90  Ad Hoc Networks Assessment

In 60 Seconds

Routing protocol selection depends on traffic patterns: proactive (DSDV/OLSR) for frequent traffic (>1 packet/30s per node), reactive (AODV/DSR) for sparse traffic, hybrid (ZRP) for mixed. Multi-path redundancy converts link failures from minutes-long outages to sub-second recovery. Energy-aware routing extends network lifetime by up to 5x compared to shortest-path, which drains high-betweenness relay nodes prematurely.

Minimum Viable Understanding
  • Routing protocol choice depends on traffic patterns: proactive (DSDV/OLSR) for frequent traffic, reactive (AODV/DSR) for sparse traffic, hybrid (ZRP) for mixed patterns.
  • Multi-path redundancy transforms link failures from minutes-long outages into sub-second recovery by pre-caching backup routes.
  • Energy-aware routing extends network lifetime by up to 5x compared to shortest-path routing, which drains high-betweenness nodes prematurely.

Without multi-path: link failure detection (100ms ACK timeout) + route discovery (8s for 4-hop RREQ/RREP) = 8.1 seconds outage. With pre-cached backup: failure detection (100ms) + cache lookup (5ms) + first packet on backup (62ms for 4-hop path) = 167ms recovery. The improvement factor is \(\frac{8100ms}{167ms} \approx 48\times\) faster. Worked example: For vineyard sensors transmitting every 300s, single-path loses \(\frac{8.1s}{300s} = 2.7\%\) of packets during each failure. Multi-path loses \(\frac{0.167s}{300s} = 0.056\%\) — 48× reduction in data loss.

Sammy the Sensor needed to send an urgent temperature reading to the Gateway across the farm, but the direct path was blocked by a tractor!

“No worries!” said Max the Microcontroller. “We have THREE different paths planned ahead of time. Think of it like knowing three different ways to get to school – if one road is blocked, you just take another!”

Bella the Battery added, “But not all paths are equal! I prefer the one where my friends along the way have plenty of energy left. If we always use the same shortcut, those friends get tired really fast.”

Lila the LED blinked to explain: “Imagine passing a note in class. The shortest row might be fastest, but if those kids pass EVERY note, they get tired of it. Spreading notes across different rows keeps everyone happy and willing to help!”

The lesson: Smart networks plan backup routes BEFORE problems happen, and they share the work so no single helper gets worn out!

90.1 Learning Objectives

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

  • Apply Routing Knowledge: Select appropriate routing strategies for different deployment scenarios
  • Analyze Network Trade-offs: Evaluate energy vs latency vs reliability trade-offs
  • Solve Production Problems: Work through real-world multi-hop network challenges
  • Diagnose Network Issues: Identify bottlenecks, link failures, and routing inefficiencies
  • Compare Protocol Behavior: Distinguish between proactive, reactive, and hybrid approaches
  • Design Failover Strategies: Implement multi-path redundancy for reliable delivery

90.2 Prerequisites

Required Chapters:

Estimated Time: 30 minutes

This chapter tests your understanding of ad-hoc network concepts through quizzes, worked examples, and scenario-based exercises. If you are new to the topic:

  • Start with the protocol comparison table at the top to review the three routing approaches (proactive, reactive, hybrid).
  • Read the worked examples carefully – they walk through real calculations step by step with numbers you can follow.
  • Try the understanding checks by thinking about the scenarios BEFORE reading the answers. Even if you get them wrong, the explanations will deepen your learning.
  • If any concept is unfamiliar, revisit the Ad-hoc Fundamentals or Multi-hop Fundamentals chapters first.

90.3 Introduction

This chapter consolidates your understanding of production ad-hoc networks through comprehensive knowledge checks, worked examples, and understanding exercises. The assessments cover routing protocol selection, energy-aware design, link quality management, and multi-path failover strategies.

90.4 Ad-Hoc Routing Protocol Comparison

Understanding the trade-offs between routing protocol types is essential for production deployments.

