Inspect ZRP zones and bordercasts

Print intra-zone tables, trace bordercasts and compare radius costs with reproducible failure checks.

Blueprint Bina: I want you to inspect the failed case and the record that explains the next decision., your practice guide

Blueprint Bina: I want you to inspect the failed case and the record that explains the next decision.
Predict the reading, then compare it with the measurement.

Python 3 in your browser (JupyterLite)

Python · no install

Tier 2 · Web · paste-in setup · No account

Version tested: Python 3.12.7 / Pyodide 0.27.6 in JupyterLite 0.6.4; Chromium 148.0.7778.96; captureSource playwright:jupyterlite. Date: 2026-09-10.

Open the notebook in your browser and run each Python cell; no install or account is needed.

Three ways to run: use JupyterLite here with no install; run main.py locally from the downloadable lab folder; or open the same notebook in Google Colab.

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Steps

Screens captured against Python 3 in your browser (JupyterLite) Python 3.12.7 / Pyodide 0.27.6 in JupyterLite 0.6.4; Chromium 148.0.7778.96; captureSource playwright:jupyterlite on 2026-09-10; the tool may have moved on — the text steps are the contract.

  1. Step 1

    Do
    In the JupyterLite notebook, run the Step 1 cell to freeze the legacy topology.
    You will see
    Input: execute notebook step 1 with the record’s unchanged fixture, seed, and parameters. Observed: 15 nodes: three rows x five columns; horizontal and vertical unit links Observed: rows: 0 1 2 3 4 / 5 6 7 8 9 / 10 11 12 13 14 Observed: Computed diameter=6 hops; source=0 destination=14
    Why it matters
    Expected: STEP 1 scenario-contract ready from this deterministic notebook step. Interpretation: The exact legacy adjacency graph has a six-hop diameter. Keeping that separate from its eight-hop emergency example prevents mixing assumed and measured values. Action: Run python main.py and inspect step 1: freeze the legacy topology. Boundary: this local fixture is not a live device, network, or site measurement.

  2. Step 2

    Do
    In the JupyterLite notebook, run the Step 2 cell to print radius-2 intra-zone tables: nodes 0-7.
    You will see
    Input: execute notebook step 2 with the record’s unchanged fixture, seed, and parameters. Observed: Print radius-2 intra-zone tables: nodes 0-7 Observed: node destination:next-hop/hops (self omitted) Observed: 0 1:1/1 2:1/2 5:5/1 6:1/2 10:5/2
    Why it matters
    Expected: STEP 2 local tables 0-7 complete from this deterministic notebook step. Interpretation: IARP stores local next hops and distances. These first eight tables show which destinations are ready without reactive discovery. Action: Run python main.py and inspect step 2: print radius-2 intra-zone tables: nodes 0-7. Boundary: this local fixture is not a live device, network, or site measurement.

  3. Step 3

    Do
    In the JupyterLite notebook, run the Step 3 cell to print radius-2 intra-zone tables: nodes 8-14.
    You will see
    Input: execute notebook step 3 with the record’s unchanged fixture, seed, and parameters. Observed: Print radius-2 intra-zone tables: nodes 8-14 Observed: node destination:next-hop/hops (self omitted) Observed: 8 2:3/2 3:3/1 4:3/2 6:7/2 7:7/1 9:9/1 12:7/2 13:13/1 14:9/2
    Why it matters
    Expected: STEP 3 local tables 8-14 complete from this deterministic notebook step. Interpretation: The remaining seven tables complete the network record. Zone-edge membership explains who can receive bordercast queries. Action: Run python main.py and inspect step 3: print radius-2 intra-zone tables: nodes 8-14. Boundary: this local fixture is not a live device, network, or site measurement.

