20 Ad-Hoc Production: Routing Trade-offs

20.1 Start With the Situation

This section: Start With the Situation — The network finds a short route, a path with few forwarding steps.

The network finds a short route, a path with few forwarding steps. That path may cross a weak battery with little charge left or an unstable link that loses contact. Failover means switching to another path after one fails. The team must compare alternatives, time that switch, and show why the selected path remains useful under change.

20.2 Overview

This section: Overview — This route challenges shortest-path thinking with energy, mobility, multipath, and field validation.

This route challenges shortest-path thinking with energy, mobility, multipath, and field validation.

This is part 2 of 2. Review Ad-Hoc Production: Readiness Checks when you need the first route.

20.3 Learning Objectives

This section: Learning Objectives

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

• explain why shortest path may not be best
• compare multipath and energy-aware routing
• validate failover and mobility behavior

20.4 Production Readiness Evidence Record

This section: Production Readiness Evidence Record — Production readiness is not the same as solving one routing exercise.

Production readiness is not the same as solving one routing exercise. A useful ad-hoc assessment asks whether the routing claim still holds when traffic rate, node mobility, relay battery, link quality, interference, gateway placement, or recovery policy changes.

The strategies in Figure 20.1 trade latency and delivery confidence against relay capacity and battery use.

Inspect Figure 20.1 and look for the production evidence that would justify moving traffic away from the shortest path.

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Read the four routes in Figure 20.1 by the cost each choice moves elsewhere. The shortest path cuts delay but loads central relays. Load sharing spreads traffic across several paths. Energy-aware routing avoids weak batteries but may use longer paths. QoS-aware routing chooses measured delivery and a current backup. In the wildlife case, fifteen nodes extend the stated life from 28 to at least 140 days. The ratio is $140/28=5$, so life is at least five times greater while the mean path grows by 1.2 hops. Save relay load, battery, delivery, and delay before choosing.

Read Figure 20.1 from shortest path through load-balanced and energy-aware routing to the QoS-aware option. The fastest path can create hotspots; load balancing trades latency for resilience; energy-aware routing preserves weak relays; and QoS-aware routing prioritizes measured delivery with a backup. Production readiness depends on proving which of those consequences fits the observed condition.

+Checkpoint: Evidence Before Release

You now know:

• The wildlife example shows why shortest-path routing can drain three central nodes in 28 days while energy-aware routing extends the same deployment to 140+ days.
• The release decision needs routing fit, link evidence, failover evidence, and energy evidence; a diagram alone is not enough.
• A production claim must name the retest trigger, such as node movement, battery threshold, gateway relocation, seasonal obstruction, firmware, or traffic change.

20.5 Selection Workflow: Ad-Hoc Release Record

This section: Selection Workflow: Ad-Hoc Release Record — The release record turns quiz-style answers into a maintainable production decision.

The release record turns quiz-style answers into a maintainable production decision. It keeps the chosen routing strategy, rejected alternatives, measured limits, operations owner, and retest trigger together.

Use this release workflow:

1. State the deployment claim. Name the traffic, area, node count, gateway, mobility, and recovery requirement.
2. Test the weak path. Use the worst likely relay, battery, obstruction, or mobility condition as the release gate.
3. Record rejected routes. Explain which paths were excluded for latency, battery, PDR, security, or stale-route risk.
4. Set the recheck trigger. Name the exact field change that invalidates the current assessment.

20.6 Failover State Machine Review

This section: Failover State Machine Review — Energy-aware routing has the same structure.

Fast recovery depends on the sequence of events, not the label “multi-path.” A node must detect the failed link, reject unsafe alternatives, choose a backup path with enough link and battery evidence, update forwarding state, and deliver the first packet under the new path. Each stage can fail or add delay.

Energy-aware routing has the same structure. It is not enough to avoid low-battery nodes once. The review must track relay duty, route churn, battery thresholds, link quality, and whether a backup path quietly moves the bottleneck to a different node.

