Forecasted Location-Based IoT Clusters for Distributed Load Processing
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Solution Overview
Problem
The exponential growth of IoT devices leads to a significant increase in data communication traffic, with existing technologies failing to efficiently manage workload distribution and reduce data packet communications, resulting in astronomical data transfer requirements.
Innovation Solution
A method for distributed load processing using forecasted location-based IoT device clusters, where a central IoT device determines suitable IoT devices within a threshold distance from a data source, forms sub-clusters, and configures lightweight applications to process workloads, thereby reducing data communication by selectively utilizing available compute cycles geographically close to data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional centralized IoT device management is used, then device connectivity and data exchange are enabled, but data communication traffic increases astronomically
Solution Approach 1:
The patent applies local quality by processing workloads at the edge of the network where IoT devices are geographically located, rather than centralizing all data communication. Each geo-fenced region processes its own workload locally using available device compute cycles, eliminating the need for all devices to communicate with centralized cloud infrastructure. This localized processing dramatically reduces overall data communication traffic while maintaining processing efficiency.
Solution Approach 2:
The patent segments the IoT network into multiple geo-fenced regions, each with its own workload processing cluster. By dividing the network into independent segments that process workloads autonomously, the system eliminates the need for cross-region data communication and reduces overall network traffic. Each segment manages its own devices and workloads independently, preventing traffic aggregation at centralized points.
2Adaptability or versatility
If all IoT devices communicate through centralized infrastructure, then device interoperability is achieved, but data transfer requirements become astronomical
Solution Approach 1:
The patent implements preliminary action by pre-configuring lightweight applications on IoT devices that enable them to participate in workload processing clusters. Devices are prepared in advance with the necessary software components to process workloads locally, eliminating the need for continuous centralized configuration and reducing data transfer requirements for device management and interoperability.
Solution Approach 2:
The patent introduces geo-fenced regions as intermediaries between individual IoT devices and centralized cloud infrastructure. These regional clusters act as mediators that handle device interoperability and workload processing locally, reducing the need for direct communication between all devices and centralized infrastructure, thereby dramatically reducing data transfer requirements.
3Productivity
If existing workload distribution technologies are used, then some processing is achieved, but data packet communications remain excessively high
Solution Approach 1:
The patent implements self-service by enabling IoT devices to autonomously participate in workload processing within their geo-fenced regions without requiring centralized coordination for each processing task. Devices automatically join clusters, receive workload assignments, and process data locally, eliminating the need for continuous centralized management and reducing data packet communications to only essential cluster coordination messages.
Data Source
AI summary
For distributed processing using forecasted location-based IoT device clusters, at a central IoT device, a data source that is to be used and a duration for processing a workload is determined. A set of IoT devices operating within a threshold distance from the data source at a first time is selected. A subset of the set is selected to form a sub-cluster of IoT devices where a forecasted travel path of a member IoT device in the subset keeps the member within the threshold distance from the data source for the duration. A lightweight application is configured at a first IoT device in the subset which enables the first IoT device to participate in the sub-cluster and process the workload.