhead increase
Solution Approach 1:
The patent implements local quality by allowing different computing nodes to operate with different levels of autonomy and processing capabilities based on their local conditions. Edge computing nodes process tasks locally when possible, reducing the need for constant communication with central nodes. The system adapts the level of local processing versus remote coordination based on task requirements and network conditions.
Solution Approach 2:
The patent applies preliminary action by pre-establishing communication channels, pre-configuring task distribution policies, and pre-synchronizing data caches between computing nodes. This preparation work is done in advance to minimize communication latency during actual task execution, allowing the distributed system to operate more efficiently when tasks are assigned.
3Productivity
If computing nodes are selected and managed dynamically, then resource utilization and efficiency are improved, but control complexity and management overhead increase
Solution Approach 1:
The patent implements self-service by enabling computing nodes to automatically register themselves with the management system, report their status and capabilities, and receive task assignments without manual intervention. The nodes autonomously manage their own resource allocation and can self-adjust based on changing conditions, reducing the complexity of centralized management while maintaining high resource utilization.
Solution Approach 2:
The patent employs feedback mechanisms where computing nodes continuously report their status, performance metrics, and resource availability to the management system. The management system uses this feedback to dynamically adjust task distribution and node selection, optimizing resource utilization automatically. This closed-loop control reduces management complexity by using automated decision-making based on real-time feedback rather than manual configuration.
Data Source
AI summary
In examples provided herein, upon receiving notification of a computational task requested by a package to provide an experience to a user, a remote node management engine identifies computing nodes for performing the computational task and determining available processing resources for each computing node, where a computing node resides at networked wearable devices associated with the user. The remote node management engine further selects one of the computing nodes as a primary controller to distribute portions of the computational task to one or more of the other computing nodes and receive results from performance of the portions of the computational task by the other computing nodes, and provides to the selected computing node information about available processing resources at each computing node.