262  Ad Hoc Networks: Production Framework Implementation

262.1 Learning Objectives

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

  • Build Production Frameworks: Implement comprehensive multi-hop ad-hoc network management systems
  • Assess Link Quality: Design link quality classification and monitoring for dynamic networks
  • Implement Multi-Path Routing: Create load-balanced routing across multiple paths
  • Monitor Network Performance: Build real-time topology discovery and health monitoring
  • Handle Network Dynamics: Manage link state changes and routing table updates
  • Deploy Production Systems: Apply the framework to real-world IoT deployments

262.2 Prerequisites

Required Chapters:

Technical Background:

  • Self-organizing networks
  • MANET concepts
  • Reactive vs proactive routing

Ad-hoc Routing Protocols:

Protocol Type Overhead Latency Best For
AODV Reactive Low High Dynamic
DSR Reactive Low High Small networks
OLSR Proactive High Low Static
DSDV Proactive High Low Stable

Estimated Time: 45 minutes

This chapter is implementation-heavy. It walks through a full Python framework for multi-hop ad hoc networks—topology discovery, link quality monitoring, multi-path routing, and simulation output.

It is designed to come after you are comfortable with the conceptual material from:

  • adhoc-fundamentals.qmd – why ad hoc networks exist, basic routing ideas, and limitations.
  • multi-hop-fundamentals.qmd – how multi-hop forwarding, path length, and connectivity work.
  • adhoc-hybrid-zrp.qmd – zone-based routing and the “Goldilocks” trade-off between proactive and reactive protocols.

If you are new to ad hoc networking:

  • Skim the code examples to see what a production-style toolkit might look like.
  • Focus on the printed outputs (topology stats, link quality, routing comparisons) and map them back to the earlier conceptual chapters.
  • Come back later for a deeper dive when you are ready to experiment with the code on your own machine.

262.3 Introduction

Production ad-hoc networks require robust management systems that go beyond simple routing protocols. Real-world IoT deployments face challenges including variable link quality, node mobility, battery constraints, and the need for continuous monitoring. This chapter provides a comprehensive production-ready Python framework that addresses these challenges through modular, extensible components.

The framework consists of four key layers:

  1. Topology Discovery Layer - Dynamic neighbor discovery, link tracking, and topology event monitoring
  2. Link Quality Assessment Layer - PDR, RSSI, latency measurement with quality classification
  3. Multi-Path Routing Layer - K-shortest paths, multiple routing metrics, and path selection strategies
  4. Performance Monitoring Layer - Network statistics, bottleneck detection, and health monitoring

262.4 Production Framework: Advanced Multi-Hop Network Management

Estimated Time: ~25 min | Difficulty: Advanced | Unit: P04.C06.U01

This section provides a comprehensive production-ready Python framework for managing multi-hop ad-hoc networks in real-world IoT deployments. The implementation covers topology discovery, link quality assessment, multi-path routing, load balancing, and network performance monitoring.

262.4.1 Enumerations and Type Definitions

262.4.2 Core Data Structures

262.4.3 Main Classes

262.4.4 Comprehensive Examples

262.4.4.1 Example 1: Complete Network Topology Management

Output:

=== Ad-Hoc Network Topology Management ===

Network created: 20 nodes

Discovering neighbors based on transmission range (100m)...

Neighbor Discovery Results:
  Total nodes: 20
  Total links: 38
  Network diameter: 6 hops
  Connectivity ratio: 98.4%

Neighbor statistics:
  Average neighbors per node: 3.8
  Min neighbors: 1
  Max neighbors: 8

  Most connected: node_12 (8 neighbors)
  Least connected: node_17 (1 neighbors)

--- Simulating Topology Change ---
Moving node_05 to new location...
  Neighbors before move: 4
  Neighbors after move: 2
  Neighbor change: -2

Topology events recorded: 78

262.4.4.2 Example 2: Link Quality Estimation and Monitoring

Output:

=== Link Quality Estimation ===

Simulating excellent link: node_00-node_01
  PDR: 94.0%
  Quality: EXCELLENT
  Latency: 10.2 +/- 1.9 ms
  RSSI: -55.3 dBm
  Stability score: 0.921
  Predicted stability (30s): 0.915

Simulating good link: node_02-node_03
  PDR: 81.0%
  Quality: GOOD
  Latency: 24.8 +/- 7.6 ms
  RSSI: -70.2 dBm
  Stability score: 0.762
  Predicted stability (30s): 0.754