20.7 Checkpoint: Summary

This section: Checkpoint: Summary — This chapter consolidated key concepts for production ad-hoc network management through assessments and practice problems.

This chapter consolidated key concepts for production ad-hoc network management through assessments and practice problems.

Key Takeaways:

1. Protocol Selection Depends on Traffic Patterns: Proactive for frequent traffic, reactive for sparse traffic, hybrid for mixed patterns

2. Link Quality Classification Enables Smart Routing: PDR, RSSI, and latency thresholds classify links into four tiers for routing decisions

3. Multi-Path Redundancy Needs Measured Failover: Pre-cached backup paths can reduce recovery time when detection, switch, and first-packet delivery are recorded

4. Energy-Aware Routing Extends Lifetime: Distributing load across nodes prevents premature battery depletion and network partition

5. Shortest Path is Not Always Best: For delay-tolerant applications, measured relay load and battery state can justify energy-aware routing

6. Adaptive Strategies Beat Static Ones: Mobility-aware cache timeouts, priority-based path selection, and proactive link prediction outperform fixed configurations

20.8 Related Chapters: Further Reading

This section: Related Chapters: Further Reading

Foundational Papers:

• Perkins, C. E., & Bhagwat, P. (1994). “Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers.” ACM SIGCOMM.
• Johnson, D. B., & Maltz, D. A. (1996). “Dynamic source routing in ad hoc wireless networks.” Mobile Computing, Springer.
• Haas, Z. J., et al. (2002). “The zone routing protocol (ZRP) for ad hoc networks.” IETF Draft.

DTN and Epidemic Routing:

• Vahdat, A., & Becker, D. (2000). “Epidemic routing for partially connected ad hoc networks.” Duke University Technical Report.
• Fall, K. (2003). “A delay-tolerant network architecture for challenged internets.” ACM SIGCOMM.

Context-Aware and Social Routing:

• Musolesi, M., et al. (2005). “CAR: Context-aware adaptive routing for delay-tolerant mobile networks.” IEEE Transactions on Mobile Computing.
• Costa, P., et al. (2008). “Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks.” IEEE JSAC.

IoT-Specific:

• RPL: IETF RFC 6550 - Routing Protocol for Low-Power and Lossy Networks
• Al-Karaki, J. N., & Kamal, A. E. (2004). “Routing techniques in wireless sensor networks: A survey.” IEEE Wireless Communications.

20.9 Related Chapters: References

This section: Related Chapters: References

1. Perkins, C. E. (2001). Ad Hoc Networking. Addison-Wesley.

2. Abolhasan, M., Wysocki, T., & Dutkiewicz, E. (2004). “A review of routing protocols for mobile ad hoc networks.” Ad Hoc Networks, 2(1), 1-22.

3. Zhang, Z. (2006). “Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: Overview and challenges.” IEEE Communications Surveys & Tutorials, 8(1), 24-37.

4. Spyropoulos, T., et al. (2008). “Efficient routing in intermittently connected mobile networks: The multi-copy case.” IEEE/ACM Transactions on Networking.

5. IoT Routing Survey: Rajandekar, A., & Sikdar, B. (2015). “A survey of MAC layer issues and protocols for machine-to-machine communications.” IEEE Internet of Things Journal.

20.10 Related Chapters: What’s Next

This section: Related Chapters: What’s Next

• Explore mobile gateway architectures: Mobile Gateway Overview
• Review this production readiness chapter: Ad-Hoc Production Readiness
• Study the production framework chapter: Ad-Hoc Deployment Framework
• Revisit DSDV proactive routing: DSDV Proactive Routing
• Revisit DSR reactive routing: DSR Reactive Routing

20.11 Related Chapters

This section: Related Chapters

Deep Dives:

• DSDV Proactive Routing - Table-driven routing with sequence numbers
• DSR Reactive Routing - On-demand source routing for sparse networks
• Multi-hop Fundamentals - Core concepts for multi-hop forwarding

