48  Link Quality and Route Repair

iot
wireless-sensor-networks
routing
Keywords

WSN routing link quality, ETX routing, wireless sensor network link estimation, WSN route trace, WSN packet delivery evidence, WSN routing repair

48.1 Start With the Field Story

A short route is not always a good route. Link-quality review starts with delivery evidence, reverse acknowledgments, sample age, traffic type, and repair behavior so hop count does not hide an unreliable path.

48.2 In 60 Seconds

WSN link quality routing is not just “choose the strongest signal” or “choose the shortest path.” A reviewer needs evidence that the selected path actually supports the monitoring claim: packet delivery, reverse acknowledgment behavior, retries, parent changes, route-state age, relay pressure, freshness, missing-data visibility, and repair behavior.

This chapter reviews link quality as a routing evidence process. ETX, MIN-T, WMEWMA, RSSI, LQI, and packet reception ratios are useful vocabulary, but the review question is broader: what did the route observe, what did it ignore, what assumptions made the metric meaningful, and what deployment change would force retesting?

Phoebe the physics guide

Phoebe’s Why

In truly empty space, power spreads out over an ever-larger sphere as it travels, so the density any receiver sees falls as the inverse square of distance – that is the whole physical content behind free-space path loss. Real WSN deployments are not empty space: walls, furniture, bodies, and shelving scatter and absorb energy on top of the pure geometric spreading, so loss grows faster than \(1/d^2\). The log-distance model keeps the same inverse-power shape but lets the exponent grow past 2 to soak up that extra environmental loss, which is exactly why a route that looked fine in an open hallway test can lose margin once it is measured through the intended walls.

The Derivation

Isotropic spreading of transmit power \(P_t\) over a sphere of radius \(d\):

\[S = \frac{P_t}{4\pi d^2}\]

Combined with receive aperture \(A_e=\lambda^2/4\pi\), this gives the Friis free-space path loss:

\[\mathrm{FSPL} = \left(\frac{4\pi d}{\lambda}\right)^2\]

The log-distance model generalizes free space (where \(n=2\)) to real environments with path-loss exponent \(n\):

\[PL(d)_{dB} = PL(d_0)_{dB} + 10n\log_{10}\left(\frac{d}{d_0}\right) + X_\sigma\]

Link margin above receiver sensitivity \(P_{rx,min}\):

\[M = P_t + G_t + G_r - PL(d) - P_{rx,min}\]

Worked Numbers: A 2.4 GHz WSN Hop

  • Reference loss at \(d_0=1\) m, 2.4 GHz: \(\lambda=c/f=0.125\) m, \(\mathrm{FSPL}(1\,\mathrm{m})=20\log_{10}(4\pi/0.125)=40.0\) dB.
  • Free-space extrapolation to 50 m (\(n=2\)): \(PL=40.0+20\log_{10}(50)=74.0\) dB.
  • Log-distance with catalog-typical indoor/obstructed \(n=3.0\): \(PL=40.0+30\log_{10}(50)=91.0\) dB – 17.0 dB more than the free-space number, purely from the distance/obstacle exponent the brief asks about.
  • Fade margin: catalog-typical WSN radio \(P_t=0\) dBm, near-unity antenna gains, receiver sensitivity \(-95\) dBm. Received power at 50 m through the \(n=3\) path is \(0-91.0=-91.0\) dBm, so margin \(=-91.0-(-95.0)=4.0\) dB – thin enough that a route review should flag it for repair or a shorter hop rather than trust a one-time RSSI reading.

48.3 Learning Objectives

By the end of this chapter, you will be able to:

  • explain why hop count and signal strength alone are weak routing evidence in WSNs
  • distinguish delivery evidence, reverse acknowledgment evidence, signal indicators, and route-state evidence
  • use ETX, MIN-T, WMEWMA, RSSI, LQI, and probe results as bounded review inputs
  • review link-quality route traces for stale state, relay pressure, repair behavior, and missing-data visibility
  • build a link-quality routing evidence record with accepted limits, owner, fallback action, and retest trigger

48.5 Prerequisites

This chapter builds on WSN Routing Fundamentals Review, WSN Routing Introduction Review, WSN Routing Challenge Review, WSN Routing Protocol Classification Review, WSN Routing Data Aggregation Review, and WSN Routing Labs and Exercises Review.

