IoT Data Filter Deployment for Bandwidth and Power Optimization
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Solution Overview
Problem
Implementing a model to analyze data from multiple IoT devices requires significant network bandwidth, and existing systems face challenges in maintaining efficient network performance and reducing power consumption, especially when handling large volumes of data from devices like thermostats.
Innovation Solution
The system configures local networks of IoT devices by deploying data filters and processing functions across multiple devices, allowing for efficient data processing and reducing network bandwidth and power consumption. This is achieved through a hub device connected to a provider network, which determines and deploys data filters and processing functions based on network topology and performance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a model is implemented to analyze data from multiple IoT devices, then data analysis capability is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent segments the centralized data processing model into distributed processing functions deployed across multiple edge devices in the network. Each device executes a portion of the processing logic locally, reducing the volume of data that must be transmitted across the network while maintaining comprehensive data analysis capability.
Solution Approach 2:
The patent introduces a new dimension of processing by deploying model functions across the spatial dimension of the network topology. Instead of single-point centralized processing, processing capabilities are distributed across multiple nodes, transforming the network from a hierarchical structure to a distributed mesh architecture that reduces bandwidth bottlenecks.
2Loss of information
If data is transmitted from multiple IoT devices to the hub device, then data collection completeness is improved, but network performance deteriorates
Solution Approach 1:
The patent extracts non-essential data processing operations from the central hub and relocates them to edge devices. By taking out filtering, aggregation, and preliminary analysis functions from the hub, the system reduces network traffic while ensuring complete data collection through distributed processing.
Solution Approach 2:
Edge devices are empowered with self-service processing capabilities, executing data filtering and processing functions locally without requiring constant hub intervention. This autonomous processing reduces network dependency while maintaining complete data collection through coordinated distributed operations.
3Reliability
If processing functions are centralized at the hub device, then model security is improved, but power consumption increases
Solution Approach 1:
The patent applies local quality by deploying different processing functions to different devices based on their capabilities and security requirements. Sensitive model components remain secured at the hub, while less sensitive processing functions are distributed to edge devices with lower power consumption characteristics, optimizing the energy-security balance.
Solution Approach 2:
The system dynamically changes operational parameters by adjusting which processing functions are executed locally versus centrally based on network conditions, device capabilities, and security requirements. This parameter adjustment allows the system to optimize power consumption while maintaining adequate model security through selective distributed processing.
Data Source
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AI summary
A deployment service at a remote provider network receives topology data for a local network and generates data filters for edge devices of the local network based on the topology data. The deployment service then sends the data filters to a hub device connected to the local network. The hub device deploys the data filters to respective edge devices of the local network. The data filters may be configured to discard a sufficient portion of collected data to prevent routers from being overloaded by network traffic. The data filters may also be configured to discard a sufficient portion of collected data to prevent the edge devices from consuming too much power in order to preserve energy cost or battery life.