Compare mobile ad-hoc routing

Measure route discovery, control work, delivery and repair under matched dense, sparse and mobile scenarios.

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 shared scenario.
    You will see
    Input: execute notebook step 1 with the record’s unchanged fixture, seed, and parameters. Observed: seed=42; square=100x100; radio uses Euclidean distance Observed: dense 30 30 0 6 30 30 Observed: sparse 15 15 0 6 30 30
    Why it matters
    Expected: STEP 1 scenario-contract ready from this deterministic notebook step. Interpretation: Matching the random field and workload isolates the routing choice. Warm-up and control-count boundaries must be stated before comparison. Action: Run python main.py and inspect step 1: freeze the shared scenario. 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 inspect the seeded field (first six nodes).
    You will see
    Input: execute notebook step 2 with the record’s unchanged fixture, seed, and parameters. Observed: 0 63.9 2.5 3,4,9,20 Observed: 1 27.5 22.3 4,5,6,11,13,21,22,23,25,29 Observed: 2 73.6 67.7 7,10,14,15,18,19,27
    Why it matters
    Expected: STEP 2 topology measured from this deterministic notebook step. Interpretation: Radio range changes the graph itself. The seeded coordinates let another learner reproduce which relays can hear one another. Action: Run python main.py and inspect step 2: inspect the seeded field (first six nodes). 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 send identical packets through the dense field.
    You will see
    Input: execute notebook step 3 with the record’s unchanged fixture, seed, and parameters. Observed: Flood 0-4-1-29 3 0 102 100% 30 Observed: AODV-style 0-4-1-29 3 20 18 100% 230 Observed: DSDV-style 0-4-1-29 3 1520 18 100% 30
    Why it matters
    Expected: STEP 3 dense comparison complete from this deterministic notebook step. Interpretation: DSR carries a source route while AODV-style forwarding uses a discovered path. DSDV pays for table formation before forwarding; flooding spends data copies. Action: Run python main.py and inspect step 3: send identical packets through the dense field. 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 repeat with the legacy sparse scenario.
    You will see
    Input: execute notebook step 4 with the record’s unchanged fixture, seed, and parameters. Observed: Flood unreachable 0 0 6 0% None Observed: AODV-style unreachable 0 1 0 0% None Observed: DSDV-style unreachable 0 48 0 0% None
    Why it matters
    Expected: STEP 4 sparse comparison complete from this deterministic notebook step. Interpretation: A disconnected graph has no immediate route. A failed delivery is evidence about topology, not proof that a named protocol is broken. Action: Run python main.py and inspect step 4: repeat with the legacy sparse scenario. 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 move for one second, invalidate, and rediscover.
    You will see
    Input: execute notebook step 5 with the record’s unchanged fixture, seed, and parameters. Observed: Max displacement <=10; broken cached edges=[] Observed: Cached route valid=True; rule: reject any missing edge Observed: Flood 0-4-1-29 3 0 96 100% 30
    Why it matters
    Expected: STEP 5 mobility rediscovery complete from this deterministic notebook step. Interpretation: Mobility can invalidate previously useful state. Rebuilding tables and rediscovering on the same moved graph makes the recovery comparison inspectable. Action: Run python main.py and inspect step 5: move for one second, invalidate, and rediscover. 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 force a route error even if movement spared the route.
    You will see
    Input: execute notebook step 6 with the record’s unchanged fixture, seed, and parameters. Observed: Before=[0, 4, 1, 29] Observed: After link removal valid=False; rule: every edge must exist Observed: RERR emitted=1; expired at t=61: True; cache=None
    Why it matters
    Expected: STEP 6 failure handled from this deterministic notebook step. Interpretation: The host requires route errors, cache cleanup and recovery evidence. The forced missing edge and cache expiry show both ways a route stops being usable. Action: Run python main.py and inspect step 6: force a route error even if movement spared the route. 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 vary load and repeat 30 seeds.
    You will see
    Input: execute notebook step 7 with the record’s unchanged fixture, seed, and parameters. Observed: Vary load and repeat 30 seeds Observed: 1 AODV-style 20 20.00 Observed: 1 DSDV-style 1520 1520.00
    Why it matters
    Expected: STEP 7 load-and-seed sensitivity measured from this deterministic notebook step. Interpretation: Control work per packet depends on workload. Thirty seeds expose topology sensitivity without claiming a radio-performance confidence interval. Action: Run python main.py and inspect step 7: vary load and repeat 30 seeds. 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 carry one packet across scheduled contacts.
    You will see
    Input: execute notebook step 8 with the record’s unchanged fixture, seed, and parameters. Observed: 5 3 0 None 2 3 Observed: 5 5 0 None 2 3 Observed: 10 3 0 None 2 3
    Why it matters
    Expected: STEP 8 DTN TTL-and-copy comparison complete from this deterministic notebook step. Interpretation: Store-and-forward needs contacts before expiry and enough copy budget. The same contact trace makes TTL and replication comparisons fair. Action: Run python main.py and inspect step 8: carry one packet across scheduled contacts. Boundary: this local fixture is not a live device, network, or site measurement.

  9. Step 9

    Do
    In the JupyterLite notebook, run the Step 9 cell to validate the model before interpreting it.
    You will see
    Input: execute notebook step 9 with the record’s unchanged fixture, seed, and parameters. Observed: two nodes one hop: PASS; disconnected target: PASS Observed: same seed same coordinates: PASS; discovered edges exist: PASS Observed: No MAC contention, fading, queues, or measured battery model.
    Why it matters
    Expected: STEP 9 validation PASS from this deterministic notebook step. Interpretation: One-hop and disconnected cases catch graph mistakes. Passing them supports this small model, while testbed measurements are still needed for field conclusions. Action: Run python main.py and inspect step 9: validate the model before interpreting it. 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. Which statement best describes why a team might choose DSR for an ad-hoc IoT deployment?

    Return to the chapter’s knowledge check

  2. A field team says DSR is working because one packet reached the gateway. What review evidence is still missing?

    Return to the chapter’s knowledge check

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