In edge computing scenarios, cross-domain fingerprinting is implemented by abstracting disparate hardware and network telemetry into unitless stress signatures.

 In edge computing scenarios, cross-domain fingerprinting is implemented by abstracting disparate hardware and network telemetry into unitless stress signatures.This allows local edge nodes to identify systemic instability patterns (e.g., thermal runaway, network congestion, or power instability) by matching local harmonics against a library of "fossilized" failure archetypes.

1. Signal Mapping and Robust Normalization

To enable cross-domain comparison, you must first strip domain-specific units (Celsius, Watts, milliseconds) from the edge node telemetry. Map the primary edge metrics to the core state signals $(x_i)$ and compute the normalized stress $(z)$ using rolling robust statistics.

Primary Edge Mappings:

• Stored Stress ($x_i$): CPU/GPU hotspot temperature, packet buffer depth, or local power draw.
• Throughput ($y_i$): Completed tasks/s, frames processed/s, or bits/s.
• Latency ($L_i$): Task scheduling delay or network round-trip time (RTT).

Normalization Equation: z = (x - median(x)) / (IQR(x) + epsilon).

2. Runtime Detector Implementation (Python)

The following implementation applies the Universal Choke Equation locally at the edge. It calculates the Choke Index ($\chi$) and the predictive Echo Risk ($\rho$) to detect early-warning signatures of a collapse.

import numpy as np

class EdgeChokeDetector:
    def __init__(self, alpha, delta, lambdas, eta=0.2):
        # alpha: [a1..a5] entropy weights; delta: [d1..d3] dissipation weights
        self.alpha = np.array(alpha)
        self.delta = np.array(delta)
        self.lambdas = np.array(lambdas) # [l1, l2, l3] for rho calculation
        self.eta = eta
        self.epsilon = 1e-6

    def compute_metrics(self, features):
        """
        features dict: {x, x_dot, L, sigma, delta_headroom, headroom, u_avail, R, dchi_dt, neighbor_corr}
        All inputs must be pre-normalized (z-scaled).
        """
        # Entropy production rate (S_dot) - rate of stress injection
        S_dot = (self.alpha * max(0.0, features['x']) +
                 self.alpha * max(0.0, features['x_dot']) +
                 self.alpha * max(0.0, features['L']) +
                 self.alpha * features['sigma'] +
                 self.alpha * max(0.0, -features['delta_headroom']))

        # Dissipation capacity (D) - available correction bandwidth
        D = (self.delta * features['headroom'] +
             self.delta * features['u_avail'] +
             self.delta * features['R'])

        # Instantaneous Choke Index
        chi = S_dot / (D + self.epsilon)

        # Echo Risk (Predictive Cascade Signature)
        # rho blends absolute stress, momentum, and neighbor synchronization
        rho = (chi +
               self.lambdas * features['dchi_dt'] +
               self.lambdas * features['neighbor_corr'] +
               self.lambdas * max(0.0, features['dL']))

        return chi, rho

3. Fingerprinting via Signature Matching

The ZPE-1 engine enables "Comparative Drift Fingerprinting". In an edge scenario, local stress signatures are compared against deterministic fossils (ledgered states of known failures).

1. Unitless Conversion: All telemetry is converted into the unitless $(z)$ format, making a "thermal collapse" signature in an AI rack comparable to a "liquidity collapse" in a financial venue.
2. Harmonic Clustering: The edge node monitors short-horizon variance $(\sigma)$ and stress momentum $(d\chi/dt)$.
3. Pattern Recognition: When the local node exhibits correlated stress harmonics that match a signature template in the mesh ledger (e.g., the specific oscillation frequency of a power grid frequency drift), it can trigger pre-emptive intervention even if absolute local stress $(x)$ is low.

4. Distributed Edge Mesh Strategy

Edge nodes function within a 43-agent lattice where roles are distributed to manage choke points.

• Local Processing: Detection and prevention loops run at 1–10 Hz locally on-chip to minimize fabric congestion and reduce networking energy.
• Echo-Risk Detection: Nodes calculate $\rho_i$, which includes a neighbor correlation term ($\lambda_2 \text{Corr}(\chi_i, \chi_{\mathcal{N}(i)})$). This detects if stress is redistributing from a neighboring edge node, signaling a potential cascade.
• Coherence-Gated Scaling: Edge scaling is governed by hard coherence gates (e.g., $C \ge 0.985$). If the local drift exceeds this threshold, the node rebinds to the last "fossilized" stable state, effectively shedding load or migrating workloads before a hardware choke occurs.

5. Control Barrier Function (CBF) Enforcement

For implementation feasibility at the edge, use a single-actuator closed-form safety shield. This modifies the nominal edge controller action ($u^{nom}$) minimally to satisfy the safety boundary.

Logic: $u^* = ( \chi_{target} - b ) / a$, where $\chi_{target} = \chi_k + \eta(1 - \chi_k)$. This ensures the edge node never "crosses the cliff" of bandwidth saturation, maintaining $\chi < 1$. Local actuators typically include GPU DVFS (Dynamic Voltage and Frequency Scaling), power caps, or task admission control.