Edge Device Mutual Reinforcement with Dynamic Triggering
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Solution Overview
Problem
Existing edge computing systems in IoT networks rely heavily on central control servers for data analysis and decision-making, leading to inefficiencies in resource allocation and accuracy in monitoring and predicting conditions, such as fire alerts, which can be improved by enabling edge devices to autonomously monitor and calculate based on dynamic triggering conditions and computation frequencies.
Innovation Solution
Implementing a system where an initializer connects multiple edge devices, allowing them to autonomously initiate monitoring and calculation tasks based on predefined conditions, adjust monitoring intervals, and trigger other devices to share the calculation load, thereby reducing the need for central server intervention and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If edge devices rely on central control servers for data analysis and decision-making, then device complexity is reduced, but productivity and response time deteriorate due to data transmission delays and server dependency
Solution Approach 1:
The patent extracts computational capabilities from the central server and distributes them to edge devices. Each edge device is equipped with local processing units that can independently analyze data and make decisions without continuous server intervention, thereby reducing dependency on central infrastructure while maintaining high computational efficiency
Solution Approach 2:
The system segments the overall computational task into distributed units performed by individual edge devices. Each device handles specific monitoring and calculation functions locally, dividing the workload across multiple devices rather than concentrating it at a single central server, which improves response time and reduces server burden
2Measurement precision
If all edge devices continuously monitor all parameters, then measurement precision is improved, but use of energy deteriorates due to constant operation
Solution Approach 1:
Instead of continuous monitoring, the system implements periodic monitoring where edge devices activate sensors and processors at scheduled intervals. The monitoring frequency is dynamically adjusted based on system state, allowing devices to remain dormant between intervals to conserve energy while maintaining adequate measurement precision through timely data collection
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on real-time conditions. When parameters are stable, monitoring frequency is reduced to save energy; when anomalies are detected or critical thresholds are approached, monitoring intensity increases to maintain precision. This dynamic adaptation balances energy consumption with measurement accuracy
3Measurement precision
If multiple edge devices operate simultaneously, then measurement precision is improved through collaborative monitoring, but use of energy deteriorates due to redundant operation
Solution Approach 1:
The patent merges the monitoring capabilities of multiple edge devices into a coordinated collaborative system. Devices share data and computational results through communication protocols, combining their observations to achieve higher measurement precision than individual devices could attain alone, while avoiding redundant operations through coordinated workload distribution
Solution Approach 2:
The system implements feedback mechanisms where edge devices exchange status information and monitoring results. Based on this feedback, devices can adjust their operation - if one device detects a condition that another device is already monitoring, the second device can reduce or stop its monitoring to avoid redundancy. This feedback-driven coordination maintains measurement precision while minimizing overall energy consumption
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for mutual reinforcement of edge devices with dynamic triggering conditions and/or computation frequencies. In one example, a first edge device in multiple edge devices in an Internet of Things (IoT) system monitors at least a first parameter. The first edge device determines whether a first condition from one set of conditions is satisfied based on at least the monitored first parameter. In response to determining that the first condition is satisfied, the first edge device automatically transmits a signal to a second edge device in the multiple edge devices to initiate or stop monitoring of a second parameter by the second edge device.