Floorplan Network Power Control Using Demand Confidence Prediction
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Solution Overview
Problem
Existing network setups fail to efficiently manage network devices within a sustainable configuration, with existing technologies struggling to predict future bandwidth capacity needs and power usage, resulting in inefficient power and coverage.
Innovation Solution
A device with a processor, network interface controller, and memory, determines a sustainable configuration to manage network devices by predicting future bandwidth needs and adjusting power settings to optimize coverage and minimize power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access points transmit at full power all the time, then sufficient coverage is provided, but power usage is wasted
Solution Approach 1:
The system dynamically adjusts access point power levels based on real-time client density and predicted future needs. APs transition between full power, reduced power, and sleep modes according to changing environmental conditions, resolving the contradiction between maintaining sufficient coverage and reducing power waste during low-usage periods
Solution Approach 2:
The system uses machine learning models to predict future client arrivals and bandwidth needs, allowing APs to proactively adjust power levels before actual demand changes occur. This preliminary action ensures coverage is maintained when needed while avoiding power waste during predicted low-usage periods
2Use of energy by moving object
If power usage is lowered or devices are turned off, then power efficiency improves, but service level acceptance deteriorates due to sudden capacity demand changes
Solution Approach 1:
The system continuously monitors actual client density, bandwidth usage, and service quality metrics, using this feedback to adjust AP power levels in real-time. This closed-loop control ensures service level acceptance is maintained while optimizing power efficiency, as the system responds to actual conditions rather than relying solely on predictions
Solution Approach 2:
By predicting future capacity needs using machine learning models that analyze historical patterns and scheduled events, the system proactively adjusts power levels before demand changes occur, preventing service level violations while maintaining power efficiency
3Ease of manufacture
If access points are stationary, then deployment simplicity is maintained, but adaptability to changing environment deteriorates
Solution Approach 1:
The system enables stationary access points to self-adjust their operational characteristics based on environmental conditions. Each AP autonomously monitors its surroundings, predicts future needs, and adjusts its power levels and client association strategies, providing environmental adaptability without requiring physical relocation or complex manual reconfiguration
Solution Approach 2:
While the physical location of APs remains fixed for deployment simplicity, the system dynamically adjusts operational parameters including transmit power, client association policies, and sleep schedules, enabling the network to adapt to changing environmental conditions without requiring physical movement of devices
Data Source
AI summary
Devices, systems, methods, and processes for managing network devices through generated predictions and associated confidence levels are described herein. Networks within a floorplan can be operated at full capacity all day in an inefficient way when not adjusted due to traffic patterns and seasonality changes. Data related to the topology of the network, along with historical data can be utilized to generate predictions of various network needs. For example, the overall network throughput capacity needs may be predicted for a series of points in the future. An associated confidence level can be generated as well including one or more confidence intervals. These can be utilized to select a future need for the network and generate a corresponding sustainable network configuration for the network devices and/or their transceivers that can provide sufficient network needs while minimizing the overall power used. This can be automated over time once trust has been established.