Ad-hoc Routing Protocol Comparison

Protocol Type Examples How It Works Overhead Latency Best For
Proactive DSDV, OLSR Maintain all routes continuously High (periodic updates) Low (routes ready) Frequent, predictable traffic
Reactive AODV, DSR Discover routes on-demand Low (only when needed) High (discovery delay) Sparse, occasional traffic
Hybrid ZRP Proactive intra-zone, reactive inter-zone Medium (configurable) Medium Mixed traffic patterns
Ad-hoc routing protocol comparison flowchart showing three parallel approaches with trade-off analysis: Proactive protocols (DSDV, OLSR) initialize route tables then maintain all routes continuously through periodic updates enabling immediate data transmission with low latency but high overhead, Reactive protocols (AODV, DSR) stay idle until data needed then flood RREQ/RREP messages for on-demand route discovery providing low overhead but high latency with route expiration requiring rediscovery, and Hybrid ZRP using proactive intra-zone routing for fast local traffic and reactive inter-zone routing for efficient remote traffic achieving balanced medium latency and medium overhead
Figure 90.1: Ad-hoc routing protocol comparison showing three approaches: Proactive protocols (DSDV, OLSR) initialize route tables and maintain all routes continuously with periodic updates providing low latency but high overhead, Reactive protocols (AODV, DSR) stay idle until data needed then flood RREQ/RREP messages for on-demand discovery providing low overhead but high latency, Hybrid ZRP uses proactive intra-zone routing for local traffic and reactive inter-zone routing for remote traffic achieving balanced medium latency and medium overhead

Protocol Selection Guidelines:

  • Connected, static, frequent traffic -> DSDV
  • Connected, mobile, sparse traffic -> DSR
  • Connected, mixed traffic -> ZRP
  • Disconnected, abundant resources -> Epidemic
  • Disconnected, constrained resources, predictable -> CAR

90.5 Link Quality Classification

Understanding how to classify and respond to link quality changes is critical for maintaining network reliability.

Link quality classification decision tree showing hierarchical assessment process starting with Link Quality Assessment node branching through PDR thresholds (>=90%, >=70%, >=50%) and RSSI levels (>-70dBm, >-80dBm, >-90dBm) to classify links into four tiers: EXCELLENT (90-100% PDR, >-70dBm RSSI, <20ms latency) for best quality links, GOOD (70-90% PDR, -70 to -80dBm RSSI, 20-50ms latency) for reliable links, FAIR (50-70% PDR, -80 to -90dBm RSSI, 50-100ms latency) for marginal links, and POOR (<50% PDR, <-90dBm RSSI, >100ms latency) for unreliable links requiring avoidance or replacement
Figure 90.2: Link quality classification decision tree showing four-tier assessment: First check PDR thresholds (90%, 70%, 50%), then verify RSSI levels (-70dBm, -80dBm, -90dBm) to classify links as EXCELLENT (>90% PDR, >-70dBm RSSI, <20ms latency), GOOD (70-90% PDR, -70 to -80dBm, 20-50ms), FAIR (50-70% PDR, -80 to -90dBm, 50-100ms), or POOR (<50% PDR, <-90dBm, >100ms latency)

90.6 Inline Knowledge Check

90.7 Routing Strategy Comparison

Different routing strategies optimize for different objectives. Understanding when to apply each is essential for production deployments.

Routing strategy comparison diagram showing four parallel paths from source node to destination node with different traffic distribution patterns: Shortest Path strategy sends 100% traffic on single 3-hop Path A achieving low latency but creating unbalanced load and bottleneck risk, Load Balanced strategy distributes traffic 34%/33%/33% across three paths achieving distributed load and better resilience but higher latency, Energy-Aware strategy routes 48% on Path A (low battery), 30% on Path B (medium battery), 22% on Path C (high battery) achieving extended network lifetime and battery-aware routing, and QoS-Aware strategy prioritizes best PDR path with backup achieving maximize delivery reliability and link quality priority
Figure 90.3: Routing strategy comparison showing four approaches from source to destination: Shortest Path sends 100% traffic on 3-hop Path A (low latency but bottleneck risk), Load Balanced distributes 34/33/33% across three paths (distributed load and resilience), Energy-Aware routes 48/30/22% favoring high-battery nodes (extended lifetime), and QoS-Aware prioritizes best PDR path with backup (maximize delivery reliability)

Trade-offs Summary:

Metric Proactive Reactive Hybrid
Control Overhead High Low Medium
Route Latency Low High Configurable
Scalability Poor Good Good
Mobility Support Moderate Good Good

90.8 Knowledge Check

90.9 Worked Examples

Worked Example: Link Failure Recovery with Multi-Path Redundancy

Scenario: A smart agriculture deployment monitors 40 hectares of vineyard with soil moisture sensors. The gateway receives data from sensors via multi-hop ad-hoc network. During harvest season, vineyard workers and machinery frequently block wireless paths, causing link failures.

