Roaming Sensitivity Level | !!link!!

| Scenario | Recommended RSL | Reasoning | |----------|----------------|------------| | Streaming 4K video (train) | 0.8 | High volatility, need quick roam | | VoIP call (office) | 0.4 | Moderate, avoid mid-call handover | | Sensor node (factory) | 0.1 | Stability over reactivity | | Emergency responder | 0.95 | Always seek best link | | Idle smartphone | 0.2 | Save battery, no urgency | Correspondence: model@adaptive-systems.ai

We define as a dimensionless parameter, typically ranging from 0 (least sensitive, slowest to roam) to 1 (most sensitive, fastest to roam), that modulates the decision boundary for initiating a roaming event. RSL is not a single value but a dynamically adjustable state variable. 2. Related Work Existing mobility management protocols (e.g., MIH in IEEE 802.21, FMIPv6) use signal strength and latency thresholds but lack a unified sensitivity parameter. Reinforcement learning approaches adjust behavior post-facto, but none propose an explicit sensitivity level as a first-class control variable. Our work fills this gap by formalizing RSL and enabling predictive sensitivity tuning. 3. Mathematical Formulation of RSL Let the effective RSL at time ( t ) be defined as: roaming sensitivity level

[ HDH = \min\left(1, \frac\Delta t_since_last_roamT_hysteresis}\right) ] | Scenario | Recommended RSL | Reasoning |

where ( \alpha + \beta + \gamma = 1 ) (weighting coefficients determined by use case). SVI measures short-term fluctuations in primary link quality (e.g., RSSI, SNR). For ( N ) recent samples: Related Work Existing mobility management protocols (e