Comparisons:

• Routing Basics - General routing principles and protocols
• WSN Overview - Wireless sensor network architectures

Learning:

• Ad-hoc Labs and Quiz - Practice implementations and assessments
• Simulations Hub - Network simulation tools

20.12 Visual Reference Gallery

This section: Visual Reference Gallery

Visual: Ad Hoc Network Architecture

Before comparing protocols, use Figure 20.2 to establish what the routing system must control. Look for the absence of a fixed coordinator, then identify the wireless neighbours and the multi-hop path that lets a node reach beyond its own radio range.

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Read Figure 20.2 from an originating node through its immediate neighbour and onward to the remote node. Every relay is both a participant and part of the path, so movement, loss, or battery depletion changes the topology the protocol sees. That dependence is why production assessment begins with route formation and repair rather than assuming a permanent network backbone.

Ad Hoc Routing Protocol Comparison

Once the network boundary is clear, inspect Figure 20.3 to decide when route knowledge should be paid for. Move from proactive to reactive to hybrid, comparing the control overhead incurred before traffic with the discovery delay incurred when traffic begins.

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In Figure 20.3, the proactive branch keeps routes ready by spending periodic control traffic, while the reactive branch saves that idle work but discovers paths on demand. The hybrid branch limits proactive knowledge to a local zone and searches beyond it. The comparison turns the architecture into a workload decision: traffic frequency, mobility, and acceptable first-packet delay determine which cost is defensible.

Visual: Flying Ad Hoc Networks (FANETs)

Use Figure 20.4 to stress the previous protocol choice with fast three-dimensional movement. Start with the airborne peers, follow their changing inter-aircraft links, and then locate the air-to-ground boundary where mission data leaves the swarm.

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Figure 20.4 shows why a route that is valid at one instant can age quickly: aircraft separation and orientation alter both neighbour reachability and the path to the ground station. The production lesson is to assess route freshness, handoff behaviour, and degraded operation together; a protocol label alone does not prove that the swarm can preserve its mission traffic.

Energy-Aware vs Shortest Path

After choosing a routing family, inspect Figure 20.5 to see why its path metric must include deployment lifetime. Read the shortest-path case first, tracking which central relays carry repeated traffic, and then compare the battery distribution in the energy-aware case.

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Read Figure 20.5 from “Traffic avoids weak batteries” to “Day 140: all relays above 30%”. In the illustrated 140-day comparison, “Traffic avoids weak batteries” and “Day 140: all relays above 30%” show how shortest-path routing concentrates traffic on high-betweenness nodes, depleting their batteries. In the illustrated comparison, energy-aware routing sacrifices average path length to spread load across more nodes and extends the modelled lifetime from 28 to at least 140 days. This visualization helps operators understand why “optimal” paths may be suboptimal for network longevity. For the illustrated Energy-Aware vs Shortest-Path comparison, the “Traffic avoids weak batteries” and “Day 140: all relays above 30%” labels connect load distribution to the next routing decision.

In Figure 20.5, minimizing hop count repeatedly selects the same high-betweenness relays, so those batteries become the partition point even while other nodes retain energy. The energy-aware strategy accepts longer paths to spread work and keep the topology connected. This connects path selection to the earlier production objective: the best route is the one that meets latency and delivery needs without silently shortening the useful life of the network.

Multi-Path Routing Strategies

Finally, use Figure 20.6 to turn the single-metric comparison into an explicit policy choice. Follow each source-to-destination path from left to right, noting whether traffic is concentrated, split, steered away from depleted nodes, or assigned according to delivery quality.

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Figure 20.6 makes the consequence of each policy visible: shortest path optimizes immediate latency, load balancing reduces hotspots, energy-aware routing protects weak relays, and QoS-aware routing favours measured delivery with a backup. None is universally best. The running assessment therefore needs traffic priority, battery state, link evidence, and failover acceptance criteria before it can justify a production route policy.