Given:

  • Primary path: Sensor S1 -> Node A -> Node B -> Gateway (3 hops, latency 45ms)
  • Backup path 1: S1 -> Node C -> Node D -> Node E -> Gateway (4 hops, latency 62ms)
  • Backup path 2: S1 -> Node F -> Node G -> Gateway (3 hops, latency 58ms, but G has 35% battery)
  • Link A-B failure rate during harvest: 15 failures/day, average recovery time: 8 seconds
  • Data transmission frequency: every 5 minutes (288 transmissions/day)
  • Maximum acceptable packet loss: 2%

Steps:

  1. Calculate expected failures during transmission windows:
    • Transmissions per day: 288
    • Link failures per day: 15
    • Probability of failure during any transmission: 15 x 8s / (24 x 3600s) = 0.14%
    • However, failures cluster during work hours (8am-6pm): 15 failures in 10 hours
    • During work hours: 15 x 8s / (10 x 3600s) = 0.33% per transmission
  2. Evaluate single-path (no backup) packet loss:
    • Each failure blocks 8 seconds of communication
    • Transmissions during failure window: 8s / 300s x 120 (work-hour transmissions) = 3.2 blocked
    • Total blocked: 15 failures x 3.2 = 48 packets/day
    • Packet loss rate: 48/288 = 16.7% (exceeds 2% threshold)
  3. Design multi-path failover strategy:
    • Detect failure: Link-layer ACK timeout (100ms)
    • Switch to backup: Immediate failover to pre-cached alternate path
    • Path selection priority:
      • Backup path 2 (F->G) rejected: G at 35% battery (below 40% threshold)
      • Backup path 1 (C->D->E) selected: All nodes >60% battery
  4. Calculate multi-path recovery time:
    • Failure detection: 100ms (ACK timeout)
    • Path switch: 5ms (cache lookup)
    • First packet on backup: 62ms (backup path latency)
    • Total recovery: 167ms (vs 8 seconds without backup)
  5. Calculate improved packet loss:
    • Packets lost during 167ms recovery: 167ms / 300s = 0.06%
    • Total failures: 15 x 0.06% = 0.9%
    • Final packet loss: <1% (meets 2% threshold)

Result: Implementing multi-path routing with automatic failover reduces packet loss from 16.7% to <1%, meeting the 2% reliability requirement. The energy-aware path selection preserves Node G’s battery for future backup duty.

Key Insight: Multi-path routing transforms link failures from minutes-long outages into sub-second blips. The key is pre-computing and caching backup paths, not discovering them after failure. Path selection should consider both latency and node energy to maintain backup availability throughout deployment lifetime.

Worked Example: Hop Count Optimization for Latency-Critical Alerts

Scenario: An industrial safety monitoring system detects gas leaks and must deliver alerts to the control room within 500ms. The default energy-aware routing uses 6-hop paths to preserve battery, but this may violate latency requirements.

Given:

  • Network topology: 25 nodes monitoring a chemical plant
  • Control room gateway: Central location
  • Current routing metric: Energy-aware (minimizes battery drain)
  • Current path: Sensor -> 6 hops -> Gateway (typical latency: 420ms)
  • Alternative path: Sensor -> 3 hops -> Gateway (latency: 180ms)
  • Per-hop latency: ~60ms (transmission + propagation + processing)
  • Energy cost per hop: ~0.5mJ transmission, ~0.2mJ reception
  • Alert frequency: 2-3 per month (rare)
  • Normal telemetry: every 60 seconds

Steps:

