Abstract: Devices, systems, methods, and processes for tackling the problem of sticky clients in wireless networks using machine learning processes are described herein. Thresholds related to data rates and/or Received Signal Strength Indicators (RSSI), for Access Points (APs) in a network are identified. These thresholds are configured to dynamically adjust, either based on the time of day or real-time roaming patterns, with the aim of mitigating sticky clients and enhancing overall network performance. Advanced machine learning techniques such as Graph Neural Networks are implemented within the domain of deep learning to significantly alleviate sticky client concerns and promote seamless connectivity for all users.

Abstract: Devices, systems, methods, and processes for tackling the problem of sticky clients in wireless networks using machine learning processes are described herein. Thresholds related to data rates and/or Received Signal Strength Indicators (RSSI), for Access Points (APs) in a network are identified. These thresholds are configured to dynamically adjust, either based on the time of day or real-time roaming patterns, with the aim of mitigating sticky clients and enhancing overall network performance. Advanced machine learning techniques such as Graph Neural Networks are implemented within the domain of deep learning to significantly alleviate sticky client concerns and promote seamless connectivity for all users.