If a learner cannot describe source nodes, relay nodes, sinks, parent or next-hop state, acknowledgments, retries, freshness, and route repair, review those chapters before using link-quality metrics.

48.7 Evidence Map

Use Figure 48.1 to keep signal measurements tied to route evidence.

The map prevents a common mistake: treating one measurement as the whole routing decision. RSSI can be useful, but it is not delivery evidence by itself. A path trace can be useful, but it is stale if probes, acknowledgments, and failure behavior are not reviewed.

48.8 Measurement Proof Limits

48.9 Evidence Record

Use Figure 48.2 to turn link-quality observations into a reviewable routing decision.

48.12 Reviewing Estimation Windows

Link estimation needs a memory of recent behavior, but stale memory can keep bad links in service. The review should make estimator behavior visible without overfitting to one fixed parameter.

Use a link-evidence ledger when the route compares candidate parents or next hops. The ledger should be one row per candidate transition, not one row for the whole route, so the reviewer can see where the risk lives: a marginal child-to-parent hop, a stressed relay near the sink, an old neighbor-table entry, or a reverse-link problem that appears only during acknowledgment or query traffic.

When both directions matter, a simplified ETX review often starts from 1 / (forward delivery * reverse delivery) and sums link costs across the path. That number is useful only if the ledger says what was measured, how old the evidence is, and which direction the workload or protocol actually depends on. Do not mix a probe PRR, an application delivery ratio, hardware LQI, and route-control reply rate unless the estimator window, decay, expiry, and hysteresis rule are visible.

Low-power radio coverage also has a transitional region where links work intermittently. A route estimator should keep those links out of a simple good/bad bucket and move them through visible repair states.

Local repair is acceptable only when the repair state is observable. If the node silently switches parent while the application loses sources or freshness, the routing layer has hidden the failure instead of repairing the decision path. During repair, the application-level effect still matters: aggregated traffic can hide which source was lost, duplicate packets can inflate an event count, and an alternate parent can move relay pressure to a node that was already close to its duty-cycle or energy limit.

48.13 Worked Review: Building Sensor Route

Scenario: Room sensors report regular environmental readings to a building gateway. Trend visibility and missing-room detection matter more than immediate control.

48.14 Worked Review: Event Query Route

Scenario: A gateway occasionally asks whether a region crossed a condition. The response must show which sources matched, which were missing, and whether results were fresh.

48.15 Common Mistakes

48.16 Review Checklist

Before accepting link-quality routing evidence, verify that the record includes:

  • monitoring claim, traffic pattern, freshness need, and sink boundary
  • chosen route, rejected alternatives, route-state age, and route repair rule
  • forward delivery evidence, reverse acknowledgment evidence, retries, and parent changes
  • probe or data sample scope, estimator window, expiry rule, and hysteresis behavior
  • relay pressure, missing-data visibility, aggregation effect, and exception path
  • accepted limits, owner, fallback action, monitoring signal, and retest trigger

48.19 Knowledge Check: Repair Evidence

48.22 Summary

WSN routing link quality is a review discipline, not a single number. Strong review connects signal indicators, delivery evidence, reverse acknowledgments, route-state age, relay pressure, freshness, repair behavior, and operations ownership. Metrics such as ETX, MIN-T, and WMEWMA are useful only when their assumptions and limits are visible.

48.23 Key Takeaway

WSN Routing Link Quality Review should balance path reliability, link quality, energy cost, latency, aggregation, topology change, control overhead, and deployment evidence.

48.24 Concept Relationships

48.25 What’s Next

Continue with WSN Routing: Trickle Algorithm to review dissemination and repair behavior, then revisit WSN Routing Labs and Exercises Review, WSN Routing Data Aggregation Review, and WSN Routing Protocol Classification Review when a route choice needs stronger evidence.