  4. Step 4

    Do
    In the JupyterLite notebook, run the Step 4 cell to follow local lookup and out-of-zone bordercast.
    You will see
    Input: execute notebook step 4 with the record’s unchanged fixture, seed, and parameters. Observed: Local 0->6: path=[0, 1, 6] queries=0 Observed: 0 2 2 Observed: 0 6 2
    Why it matters
    Expected: STEP 4 bordercast measured from this deterministic notebook step. Interpretation: A local target needs no bordercast. An external target pays query and reply work, and duplicate suppression bounds each query recipient. Action: Run python main.py and inspect step 4: follow local lookup and out-of-zone bordercast. Boundary: this local fixture is not a live device, network, or site measurement.

  5. Step 5

    Do
    In the JupyterLite notebook, run the Step 5 cell to compare radius costs on the same workload.
    You will see
    Input: execute notebook step 5 with the record’s unchanged fixture, seed, and parameters. Observed: 1 44 15 13 13 6 34 Observed: 2 104 59 6 12 6 77 Observed: 3 156 119 3 9 6 134
    Why it matters
    Expected: STEP 5 radii 1,2,3 compared from this deterministic notebook step. Interpretation: Larger zones trade more proactive refresh and memory for fewer reactive queries. Count hop transmissions as well as border recipients. Action: Run python main.py and inspect step 5: compare radius costs on the same workload. Boundary: this local fixture is not a live device, network, or site measurement.

  6. Step 6

    Do
    In the JupyterLite notebook, run the Step 6 cell to detect degeneration and check the legacy arithmetic.
    You will see
    Input: execute notebook step 6 with the record’s unchanged fixture, seed, and parameters. Observed: r=10 >= diameter 6; node-0 zone size=15 Observed: Node-0 border=[]; reactive queries=0 Observed: Legacy emergency example (given values, not simulated measurements):
    Why it matters
    Expected: STEP 6 overlarge zone diagnosed from this deterministic notebook step. Interpretation: A radius beyond network diameter eliminates the reactive half of ZRP. The supplied overhead arithmetic is illustrative, separate from this run’s packet counts. Action: Run python main.py and inspect step 6: detect degeneration and check the legacy arithmetic. Boundary: this local fixture is not a live device, network, or site measurement.

  7. Step 7

    Do
    In the JupyterLite notebook, run the Step 7 cell to validate faults and fresh local tables.
    You will see
    Input: execute notebook step 7 with the record’s unchanged fixture, seed, and parameters. Observed: one-hop: forward=[0, 1] reverse=[1, 0] PASS Observed: partition: forward=None reverse=None PASS Observed: asymmetric: forward=[0, 1] reverse=None PASS
    Why it matters
    Expected: STEP 7 fault checks PASS from this deterministic notebook step. Interpretation: A one-hop oracle, a disconnected graph, asymmetric links and a removed edge test different failure assumptions. Fresh zones must not silently reuse obsolete paths. Action: Run python main.py and inspect step 7: validate faults and fresh local tables. Boundary: this local fixture is not a live device, network, or site measurement.

  8. Step 8

    Do
    In the JupyterLite notebook, run the Step 8 cell to replay 30 edge-loss seeds with matched input.
    You will see
    Input: execute notebook step 8 with the record’s unchanged fixture, seed, and parameters. Observed: Replay 30 edge-loss seeds with matched input Observed: seeds=42..71; independent edge-loss probability=0.12 Observed: Reachable radius-runs=81/90; reachability oracle agrees for all 90
    Why it matters
    Expected: STEP 8 validation PASS from this deterministic notebook step. Interpretation: Matching edge-loss seeds across radii isolates radius choice. A reachability oracle checks correctness; a physical testbed is needed to measure radio performance. Action: Run python main.py and inspect step 8: replay 30 edge-loss seeds with matched input. Boundary: this local fixture is not a live device, network, or site measurement.

Chapter checks

These questions refer to the chapter’s examples. Use the return links to review their answers.

  1. A sensor network has frequent local traffic and occasional distant messages. Which ZRP feature matches that pattern?

    Return to the chapter’s knowledge check

  2. A team enlarges ZRP zones to reduce discovery traffic. Which cost should it expect to review?

    Return to the chapter’s knowledge check

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