  1. Analyze latency breakdown for 6-hop path:
    • Per-hop delay: 60ms average
    • 6 hops x 60ms = 360ms transmission
    • Queueing delays at intermediate nodes: ~60ms
    • Total: ~420ms (within 500ms budget, but minimal margin)
  2. Calculate worst-case latency scenarios:
    • Network congestion (2 packets queued): +120ms
    • Retransmission (1 lost packet): +80ms
    • Worst case: 420 + 120 + 80 = 620ms (exceeds 500ms!)
  3. Design priority-based hop optimization:
    • Normal telemetry: Continue using 6-hop energy-efficient path
    • Critical alerts: Switch to 3-hop minimum-latency path
    • Path selection trigger: Alert priority flag in packet header
  4. Calculate optimized alert latency:
    • 3 hops x 60ms = 180ms baseline
    • Worst case with congestion/retry: 180 + 120 + 80 = 380ms
    • Guaranteed <500ms even under adverse conditions
  5. Evaluate energy trade-off:
    • 6-hop energy: 6 x 0.5mJ + 5 x 0.2mJ = 4.0mJ per packet
    • 3-hop energy: 3 x 0.5mJ + 2 x 0.2mJ = 1.9mJ per packet
    • Surprisingly, 3-hop path uses 52% less energy!
  6. Calculate annual impact:
    • Alerts: 30/year x 1.9mJ = 57mJ (negligible)
    • Telemetry on 6-hop: 525,600 packets x 4.0mJ = 2,102,400mJ
    • Telemetry on 3-hop: 525,600 x 1.9mJ = 998,640mJ
    • 3-hop saves 1,103,760mJ (52.5%) annually

Result: Switching critical alerts to the 3-hop path reduces worst-case latency from 620ms to 380ms, guaranteeing compliance with the 500ms requirement. Unexpectedly, the 3-hop path also uses 52% less energy per packet than the 6-hop “energy-optimized” path.

Key Insight: “Energy-aware” routing doesn’t always mean more hops. Longer paths consume more cumulative energy (more transmissions + receptions) even if each link is energy-efficient. For rare critical alerts, always use minimum-hop paths - they’re both faster AND more energy-efficient. Reserve complex routing optimizations for bulk data transfer where the overhead is amortized.

90.10 Understanding Checks

Apply these concepts to real-world deployment scenarios.

Scenario: Earthquake destroys cellular infrastructure. 50 rescue workers need communication across 2km disaster zone. Each radio: 500m range, 1W transmit power, battery lasts 8 hours continuous transmission.

Think about:

  1. Calculate minimum hops for end-to-end connectivity (2000m coverage)
  2. Why not use a single 2km-range radio (4x power increase)?
  3. How does multi-hop extend battery life in energy-constrained scenarios?

Key Insight: Minimum hops = 2000m / 500m = 4 hops. Path: Source -> Node1(500m) -> Node2(1000m) -> Node3(1500m) -> Dest(2000m). Radio power follows inverse square law: doubling distance requires 4x power. Single 2km link needs 16x power (1W x 16 = 16W) vs four 500m hops at 1W each. Battery impact: 16W transmission = 30 minutes runtime vs 1W multi-hop = 8 hours. Multi-hop isn’t just about range - it’s about energy efficiency. Disaster response networks prioritize battery life over latency.

Scenario: University campus deploys delay-tolerant messaging app for 5,000 students. Epidemic routing floods 50+ message copies per message. SocialCast analyzes: contact frequency (Alice-Bob meet daily), recency (last contact 2 hours ago), location similarity (both frequent library).

Think about:

  1. Why does epidemic routing work but waste bandwidth?
  2. How does SocialCast reduce copies from 50 to 5-10 while maintaining 90% delivery?
  3. What happens when social patterns are unpredictable (random mobility)?

Key Insight: Epidemic = flood everywhere (guarantees delivery but 80% wasted bandwidth). SocialCast = forward only if carrier’s social metrics > current holder’s. Example: Message for “Bob”. Current holder “Charlie” (meets Bob monthly). Encounters “Alice” (meets Bob daily) -> transfer to Alice (higher contact frequency). Encounters “Dave” (never meets Bob) -> don’t transfer. Result: 5-10 targeted copies follow social graph toward Bob. 90% delivery ratio, 80% less bandwidth. Perfect for human mobility (predictable social patterns). Fails for random mobility (no patterns to exploit) - use epidemic instead.

Scenario: Disaster response team uses DSR routing. Command post (stationary) communicates with mobile rescue teams (3-5 m/s). Device caches 5 routes. After 10 minutes, 40% of routes have stale/broken links due to mobility.

