Probability-Based Network Data Updates to Reduce HVAC Bandwidth Waste
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Solution Overview
Problem
HVAC systems face bandwidth inefficiencies due to frequent data updates over networks, even when data has not changed significantly, leading to wastage of network resources.
Innovation Solution
A data system controller with a data service module that determines data change probability based on time-since-last-read value, number of total reads, and number of data changed reads, updating data only when the probability exceeds a threshold or at a maximum time-since-last-read value, and using stored data otherwise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is updated frequently over the network, then data freshness is improved, but network bandwidth consumption increases
Solution Approach 1:
The system dynamically changes the data update frequency parameter based on the calculated probability of data change. When probability is high, updates occur frequently; when probability is low, updates are reduced. This adaptive parameter adjustment resolves the contradiction by optimizing the balance between data freshness and bandwidth consumption.
Solution Approach 2:
The system transitions from static periodic data updates to dynamic probability-based updates. The update frequency adapts in real-time based on observed data change patterns, allowing the system to respond to changing conditions and optimize network resource usage while maintaining data freshness when necessary.
2Measurement precision
If data is transmitted frequently, then data accuracy is improved, but network resource consumption increases
Solution Approach 1:
The system adjusts the data transmission frequency parameter based on the calculated probability of data change. By changing this parameter dynamically, the system transmits data accurately when changes are likely while reducing unnecessary transmissions when data remains stable, thus resolving the contradiction between data accuracy and network resource consumption.
Solution Approach 2:
The system uses historical data patterns to self-determine when updates are necessary, eliminating the need for continuous network transmissions. The probability calculation mechanism allows the system to serve its own data freshness requirements by intelligently predicting when updates will be valuable, reducing overall network resource consumption.
3Reliability
If data updates are performed continuously, then data currency is improved, but bandwidth efficiency deteriorates
Solution Approach 1:
The system replaces continuous data updates with dynamic probability-based updates. The update frequency adapts based on observed data change patterns, allowing the system to maintain data currency when necessary while maximizing bandwidth efficiency during periods of stability. This dynamic approach resolves the contradiction by making the system responsive to actual data change needs.
Solution Approach 2:
The system dynamically changes the update interval parameter based on calculated data change probability. When probability thresholds are exceeded, updates occur to maintain data currency; otherwise, updates are deferred to improve bandwidth efficiency. This parameter adaptation resolves the contradiction between data currency and bandwidth efficiency.
Data Source
AI summary
A data system controller can include one or more application modules configured to receive data from one or more devices, and a data service module in operative communication with the one or more application modules to provide data thereto. The data service module can be configured to receive data from the one or more devices. The data service module can be configured to update data from a respective device of the one or more devices when a data change probability of the respective device is above a probability threshold to provide updated data from the respective device to the one or more application modules.