Simulating poor link: node_04-node_05
  PDR: 44.0%
  Quality: POOR
  Latency: 59.3 +/- 19.2 ms
  RSSI: -87.8 dBm
  Stability score: 0.312
  Predicted stability (30s): 0.298

262.4.4.3 Example 3: Multi-Path Routing with K-Shortest Paths

Output:

=== Multi-Path Routing ===

Computing multiple paths: node_00 -> node_15

Found 3 disjoint paths:

Path 1 (path_1):
  Hops: node_00 -> node_03 -> node_07 -> node_12 -> node_15
  Hop count: 4
  Cost: 4.00
  State: ACTIVE

Path 2 (path_2):
  Hops: node_00 -> node_02 -> node_06 -> node_11 -> node_14 -> node_15
  Hop count: 5
  Cost: 5.00
  State: ACTIVE

Path 3 (path_3):
  Hops: node_00 -> node_01 -> node_05 -> node_09 -> node_13 -> node_15
  Hop count: 5
  Cost: 5.00
  State: ACTIVE

=== Routing Strategy Comparison ===

SHORTEST_PATH:
  Path usage distribution:
    path_1: 50 packets (100%)

LOAD_BALANCED:
  Path usage distribution:
    path_1: 17 packets (34%)
    path_2: 17 packets (34%)
    path_3: 16 packets (32%)

ENERGY_AWARE:
  Path usage distribution:
    path_1: 24 packets (48%)
    path_2: 15 packets (30%)
    path_3: 11 packets (22%)

Final path delivery ratios:
  path_1: 91.0% (141/155)
  path_2: 87.5% (28/32)
  path_3: 92.6% (25/27)

262.4.4.4 Example 4: Network Performance Monitoring

Output:

=== Network Performance Monitoring ===

Simulating network traffic...

Network Statistics:
  Total nodes: 20
  Total links: 38
  Network diameter: 6 hops
  Connectivity: 98.4%
  Average battery: 84.3%
  Average latency: 48.7 ms
  Max latency: 92.3 ms

Bottleneck nodes detected: 3
  High-traffic nodes:
    node_12: 48 forwarded, 92.3% forwarding ratio
    node_07: 35 forwarded, 87.5% forwarding ratio
    node_03: 29 forwarded, 82.9% forwarding ratio

Top 5 nodes by packet forwarding:
  node_12: 48 packets forwarded, 8 neighbors, 78.3% battery
  node_07: 35 packets forwarded, 6 neighbors, 81.7% battery
  node_03: 29 packets forwarded, 5 neighbors, 88.2% battery
  node_06: 22 packets forwarded, 4 neighbors, 92.1% battery
  node_11: 18 packets forwarded, 3 neighbors, 85.9% battery

262.4.4.5 Example 5: Energy-Aware Routing

Output:

=== Energy-Aware Routing Comparison ===

Testing Shortest Path strategy...
  Packets delivered: 489
  Min battery: 42.3%
  Avg battery: 87.6%
  Battery std dev: 18.7%
  Dead nodes: 0

Testing Energy-Aware strategy...
  Packets delivered: 487
  Min battery: 71.8%
  Avg battery: 89.2%
  Battery std dev: 9.3%
  Dead nodes: 0

=== Comparison ===
Energy-Aware vs Shortest Path:
  Min battery improvement: +29.5%
  Avg battery improvement: +1.6%
  Battery balance (lower std is better): +9.4%

262.4.4.6 Example 6: Integrated Network Simulation

Output:

=== Integrated Multi-Hop Network Simulation ===

Creating 25-node ad-hoc network...
Measuring link quality...

Network initialized:
  Nodes: 25
  Links: 52
  Diameter: 5 hops
  Connectivity: 99.7%

============================================================
Period 1
============================================================

Network Performance:
  Avg battery: 98.2%
  Avg latency: 47.3 ms
  Topology events: 105

Network Health:
  Bottleneck nodes: 2
  Failing links: 3

============================================================
Period 2
============================================================

  Warning: Node failure detected!
  Removed wsn_10 from network

Network Performance:
  Avg battery: 96.5%
  Avg latency: 49.1 ms
  Topology events: 116

Network Health:
  Bottleneck nodes: 3
  Failing links: 4

============================================================
Period 3
============================================================

Network Performance:
  Avg battery: 94.7%
  Avg latency: 50.8 ms
  Topology events: 116

Network Health:
  Bottleneck nodes: 3
  Failing links: 5

============================================================
Simulation Complete
============================================================

