Edge Computing System Aggregating Outage Data via Self-Locating Nodes
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Solution Overview
Problem
Cloud computing limitations include delayed data processing due to shared resource usage and loss of access when devices go offline, as data is processed and stored centrally, leading to inefficiencies and potential outages.
Innovation Solution
An edge computing system that aggregates data from multiple edge-nodes within a network, enabling self-identifying and self-locating communications between devices, detecting outages, and dispatching assistance through a network of edge-nodes with GPS and communication modules, using technologies like Bluetooth, Wi-Fi, and LORA WAN for continuous communication and data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is processed and stored centrally in the cloud, then processing capabilities and storage capacity are improved, but data access availability and processing speed deteriorate when devices go offline or experience outages
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes. Each edge node (mobile devices, tablets, wearables, IoT devices) maintains local processing capabilities and data storage, dividing the monolithic cloud system into autonomous segments that can operate independently. This segmentation allows data to be processed locally without requiring constant cloud connectivity, thereby maintaining reliability during outages while preserving processing power through distributed computation.
Solution Approach 2:
The patent introduces a new dimensional approach by creating a multi-layered computing architecture that operates across different dimensions: local device dimension (edge nodes), network dimension (ad-hoc mesh network), and cloud dimension. This dimensional expansion allows the system to maintain data access and processing capabilities across multiple layers simultaneously, so when one layer is unavailable (e.g., cloud disconnected), other layers continue to function, thereby improving overall reliability without sacrificing processing power.
2Productivity
If multiple entities share cloud resources simultaneously, then resource utilization efficiency is improved, but data processing wait time increases
Solution Approach 1:
The patent segments the shared resource model into dedicated local resources at each edge node. Instead of multiple entities competing for centralized cloud resources, each entity has its own processing power and storage locally available. This segmentation eliminates the queueing delays inherent in shared resource systems while maintaining high resource utilization through autonomous local processing. The ad-hoc mesh network further segments communication paths, allowing parallel data exchange without centralized bottlenecks.
Solution Approach 2:
The patent implements self-service by enabling edge nodes to autonomously process and manage their own data without requiring centralized cloud coordination. Each device independently executes computations, manages local storage, and communicates directly with other edge nodes through the mesh network. This self-service capability eliminates wait times associated with centralized resource allocation and scheduling, as each entity immediately processes its own data using locally available resources while maintaining high overall system productivity through distributed operation.
3Reliability
If edge nodes are densely distributed within the network, then network coverage and data access continuity are improved, but communication complexity and node management difficulty increase
Solution Approach 1:
The patent merges the functions of multiple densely distributed edge nodes into cohesive functional groups through the ad-hoc mesh network. Rather than managing each node independently, the system combines their capabilities into a unified distributed network where nodes cooperate through standardized communication protocols. This merging approach maintains the reliability benefits of dense distribution (improved coverage and continuity) while reducing management complexity through collective operation and automated peer-to-peer coordination, eliminating the need for centralized node management.
Data Source
AI summary
A method for aggregating outage data via an edge computing system is provided. The method may include transmitting, by each edge-node included in a network, a self-identifying, self-locating, communication. The method may include receiving, at each edge-node included in the network, self-identifying, self-locating, communications from one or more edge-nodes included in the network. The method may include detecting an outage relating to a first edge-node included in the network. The outage may be detected by at least one edge-node included in the network. The at least one edge-node may not be the first edge-node. The at least one edge-node may be the first edge-node operating in limited capacity. The method may include identifying a most-recently confirmed location of an entity co-located with the first edge-node. The method may include dispatching outage-restoration-assistance to the most-recently confirmed real-time location of the entity co-located with the first edge-node.