Machine Learning Key Allocation for Resource-Efficient 802.11r Roaming
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Solution Overview
Problem
The current key allocation process for 802.11r in wireless networks allocates more cryptographic keys than necessary, leading to wastage of resources and increased costs, especially in dense deployments, due to a static path loss value-based approach.
Innovation Solution
Implementing machine learning to predict the next access point (AP) a wireless client device will migrate to and selectively allocate cryptographic keys to this AP and a subset of top neighbors, dynamically adjusting the subset size to optimize resource consumption while meeting client demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic keys are allocated to all APs in the neighborhood list using static path loss value, then fast roaming is ensured, but resource consumption and costs increase
Solution Approach 1:
The system performs preliminary actions by predicting the next AP a client device will migrate to using machine learning models (Markov Chain Model or deep learning-based models). Keys are pre-allocated only to the predicted next AP and a subset of top neighbor APs, rather than all APs in the neighborhood list. This preliminary prediction and selective pre-allocation ensures fast roaming is maintained while significantly reducing resource consumption and costs.
2Reliability
If cryptographic keys are allocated to all APs in the neighborhood list, then connectivity during roaming is maintained, but the number of keys allocated increases
Solution Approach 1:
The system applies local quality by differentiating key allocation across different APs based on predicted migration patterns. Instead of uniformly allocating keys to all APs in the neighborhood list, the system identifies and allocates keys only to specific APs with higher probability of being the next migration target (predicted next AP and top neighbor APs). This selective, non-uniform allocation maintains connectivity during roaming while reducing the total quantity of keys allocated.
3Loss of energy
If machine learning is used to predict next AP and selectively allocate keys, then resource consumption is reduced, but complexity of key management increases
Solution Approach 1:
The system introduces an intermediary component - a machine learning-based prediction system that acts as a mediator between the key management service and the APs. This intermediary predicts client device migration patterns and provides guidance on which APs should receive keys. By delegating the prediction and selection function to this intermediary, the system reduces resource consumption through selective key allocation while managing complexity through a specialized prediction module rather than complex key management logic throughout the entire system.
Data Source
AI summary
Systems and methods are provided for optimizing resource consumption by bringing intelligence to the key allocation process for fast roaming. Specifically, embodiments of the disclosed technology use machine learning to predict which AP a wireless client device will migrate to next. In some embodiments, machine learning may also be used to select a subset of top neighbors from a neighborhood list. Thus, instead of allocating keys for each of the APs on the neighborhood list, key allocation may be limited to the predicted next AP, and the subset of top neighbors. In some embodiments, a reinforcement learning model may be used to dynamically adjust the size of the subset in order to optimize resources while satisfying variable client demand.