Intelligent Edge Devices Dynamic Ad Hoc Network Resource Management
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Solution Overview
Problem
The increasing volume of data collected by IoT devices overloads processing and storage infrastructure, causes network bandwidth congestion, and leads to incompatibility among devices due to lack of standardization, resulting in delays in data processing and analysis.
Innovation Solution
A system of intelligent edge devices that form a dynamic, ad-hoc network for data processing and analytics, allowing devices to share compute, communication, and storage resources without relying on cloud access, using a Smart Edge Framework that includes a switchboard for resource management and a distribution layer for advanced analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collected and processed using cloud-based infrastructure, then data processing capacity increases, but network bandwidth congestion and processing delays increase
Solution Approach 1:
The patent segments the centralized cloud processing architecture into distributed edge computing nodes. Each edge device or gateway independently processes data locally, dividing the overall processing workload across multiple decentralized units. This segmentation eliminates the single bottleneck of cloud infrastructure and enables parallel processing, thereby increasing overall data processing capacity while reducing network bandwidth consumption and processing delays.
Solution Approach 2:
The patent introduces a new spatial dimension for data processing by deploying compute resources at the network edge rather than concentrating them in centralized cloud data centers. This dimensional shift from centralized to distributed processing architecture allows data to be processed closer to its source, reducing the distance data must travel over the network and thereby decreasing latency and bandwidth congestion while maintaining or enhancing processing capacity.
2Quantity of substance
If more sensors and devices are deployed to collect data, then data collection capability improves, but infrastructure overload and network congestion worsen
Solution Approach 1:
The patent extracts processing and storage functions from the centralized cloud infrastructure and places them directly on edge devices and gateways. By taking out compute and storage capabilities from the overloaded central infrastructure and distributing them to local devices, the system can accommodate increased data collection from additional sensors without proportionally increasing central infrastructure complexity or network bandwidth requirements.
Solution Approach 2:
The patent enables edge devices to perform self-service data processing, filtering, and preliminary analytics locally before transmitting processed information to the cloud. Each device independently manages its own data processing needs, reducing the burden on central infrastructure. This self-service capability allows the system to scale data collection across numerous sensors without linearly increasing infrastructure complexity, as local devices handle their own processing loads.
3Quantity of substance
If cloud storage is used to collect large amounts of data, then data storage capacity increases, but processing delays increase
Solution Approach 1:
The patent implements preliminary data processing, filtering, and aggregation at edge devices and gateways before data is transmitted to cloud storage. By performing preliminary actions locally—such as filtering out redundant data, aggregating sensor readings, and pre-processing analytics—the system reduces the volume of data that needs to be stored and processed centrally. This preliminary processing at the edge decreases the time required for cloud-based processing while maintaining adequate storage capacity for essential data.
4Adaptability or versatility
If centralized processing is used to maintain standardization, then device interoperability improves, but processing delays and network congestion worsen
Solution Approach 1:
The patent implements a universal edge computing framework that provides standardized processing capabilities, data formats, and communication protocols across diverse edge devices. This universal framework enables device interoperability without requiring centralized processing, as each edge device can independently process and exchange data using common standards. The multi-functional edge architecture supports various data types and sources while maintaining consistency, thereby achieving both interoperability and reduced processing delays.
Data Source
AI summary
A method and system are disclosed for acquiring and processing data, the exemplary system includes: one or more intelligent devices connected in a dynamic ad hoc network as a network of edge devices which can optionally access a cloud storage, at least one intelligent device being configured with a software installation to selectively receive and execute analytics on data received; at least one of the intelligent devices being configured to identify data to be requested from at least one other edge device for enhancing analytics performed on the at least one intelligent device; and a switchboard for maintaining a current view of resources in the network, and functions for which each resource is tasked, the resources including the at least one intelligent device and those edge devices which can communicate with the at least one intelligent device on the network.