Think about:

  1. Calculate optimal cache timeout balancing freshness vs discovery overhead
  2. Why not always perform fresh route discovery (zero cache)?
  3. How does mobility speed affect ideal timeout?

Key Insight: Static timeout fails: 10min timeout -> 40% stale routes -> packet failures. 30sec timeout -> excessive RREQ floods (high overhead). Adaptive solution: Monitor ROUTE_ERROR rate: High errors (>5/min) -> reduce timeout to 2-3min (high mobility). Low errors (<1/min) -> extend timeout to 10min (stable topology). Mobility-aware: Command post to command post: 10min cache (stationary). Rescue team to rescue team: 2min cache (mobile). Mixed: 5min cache. Why not zero cache? Discovery latency: 50-200ms per RREQ/RREP cycle. Real-time voice needs <100ms. Cached routes = instant forwarding. Result: Adaptive caching = DSR’s low latency + mobility resilience. Route lifetime inversely proportional to node velocity.

Scenario: 20-node ad-hoc network has 3 paths from source to gateway: Path A (3 hops, nodes at 40% battery), Path B (4 hops, nodes at 80% battery), Path C (5 hops, nodes at 95% battery). Need to route 1000 packets while maximizing network lifetime.

Think about:

  1. Why doesn’t shortest-path routing (Path A) maximize network lifetime?
  2. Calculate battery depletion if all traffic uses Path A vs distributed across all paths
  3. How does energy-aware routing trade latency for longevity?

Key Insight: Shortest-path (Path A only): 1000 packets drain 40% -> 0% battery on 3 nodes -> network partition (other nodes isolated). Energy-aware distribution: 300 packets on A (40% -> 25%), 400 packets on B (80% -> 70%), 300 packets on C (95% -> 88%). Minimum battery: 25% (vs 0% shortest-path). Network survives 3x longer. Trade-offs: Path C has 67% higher latency (5 hops vs 3) but uses nodes with abundant energy. Production strategy: Route delay-tolerant traffic on long/high-battery paths, route real-time traffic on short/low-latency paths. Prevents premature node deaths that partition network.

Scenario: Ad-hoc network link between Node A and Node B: current RSSI -65 dBm, PDR 95%, nodes approaching at 2 m/s, will separate in 30 seconds. Link quality monitor must predict when link becomes unusable (RSSI < -85 dBm, PDR < 50%).

Think about:

  1. Calculate when proactive rerouting should trigger (before link failure)
  2. Why is prediction better than reactive rerouting after failure?
  3. How does mobility speed affect prediction accuracy?

Key Insight: Path loss increases with distance: RSSI drops ~3-4 dB per doubling distance. At 2 m/s separation velocity: Distance = 2 m/s x 30s = 60m additional separation. RSSI degrades: -65 dBm (now) -> -75 dBm (15s) -> -85 dBm (30s) -> link fails. Proactive rerouting: Trigger new route discovery at 20s (RSSI -80 dBm, PDR 70%) before complete failure. Gives 10s buffer for RREQ/RREP exchange. Reactive rerouting: Wait until link fails (PDR <50%) -> buffer loss, 200ms discovery delay, user-visible disruption. Prediction accuracy: High mobility (5 m/s+) -> prediction horizon 10-15s (short). Low mobility (1 m/s) -> horizon 60s+ (plenty of time). Use velocity-aware prediction windows.

90.11 Common Misconception

Common Misconception: “Shortest Path = Best Path”

The Misconception: Many developers assume shortest-path routing (minimum hop count) is always optimal for ad-hoc networks. After all, fewer hops mean lower latency and less forwarding overhead, right?

The Reality: In battery-constrained IoT deployments, shortest-path routing can reduce network lifetime by 60-75% compared to energy-aware routing.

