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

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

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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
    Run python main.py and inspect step 1: freeze the legacy topology.
    You will see
    15 nodes: three rows x five columns; horizontal and vertical unit links; rows: 0 1 2 3 4 / 5 6 7 8 9 / 10 11 12 13 14. STEP 1 scenario-contract ready
    Why it matters
    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.

  2. Step 2

    Do
    Run python main.py and inspect step 2: print radius-2 intra-zone tables: nodes 0-7.
    You will see
    node destination:next-hop/hops (self omitted); 0 1:1/1 2:1/2 5:5/1 6:1/2 10:5/2. STEP 2 local tables 0-7 complete
    Why it matters
    IARP stores local next hops and distances. These first eight tables show which destinations are ready without reactive discovery.

  3. Step 3

    Do
    Run python main.py and inspect step 3: print radius-2 intra-zone tables: nodes 8-14.
    You will see
    node destination:next-hop/hops (self omitted); 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. STEP 3 local tables 8-14 complete
    Why it matters
    The remaining seven tables complete the network record. Zone-edge membership explains who can receive bordercast queries.

  4. Step 4

    Do
    Run python main.py and inspect step 4: follow local lookup and out-of-zone bordercast.
    You will see
    Local 0->6: path=[0, 1, 6] queries=0; origin border query_hops. STEP 4 bordercast measured
    Why it matters
    A local target needs no bordercast. An external target pays query and reply work, and duplicate suppression bounds each query recipient.

  5. Step 5

    Do
    Run python main.py and inspect step 5: compare radius costs on the same workload.
    You will see
    r entries refresh queries query_tx reply total; 1 44 15 13 13 6 34. STEP 5 radii 1,2,3 compared
    Why it matters
    Larger zones trade more proactive refresh and memory for fewer reactive queries. Count hop transmissions as well as border recipients.

  6. Step 6

    Do
    Run python main.py and inspect step 6: detect degeneration and check the legacy arithmetic.
    You will see
    r=10 >= diameter 6; node-0 zone size=15; Node-0 border=[]; reactive queries=0. STEP 6 overlarge zone diagnosed
    Why it matters
    A radius beyond network diameter eliminates the reactive half of ZRP. The supplied overhead arithmetic is illustrative, separate from this run’s packet counts.

  7. Step 7

    Do
    Run python main.py and inspect step 7: validate faults and fresh local tables.
    You will see
    one-hop: forward=[0, 1] reverse=[1, 0] PASS; partition: forward=None reverse=None PASS. STEP 7 fault checks PASS
    Why it matters
    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.

  8. Step 8

    Do
    Run python main.py and inspect step 8: replay 30 edge-loss seeds with matched input.
    You will see
    seeds=42..71; independent edge-loss probability=0.12; Reachable radius-runs=81/90; reachability oracle agrees for all 90. STEP 8 validation PASS
    Why it matters
    Matching edge-loss seeds across radii isolates radius choice. A reachability oracle checks correctness; a physical testbed is needed to measure radio performance.

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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