IoT Cluster Workload Distribution for Data Chatter Reduction
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Solution Overview
Problem
The exponential growth of IoT devices leads to a significant increase in data exchange chatter, resulting in astronomical amounts of data transfer, which can be mitigated by determining the geographical location of devices and utilizing spare compute cycles for workload processing, thereby reducing packet communications.
Innovation Solution
A method and system for distributed load processing using clusters of interdependent IoT devices, where a central IoT device selects and configures nearby devices to form clusters, enabling them to process workloads locally and reduce data transmission by forming high-availability configurations and managing device participation based on geographical proximity and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If IoT devices communicate and exchange data over a data communication infrastructure, then devices can interoperate and exchange information, but the data exchange chatter increases exponentially with the number of devices
Solution Approach 1:
The patent segments the monolithic data exchange problem into hierarchical levels: local device-to-device communication within clusters, regional gateway aggregation, and cloud connectivity. This segmentation reduces the communication complexity from O(N^2) device-to-device interactions to O(N) gateway-mediated interactions, dramatically reducing data chatter while preserving interoperability.
Solution Approach 2:
The patent introduces regional gateways as intermediary devices that aggregate data from multiple IoT devices before forwarding to the cloud or other devices. This intermediary layer reduces redundant data transmissions by filtering, filtering, and consolidating data flows, thereby reducing overall data transfer volume while maintaining full device interoperability capabilities.
2Adaptability or versatility
If more IoT devices are deployed to offer services, then service availability and device selection options increase, but the amount of data communication and processing overhead increases
Solution Approach 1:
The patent divides the IoT system into autonomous clusters managed by local cluster heads, rather than requiring centralized management of all devices. Each cluster independently manages its devices and workloads, reducing system management complexity from O(N) centralized operations to O(N/k) distributed operations where k is the number of clusters, while maintaining high service availability through local autonomy.
Solution Approach 2:
The patent implements dynamic cluster formation and device mobility management where devices can join or leave clusters based on their operational status and location. This dynamic approach allows the system to adapt to changing conditions without requiring complex reconfiguration, maintaining service availability while simplifying management through automated, rules-based dynamics rather than static, manually-configured structures.
3Quantity of substance
If devices process workloads locally using spare compute cycles, then data transmission is reduced, but coordination and state management between devices becomes more complex
Solution Approach 1:
The patent merges the state management functions of multiple distributed devices into a unified cluster-level state model managed by the cluster head. This consolidation reduces distributed state management complexity by providing a single source of truth for cluster-wide workload state, while still enabling local processing on member devices. The cluster head maintains the authoritative state and coordinates updates, transforming complex distributed state management into simpler centralized coordination within each autonomous cluster.
Data Source
AI summary
For distributed processing using clustering of interdependent Internet of Things (IoT) devices, at a central device, a data source to be used for processing a workload is determined. A set is selected of devices operating within a threshold distance from the data source at a first time. A first subset of the set of devices is selected. Each device in the first subset satisfies a clustering condition. A first device in the subset is instructed to configure a lightweight application to participate in the cluster and process the workload. The processing of the workload is halted on a second device, where the first device has a processing dependency on the second device in processing the workload. A preserved current state of processing the workload is transferred from the first device to a third device. The processing of the workload is continued using the second device and the third device.