Real-World Example - Wildlife Tracking Deployment:

A 2022 wildlife tracking project deployed 50 sensor nodes across 2km^2 of forest to monitor endangered species. Initial deployment used shortest-path routing (AODV with hop-count metric):

Shortest-Path Results (Week 1-4):

  • 3 central nodes (high-betweenness) forwarded 85% of all traffic
  • These 3 nodes depleted batteries in 28 days (4 weeks)
  • Battery drain: 40% -> 5% (critical level)
  • Network partitioned into 3 disconnected islands
  • Remaining 47 nodes had 70-85% battery (wasted capacity)
  • Effective network lifetime: 28 days

Energy-Aware Routing Results (Week 5-24):

  • Deployed battery-aware routing (cost = hop_count / remaining_battery^2)
  • Traffic distributed across 15 nodes instead of 3
  • Minimum battery after 20 weeks: 32% (vs 5% shortest-path at 4 weeks)
  • Extended network lifetime to 140+ days (5x improvement)

Quantified Impact:

  • Network lifetime: 28 days (shortest) -> 140+ days (energy-aware) = 400% improvement
  • Battery variance: sigma=23% (shortest, highly unbalanced) -> sigma=8% (energy-aware, balanced)
  • Latency penalty: +1.2 hops average (acceptable for 1-hour reporting interval)
  • Cost savings: Avoided 3 expensive technician visits for battery replacement

The Lesson: For delay-tolerant IoT applications (environmental monitoring, asset tracking, smart agriculture), energy-aware routing significantly outperforms shortest-path routing despite higher hop counts. Always consider application requirements (latency tolerance, battery budget, network lifetime goals) when selecting routing metrics.

Scenario: Industrial control network uses multi-path routing. Primary path fails - calculate total failover time to backup path.

Given:

  • Primary path: A → B → C → Gateway (3 hops, 45ms latency)
  • Backup path: A → D → E → F → Gateway (4 hops, 62ms latency)
  • Link failure detection: 100ms (ACK timeout)
  • Routing table update: 15ms
  • Packet buffering: 50 packets max

Steps:

  1. Failure detection: 100ms (first missing ACK)
  2. Route switch: 15ms (lookup backup in cache)
  3. First packet on backup: 62ms (4-hop transmission)
  4. Total recovery: 100 + 15 + 62 = 177ms

Result: <200ms failover meets industrial control requirements. Without pre-cached backup, would need 2-3 seconds for route discovery.

Metric Shortest-Path Energy-Aware Hybrid
Delivery latency Lowest (min hops) +20-40% higher +10-20% higher
Network lifetime Baseline +300-500% longer +150-250% longer
Implementation complexity Simple Moderate (battery monitoring) High (adaptive)
Best for Latency-critical Long deployment (2+ years) Mixed traffic

Example: Wildlife tracking with 2-year battery requirement → Energy-aware wins despite higher latency.

Common Mistake: Ignoring Backup Path Battery Levels

The Mistake: Pre-caching backup paths without checking node battery levels, leading to failover to dying nodes.

Impact: Primary path fails → switches to backup → backup node at 8% battery fails within minutes → both paths down.

Solution: Validate backup path battery levels during periodic health checks, invalidate backups with nodes <20% battery.

Common Pitfalls

Students often pick reactive protocols by default without analyzing traffic patterns. Proactive protocols (DSDV/OLSR) outperform reactive ones when traffic rate exceeds ~1 packet per 30 seconds per node — because route discovery overhead amortizes. Profile your traffic before selecting a protocol.

Single-path routing fails silently when the primary route breaks, causing 2-5 minute outages while new routes are discovered. Multi-path routing pre-caches backup routes, reducing failure recovery to sub-second. Always implement at least two disjoint paths for critical links.

Deploying shortest-path routing in energy-constrained networks drains high-betweenness relay nodes 3-5x faster than the rest, causing premature network partition. Energy-aware metrics (residual battery × path length) distribute load to extend network lifetime by up to 5x.

Default beacon intervals (1-2 seconds) cause excessive control overhead in static scenarios and stale routes in fast-moving scenarios. Adaptive beaconing (increase rate on link quality changes, decrease when stable) reduces overhead by 40-60% while maintaining route freshness.

90.12 Summary

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 Transforms Failures: Pre-cached backup paths reduce recovery time from seconds to milliseconds

  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, energy-aware routing can extend network lifetime by 5x

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

90.13 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.

90.14 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.

90.15 What’s Next

If you want to… Read this
Explore mobile phone gateway architectures Mobile Phones As A Gateway
Review all ad hoc networking concepts Ad Hoc Networks Production Review
Study the production framework chapter Ad Hoc Production Framework
Revisit DSDV proactive routing Ad-hoc Routing: Proactive (DSDV)
Revisit DSR reactive routing Ad-hoc Routing: Reactive (DSR)