=== Final Network State ===

Topology:
  Active nodes: 24
  Active links: 48
  Network diameter: 6 hops
  Connectivity: 98.9%

Performance:
  Average latency: 49.1 ms
  Max latency: 87.6 ms
  Average battery: 94.7%

Traffic Statistics:
  Total sent: 289
  Total received: 266
  Total dropped: 23
  Network PDR: 92.0%

262.5 Framework Summary

This production framework provides comprehensive multi-hop ad-hoc network management:

Topology Management:

  • Dynamic neighbor discovery
  • Link tracking and maintenance
  • Topology event monitoring
  • Network diameter and connectivity metrics

Link Quality Assessment:

  • PDR, latency, jitter, RSSI tracking
  • Quality classification (Excellent/Good/Fair/Poor)
  • Stability scoring and prediction
  • Windowed metric estimation

Multi-Path Routing:

  • K-shortest paths algorithm (Yen’s variant)
  • Multiple routing metrics (hop count, latency, reliability, energy, composite)
  • Path disjointness for redundancy
  • Route cost calculation

Routing Strategies:

  • Shortest path (minimum hops)
  • Load balanced (distribute traffic)
  • Energy-aware (extend lifetime)
  • QoS-aware (maximize delivery ratio)

Network Monitoring:

  • Per-node statistics (forwarding, battery, energy)
  • Network-wide metrics (latency, connectivity, diameter)
  • Bottleneck detection
  • Failing link identification

The framework enables production-ready multi-hop ad-hoc networks with advanced routing, quality monitoring, and performance optimization.

262.6 Summary

This chapter provided a production-ready framework for managing multi-hop ad-hoc networks in IoT deployments.

Key Takeaways:

  1. Modular Architecture: The four-layer design (topology, quality, routing, monitoring) enables independent component development and testing

  2. Link Quality Classification: PDR, RSSI, and latency thresholds classify links as Excellent (>90% PDR), Good (70-90%), Fair (50-70%), or Poor (<50%)

  3. Multi-Path Redundancy: K-shortest paths with disjoint routes provide failover capability and load distribution options

  4. Routing Strategy Selection: Choose based on application needs:

    • Shortest path for low latency
    • Load balanced for even wear
    • Energy-aware for network longevity
    • QoS-aware for delivery reliability

  5. Continuous Monitoring: Real-time bottleneck detection and health monitoring prevent unexpected failures

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graph TB
    subgraph Discovery["Topology Discovery Layer"]
        ND[Neighbor Discovery]
        LT[Link Tracking]
        TE[Topology Events]

        ND -->|Discovers| LT
        LT -->|Monitors| TE
    end

    subgraph Quality["Link Quality Assessment Layer"]
        PDR[PDR Measurement]
        RSSI[RSSI Monitoring]
        LAT[Latency Tracking]
        QUAL[Quality Classification]

        PDR --> QUAL
        RSSI --> QUAL
        LAT --> QUAL
    end

    subgraph Routing["Multi-Path Routing Layer"]
        KSP[K-Shortest Paths]
        METRIC[Route Metrics]
        STRAT[Routing Strategy]

        KSP -->|Calculates| METRIC
        METRIC -->|Feeds| STRAT
    end

    subgraph Monitor["Performance Monitoring Layer"]
        STATS[Network Statistics]
        BOTTLE[Bottleneck Detection]
        HEALTH[Health Monitoring]

        STATS --> BOTTLE
        STATS --> HEALTH
    end

    Discovery --> Quality
    Quality --> Routing
    Routing --> Monitor
    Monitor -.->|Feedback| Discovery

    style ND fill:#2C3E50,stroke:#16A085,color:#fff
    style QUAL fill:#16A085,stroke:#2C3E50,color:#fff
    style STRAT fill:#E67E22,stroke:#2C3E50,color:#fff
    style HEALTH fill:#2C3E50,stroke:#16A085,color:#fff

Figure 262.1: Production framework architecture with four layers: Topology Discovery (neighbor discovery, link tracking, topology events), Link Quality Assessment (PDR/RSSI/latency measurements feeding quality classification), Multi-Path Routing (K-shortest paths algorithm with route metrics driving routing strategy), and Performance Monitoring (network statistics enabling bottleneck detection and health monitoring with feedback to discovery layer)

262.7 What’s Next?

Continue to the assessment chapter to test your understanding with knowledge checks, worked examples, and understanding exercises.

Continue to Ad Hoc Networks: Assessment and Practice ->

Deep Dives:

Comparisons:

Learning: