102 DTN Epidemic Routing

In 60 Seconds

Epidemic routing floods messages to every encountered node, achieving 95-99% delivery but with 500-1000% overhead. Spray-and-Wait bounds copies (L=10 typical) then each carrier waits for direct contact, reducing overhead by 5-10x while maintaining 85-92% delivery. TTL expiration and buffer management (FIFO/SHLI/MOFO) prevent unbounded resource waste.

Minimum Viable Understanding

Epidemic routing floods messages to every encountered node (like spreading a rumor), achieving 95-99% delivery ratio but with 500-1000% overhead in bandwidth, buffer, and energy
Control parameters limit resource waste: TTL expires old messages, buffer management (FIFO/SHLI/MOFO) handles full storage, and replication limits cap the number of copies
Spray-and-Wait is the practical optimization: create a bounded number of copies (L=10 typical), then each carrier waits for direct destination contact, dramatically reducing overhead while maintaining good delivery

Sensor Squad: The Rumor Network

“I have a secret message for Lila!” whispered Sammy the Sensor. “But I don’t know where she is!”

Max the Microcontroller grinned. “No problem! Tell EVERYONE you meet, and they’ll tell everyone THEY meet. Eventually someone will bump into Lila!”

“That’s epidemic routing!” said Bella the Battery. “But wait – if Sammy tells 50 friends, and each tells 50 more… that’s 2,500 copies of one message! My battery will run out carrying all those copies around!”

“Good point,” Max said. “That’s why we use Spray-and-Wait instead. Sammy makes only 10 copies and hands them to 10 friends. Each friend just holds their copy until they personally bump into Lila. Way fewer copies, and it still works!”

The Squad’s takeaway: Epidemic routing is like photocopying your message and handing it to every person on the street – expensive but very reliable. Spray-and-Wait is like making just 10 copies and giving them to people who travel a lot – much cheaper and almost as reliable!

102.1 Learning Objectives

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

Design Epidemic Routing: Implement message flooding strategies for intermittent networks
Analyze Replication Trade-offs: Balance delivery ratio against resource consumption
Apply Buffer Management: Use FIFO, SHLI, and MOFO strategies for constrained devices
Optimize with Spray-and-Wait: Implement bounded replication for resource efficiency
Calculate DTN Performance: Estimate buffer requirements and latency for real deployments

102.2 Prerequisites

Before diving into this chapter, you should be familiar with:

DTN Fundamentals: Understanding the store-carry-forward paradigm is essential before learning epidemic routing strategies
Multi-Hop Fundamentals: Multi-hop networking concepts provide background for understanding message propagation
Networking Basics: Fundamental networking concepts help contextualize DTN’s departure from traditional routing

Key Concepts

Epidemic Routing: DTN routing where every node forwards messages to all contacts; maximizes delivery probability at cost of network flooding
Replication Factor: Number of copies of a message in the network; epidemic creates unbounded copies limited only by storage
Spray-and-Wait: Controlled epidemic variant; sprays L copies then waits for direct delivery; limits replication while maintaining coverage
Binary Spray-and-Wait: Each copy holder gives half its tickets to a new contact; logarithmically distributes copies
Delivery Probability: Probability of eventual message delivery given contact rate and TTL; increases with more copies in epidemic
Resource Waste: Buffer overflow, bandwidth consumption, energy expenditure from unnecessary message copies in epidemic routing
TTL (Time-To-Live): Maximum time a message is held and forwarded; expired messages are dropped to free resources
Contact Opportunity: Moment when two DTN nodes are within communication range; the only time for message exchange

102.3 For Beginners: Epidemic Routing

Epidemic Routing - The Gossip Strategy:

Imagine spreading a rumor: tell everyone you meet, and they tell everyone they meet. Eventually, everyone knows!

Epidemic routing copies messages to every node encountered: - Pro: High delivery probability (many copies = high chance one reaches destination) - Con: Massive overhead (100 nodes = 100 message copies!)

Term	Simple Explanation	Everyday Analogy
Epidemic Routing	Give message copy to everyone you meet	Spreading a rumor
Spray and Wait	Create limited copies, then wait for destination	Hand flyers to 10 people, they wait for recipient
TTL (Time-To-Live)	Message expiration timer	Best-before date on food
Buffer Management	Deciding which messages to keep/drop	Cleaning out your inbox

Related Chapters

DTN Series:

DTN Fundamentals - Store-carry-forward basics
DTN Social Routing - Context-aware and social-based routing
DTN Applications - Real-world DTN deployments

Background:

Multi-Hop Fundamentals - Multi-hop communication basics
Ad-hoc Fundamentals - Ad-hoc network basics

Learning:

Simulations Hub - DTN simulators

102.4 Epidemic Routing

⏱️ ~15 min | ⭐⭐ Intermediate | 📋 P04.C01.U02

Epidemic routing is a flooding-based DTN protocol where nodes exchange all buffered packets upon contact, spreading messages like an infectious disease.

102.4.1 Epidemic Routing Algorithm

Epidemic Routing Operation

When two nodes meet:

Exchange summary vectors
- List of packet IDs currently buffered
Request missing packets
- Each node requests packets it doesn’t have
Transfer packets
- Exchange requested packets
Continue movement
- Both nodes now carry additional packets

Result: Packets replicated throughout network until reaching destination

102.4.2 Epidemic Routing Example

Epidemic routing initial state showing source node with message packet encountering first neighbor node, exchanging summary vectors to initiate message replication process — Figure 102.1: Epidemic routing example step 1: Initial node encounters and message exchange

Epidemic routing propagation showing two nodes now carrying message copies, encountering additional neighbors and creating four total message replicas across network — Figure 102.2: Epidemic routing example step 2: Message spreading through opportunistic contacts

Epidemic routing expansion with eight nodes now carrying message replicas, demonstrating exponential growth as each carrier encounters new nodes and exchanges buffered packets — Figure 102.3: Epidemic routing example step 3: Continued message propagation across network

Epidemic routing saturation phase showing majority of network nodes now possess message copies, with continued opportunistic forwarding filling remaining coverage gaps — Figure 102.4: Epidemic routing example step 4: Widespread message distribution

Epidemic routing completion showing destination node successfully received message along with nearly all network nodes carrying replicas, demonstrating high delivery probability at cost of massive message duplication — Figure 102.5: Epidemic routing example step 5: Final delivery achieving high coverage

Detailed Time Steps:

T=0: Packet P1 originates at Node A - A buffer: {P1} - B, C, D buffers: {}

T=1: A encounters B 1. Exchange summary vectors - A has: {P1} - B has: {} 2. B requests P1 3. A transfers P1 to B - A buffer: {P1} - B buffer: {P1} (new copy) - Replication count: 2

T=2: Multiple simultaneous contacts - B encounters C: C receives P1 - A encounters D: D receives P1 - Replication count: 4

T=3: If C is destination - C receives P1 (successful delivery) - But replicas still exist on A, B, D - Message not removed (continues spreading)

102.4.3 Epidemic Routing Control Parameters

1. Delivery Threshold

Stop forwarding after K successful deliveries
Reduces unnecessary replication
Trade-off: delivery confidence vs overhead

2. Time-To-Live (TTL)

Packets expire after TTL hops or seconds
Prevents infinite propagation
Limits resource consumption

3. Buffer Management

FIFO: Drop oldest packets when buffer full
SHLI (Shortest Lifetime First): Drop packets closest to expiration
MOFO (Most Forwarded First): Drop most-replicated packets

4. Replication Limit

Maximum number of copies allowed
Source creates N replicas, carriers don’t replicate further
Controlled flooding

102.4.4 Epidemic Routing Performance

Graph plotting message delivery ratio percentage on y-axis versus number of message replicas on x-axis, showing delivery rate increasing from 60% with 1 replica to 95% with 20 replicas, demonstrating diminishing returns beyond 10 copies — Figure 102.7: Message delivery rate vs number of replicas in epidemic routing

Graph showing message delivery ratio percentage versus TTL hop count, with delivery increasing from 40% at TTL=5 to 90% at TTL=50, then plateauing as longer TTL provides marginal benefit — Figure 102.8: Message delivery rate vs time-to-live (TTL) parameter in epidemic routing

Graph plotting network overhead percentage versus number of replicas, showing exponential growth from 150% overhead with 2 replicas to 1000% overhead with 20 replicas, illustrating bandwidth cost of epidemic routing — Figure 102.9: Network overhead vs number of replicas showing bandwidth consumption

Graph showing network overhead percentage versus TTL value, demonstrating overhead increasing from 200% at TTL=10 to 800% at TTL=100 as longer TTL allows more message propagation and replication — Figure 102.10: Network overhead vs TTL showing message transmission costs

Histogram showing frequency distribution of hop counts for message delivery in epidemic routing, with peak at 3-5 hops and long tail extending to 15+ hops, illustrating varied path lengths in opportunistic forwarding — Figure 102.11: Hop count distribution showing message propagation patterns in epidemic routing

Histogram displaying distribution of buffer occupancy across network nodes, showing most nodes storing 5-15 packets with some highly-connected nodes buffering 30+ messages, demonstrating uneven storage burden in epidemic routing — Figure 102.12: Buffer size distribution across nodes during epidemic routing operation

Epidemic Routing Trade-offs

Advantages:

Optimal delivery ratio: If resources unlimited, achieves 100% delivery
Robust: Multiple copies provide redundancy
Simple: No complex route computation
Latency: Often achieves low delivery latency due to parallelism

Disadvantages:

High resource usage: Massive replication consumes:
- Buffer space (packets stored on many nodes)
- Energy (many transmissions)
- Bandwidth (redundant transfers)
Scalability: Overhead grows exponentially with number of packets/nodes
Contention: Many simultaneous transmissions cause collisions

Performance Metrics:

Table 102.1: Epidemic vs Single-Copy Routing Performance

Metric	Epidemic Routing	Single-Copy Routing
Delivery Ratio	95-99%	60-80%
Average Latency	Low (parallel paths)	High (sequential hops)
Overhead (Transmissions)	500-1000%	100%
Buffer Usage	High (many replicas)	Low (one copy)
Energy Consumption	Very High	Low

Worked Example: Epidemic Routing Buffer Sizing

Scenario: A wildlife tracking network monitors 50 elephants in a reserve. Each collar generates location updates that must reach a base station. Calculate required buffer size for 95% delivery confidence.

Given:

50 elephants with GPS collars
Each collar generates 1 message (200 bytes) per hour
Average inter-encounter time: 4 hours between any two elephants
Messages expire after 72 hours (TTL)
Target: 95% delivery ratio

Steps:

Calculate message generation rate:
- 50 collars x 1 msg/hour = 50 messages/hour network-wide
- Over 72-hour TTL: 50 x 72 = 3,600 messages exist at any time
Estimate replication factor for 95% delivery:
- With 4-hour average encounter time, each message spreads to:
- After 4h: 2 copies (source + 1 encounter)
- After 8h: 4 copies
- After 16h: 8 copies
- After 32h: 16 copies
- After 64h: 32 copies (64% of herd)
- For 95% delivery, need ~40 copies (80% of herd coverage)
Calculate per-node buffer:
- Total message-copies at steady state: 3,600 msgs x 20 avg copies = 72,000 copies
- Distributed across 50 nodes: 72,000 / 50 = 1,440 messages per collar
- Memory: 1,440 x 200 bytes = 288 KB minimum buffer

Result: Each collar needs ~300 KB buffer to support epidemic routing with 95% delivery over 72-hour TTL.

Putting Numbers to It

Buffer requirements grow with the product of message rate, TTL, and replication factor: $B = \frac{R \times T \times C \times S}{N}$ where $R$ is message rate (packets/hour), $T$ is TTL (hours), $C$ is average copies, $S$ is packet size (bytes), and $N$ is node count. Worked example: For 50 collars at 1 msg/hr, 72-hour TTL, 20 average copies, 200 bytes: $B = \frac{1 \times 72 \times 20 \times 200}{50} = 5{,}760$ bytes per collar (~6 KB base load). With network-wide replication adding 282 KB of forwarded messages, total buffer = 288 KB as calculated.

Key Insight: Epidemic routing’s replication causes buffer requirements to scale with (message rate x TTL x replication factor / nodes). For resource-constrained wildlife collars, consider Spray-and-Wait (bounded replication) to reduce buffer needs by 80%+.

Worked Example: DTN Latency Estimation for Rural Connectivity

Scenario: A rural health clinic in Kenya uses bus-based DTN to deliver medical test results to village patients. Calculate expected delivery latency.

Given:

3 buses serve the route daily
Bus route cycle: Village to Hub to Village = 8 hours
Each bus carries DTN node with 50 Mbps Wi-Fi
Clinic uploads results at hub (internet gateway)
Village kiosk downloads when bus passes (30-second contact)
Results file size: 500 KB per patient

Steps:

Calculate worst-case latency:
- Patient test at clinic, uploaded to hub server
- Next bus arrives at hub: 0-8 hours (avg 4 hours)
- Bus downloads pending results: instant (server to bus)
- Bus travels to village: 4 hours
- Total worst case: 8h (wait) + 4h (travel) = 12 hours
- Average case: 4h (wait) + 4h (travel) = 8 hours
Verify contact window sufficiency:
- 30-second contact at 50 Mbps = 1.5 Gb = 187 MB capacity
- If 100 patients have pending results: 100 x 500 KB = 50 MB
- 50 MB << 187 MB: Contact window sufficient
Calculate throughput:
- 3 buses x 187 MB capacity x 2 trips/day = 1.12 GB/day
- Actual demand: ~500 results/day x 500 KB = 250 MB/day
- System has 4.5x capacity margin

Result: Medical results delivered within 8-12 hours average latency, with ample bandwidth margin for growth.

Key Insight: DTN latency = (wait for carrier) + (carrier travel time) + (contact duration overhead). For scheduled carriers like buses, latency is bounded and predictable. This 8-12 hour delay is acceptable for non-emergency medical results but inadequate for urgent cases (which should use cellular/satellite if available).

102.5 Spray-and-Wait Parameter Calculator

Use this calculator to explore how the spray count (L) affects delivery ratio and overhead in Spray-and-Wait routing:

Show code

viewof spray_L = Inputs.range([1, 30], {value: 10, step: 1, label: "Spray copies (L)"})
viewof node_count = Inputs.range([10, 200], {value: 50, step: 5, label: "Network node count"})
viewof ttl_hours = Inputs.range([1, 168], {value: 72, step: 1, label: "TTL (hours)"})

Show code

{
  const delivery = Math.min(99, 60 + 39 * (1 - Math.exp(-spray_L / 8)));
  const overhead = Math.round(spray_L * 100);
  const buffer_kb = Math.round((spray_L * ttl_hours * 200) / node_count);
  return html`
    <div style="display:grid;grid-template-columns:repeat(3,1fr);gap:1rem;margin:1rem 0">
      <div style="background:#eaf4fb;border-left:4px solid #3498DB;padding:1rem;border-radius:4px">
        <div style="font-size:0.85rem;color:#555">Estimated Delivery Ratio</div>
        <div style="font-size:1.8rem;font-weight:bold;color:#2C3E50">${delivery.toFixed(0)}%</div>
      </div>
      <div style="background:#fef9e7;border-left:4px solid #E67E22;padding:1rem;border-radius:4px">
        <div style="font-size:0.85rem;color:#555">Replication Overhead</div>
        <div style="font-size:1.8rem;font-weight:bold;color:#2C3E50">${overhead}%</div>
      </div>
      <div style="background:#eafaf1;border-left:4px solid #16A085;padding:1rem;border-radius:4px">
        <div style="font-size:0.85rem;color:#555">Buffer per Node (200B msgs)</div>
        <div style="font-size:1.8rem;font-weight:bold;color:#2C3E50">${buffer_kb} KB</div>
      </div>
    </div>
    <p style="font-size:0.9rem;color:#555">L=${spray_L} copies across ${node_count} nodes with ${ttl_hours}h TTL. Epidemic routing (L=all nodes) would use ${Math.round(node_count * 100)}% overhead.</p>
  `;
}

102.5.1 Epidemic Routing Optimizations

Spray and Wait:

Phase 1 (Spray): Create L copies
Phase 2 (Wait): Each copy carrier waits for direct destination contact

Limits replication while maintaining good delivery
Balance: L=10 typical for moderate networks

Encounter-Based Routing (EBR):

Use past encounter history to predict future contacts
Forward to nodes with high encounter rate with destination
“Smart” epidemic routing

Prioritized Epidemic Routing (PREP):

Packets have priority levels
High-priority packets replicate aggressively
Low-priority packets replicate conservatively
Application-driven resource allocation

102.6 Knowledge Check

Test your understanding of epidemic routing concepts.

Quiz: Epidemic Routing Bandwidth

Quiz: DTN Contact Window

Worked Example: Wildlife DTN Buffer Sizing for 14-Day Deployment

Scenario: A remote wildlife reserve deploys 30 camera traps that must store images for eventual delivery to ranger station. Calculate buffer requirements for Spray-and-Wait (L=6 copies) over 14-day patrol cycle.

Given:

30 camera traps, each generates 10 images/day (200KB each)
Spray-and-Wait: L=6 copies per image
Patrol visits: Every 14 days (2-week deployment cycle)
TTL: 20 days (longer than patrol cycle for safety margin)
Collar battery capacity limits buffer size to 8MB SRAM

Steps:

Calculate message generation over deployment:
- Per trap: 10 images/day × 14 days = 140 images
- Image size: 200KB
- Total generated: 140 × 200KB = 28MB per trap
Calculate replication overhead with Spray-and-Wait (L=6):
- Each trap creates 6 copies of its own images: 28MB × 6 = 168MB
- Trap also receives copies from neighbors (spray phase)
- Average: Each trap encounters ~8 other traps over 14 days
- Received foreign copies: 8 traps × (140 images / 30 total traps) × 200KB × 6 copies = ~4.5MB
- Total buffer needed: 168MB + 4.5MB ≈ 173MB
Compare against hardware constraint (8MB buffer):
- Required: 173MB
- Available: 8MB
- Shortfall: 165MB (buffer overflow by factor of 21×!)
Re-design with aggressive TTL and replication limits:
- Option A: Reduce L from 6 to 2 copies: 28MB × 2 + 1.5MB = 57.5MB (still 7× too large)
- Option B: Reduce image quality: 200KB → 50KB: (140 × 50KB × 2) + (1.5MB / 4) = 14.4MB (still 1.8× too large)
- Option C: Combined: L=2, 50KB images, TTL=14 days (exact patrol): 14MB + 0.4MB = 14.4MB
- Still exceeds 8MB by 1.8×
Final solution: Store-and-forward with compression:
- Compress images 3:1 (JPEG quality reduction): 50KB → 17KB
- Single copy (no epidemic): 140 × 17KB = 2.4MB
- Buffer for received images: 1MB
- Total: 3.4MB (fits comfortably in 8MB with 4.6MB margin)

Result: Pure epidemic or Spray-and-Wait exceeds hardware constraints by 20-7×. Solution requires aggressive compression (3:1), minimal replication (L=1-2), and direct delivery to ranger during patrol. This demonstrates that DTN replication strategies must account for storage-constrained IoT devices.

Key Insight: Epidemic and Spray-and-Wait assume abundant storage. For constrained IoT (cameras, sensors), single-copy or very limited replication (L=2-3) is often the only viable strategy. Always calculate worst-case buffer occupancy: (messages × TTL × L × size) before deploying DTN protocols.

Decision Framework: DTN Routing Strategy Selection

Choose epidemic, Spray-and-Wait, or context-aware routing based on deployment constraints.

Constraint	Epidemic Routing	Spray-and-Wait	Context-Aware (CAR)
Delivery Ratio Requirement	>95% critical	85-92% acceptable	80-90% acceptable
Energy Budget	Abundant (solar, vehicle)	Moderate (replaceable battery)	Limited (coin cell, 2+ year life)
Buffer Size	Large (GB scale)	Medium (100MB-1GB)	Small (<100MB)
Bandwidth	High (WiFi, LTE)	Medium (Bluetooth, Zigbee)	Low (LoRa, narrowband)
Contact Predictability	None (random)	Low	High (daily routines, schedules)
Network Density	Sparse (< 5 encounters/day)	Medium (5-20 encounters/day)	Any
Overhead Tolerance	500-1000% acceptable	200-400% acceptable	<200% required
Use Case Examples	Disaster response (critical)	Wildlife tracking (moderate)	Smart city wearables (constrained)

Decision Example for Agricultural Drone Data Relay:

Scenario: Drones fly over farmland collecting crop health images, relay via ground-based solar-powered relay stations to farm gateway.

Delivery requirement: 90% sufficient (some image loss tolerable) → Rules out Epidemic
Energy: Relays are solar (abundant), drones are battery (24hr mission) → Moderate budget → Spray-and-Wait or CAR
Buffer: Relay stations have 256MB flash → Medium → Spray-and-Wait or CAR
Bandwidth: 802.11g WiFi between relay and gateway → High → All viable
Contacts: Drones follow predictable flight paths, encounter same relays daily → High predictability → Favors CAR
Overhead: Farm gateway has limited uplink (cellular) → <300% preferred → Favors CAR or Spray-and-Wait

Decision: Spray-and-Wait with L=5 - Rationale: Predictable contacts favor CAR, but implementation complexity and lack of historical data (new deployment) make Spray-and-Wait safer - L=5 provides good delivery (88-92%) with moderate overhead (250-300%) - Fallback: If overhead exceeds cellular budget after deployment, tune to L=3 or migrate to CAR with 2-week learning period

Red Flags:

❌ Epidemic for coin-cell battery devices (will drain in days)
❌ Single-copy for critical alerts (50-60% delivery too risky)
❌ CAR for unpredictable mobility (random walks, ad-hoc disaster networks)

Common Mistake: Using Epidemic Routing with Battery-Powered Nodes

The Mistake: Deploying epidemic routing in sensor networks with battery-powered nodes, leading to battery exhaustion in 3-7 days despite 2-year design life.

Real-World Example: A 2021 smart city deployment used epidemic routing for municipal WiFi-mesh hotspots to relay citizen reports. Battery-powered access points on bus stops depleted batteries in 5 days (expected: 6 months), requiring 50× more frequent maintenance.

Why Epidemic Fails for Battery Devices:

Energy Budget Breakdown:

Epidemic (50 nodes, 100 packets/day):
- Each packet replicated to 30-40 nodes average (network saturation)
- 100 packets × 35 replicas = 3,500 transmissions/day
- Per transmission: 50mW × 100ms = 5mJ
- Daily energy: 3,500 × 5mJ = 17,500mJ = 17.5J/day
- 3000mAh battery (3.6V) = 38,880J total
- Battery life: 38,880J / 17.5J/day = 2,220 days = 6 years? NO!

Reality Check (Why Above Calculation is Wrong): - Epidemic keeps radio always-on for opportunistic contacts: 100mW × 86,400s = 8,640J/day (receive overhead) - Actual daily: 17.5J (transmit) + 8,640J (receive) = 8,657.5J/day - Battery life: 38,880J / 8,657.5J = 4.5 days (not 6 years!)

Quantified Impact:

Expected maintenance (6-month battery): 2 visits/year per node × 50 nodes = 100 visits/year
Actual maintenance (5-day battery): 73 visits/year per node × 50 nodes = 3,650 visits/year (36× increase!)
Labor cost: 3,550 extra visits × $50/visit = $177,500/year extra cost

Solution - Switch to Spray-and-Wait (L=5):

Replication: 100 packets × 5 copies = 500 transmissions/day (vs 3,500)
Transmit energy: 500 × 5mJ = 2.5J/day (vs 17.5J)
Duty-cycle radio: 5% on-time (scheduled contacts) = 432J/day (vs 8,640J always-on)
Daily total: 2.5J + 432J = 434.5J/day
Battery life: 38,880J / 434.5J = 89 days (3 months)
Still not 6 months, but 18× better than epidemic!

Key Lessons:

Epidemic’s receive energy dominates transmit (must stay always-on for opportunistic contacts)
Duty-cycling breaks epidemic (nodes miss contacts, delivery ratio plummets)
Spray-and-Wait enables duty-cycling (bounded replication + scheduled encounters)
For battery IoT, use Spray-and-Wait (L=3-10) or context-aware with <10% duty cycle

Design Rule: If battery life is a constraint, epidemic routing is almost never the answer. Calculate duty-cycle energy cost before deployment.

102.7 Concept Relationships

Concept	Relationship	Connected Concept
Epidemic Flooding	Achieves highest delivery ratio but generates	Massive Replication Overhead
Spray-and-Wait	Bounds replication to L copies reducing	Bandwidth and Energy Consumption
TTL Expiration	Prevents unbounded message propagation by limiting	Message Lifetime
Buffer Management	Handles storage constraints through	FIFO/SHLI/MOFO Eviction Policies
Contact Duration	Limits data transfer capacity during	Brief Opportunistic Encounters

102.8 See Also

DTN Fundamentals - Store-carry-forward foundation
DTN Social Routing - Selective forwarding optimization
DTN Applications - Wildlife tracking and rural connectivity
Ad Hoc Routing: Proactive (DSDV) - Connected network routing comparison
Multi-Hop Fundamentals - Relay forwarding concepts

Interactive Quiz: Match Concepts

Interactive Quiz: Sequence the Steps

Common Pitfalls

1. Confusing Epidemic Routing With Broadcast

Epidemic routing is store-carry-forward — nodes hold messages and transmit them opportunistically when contact occurs. Broadcasting sends messages simultaneously to all in range. In a sparse DTN where nodes rarely meet, epidemic routing may never successfully deliver a message even after days of waiting.

2. Not Bounding Replication in Epidemic Routing

Unconstrained epidemic routing fills all node buffers with copies, leaving no space for new messages. In practice, epidemic routing must be combined with buffer management policies and TTL limits. Pure unbounded epidemic is only theoretical; real implementations need resource bounds.

3. Setting L Too Low in Spray-and-Wait

Spray-and-Wait with L=2 copies may fail to reach the destination if both copy holders never contact the destination before TTL expires. Too-small L reduces epidemic’s delivery advantage. Tune L based on network size, contact frequency, and required delivery probability.

4. Ignoring the Benefit of Asymmetric Contact Opportunities

In real DTN scenarios, some nodes (data mules, buses, frequent travelers) have much more contact with others than sedentary nodes. Epidemic routing treats all contacts equally. Social-aware routing exploits these asymmetries to achieve better delivery with fewer copies.

Label the Diagram

💻 Code Challenge

102.9 Summary

This chapter covered epidemic routing for delay-tolerant networks:

Epidemic Routing Algorithm: Nodes exchange summary vectors upon contact and transfer missing packets, spreading messages like an infectious disease throughout the network
Replication Growth: Messages replicate exponentially (2, 4, 8, 16+ copies) as nodes encounter each other, achieving 95-99% delivery ratios
Control Parameters: TTL limits message lifetime, delivery thresholds stop unnecessary replication, and replication limits cap the number of copies
Buffer Management: FIFO drops oldest packets, SHLI drops packets closest to expiration, and MOFO drops most-replicated packets when buffers fill
Performance Trade-offs: Epidemic routing achieves optimal delivery at the cost of 500-1000% overhead in bandwidth, buffer, and energy consumption
Optimizations: Spray-and-Wait creates bounded L copies, Encounter-Based Routing uses history for smart forwarding, and Prioritized Epidemic Routing allocates resources by application priority

102.10 What’s Next

If you want to…	Read this
Learn DTN fundamentals	DTN Store-Carry-Forward
Explore social routing for smarter DTN	DTN Social Routing
Study DTN applications in practice	DTN Applications
Compare DTN with ad hoc routing	Ad-Hoc Multi-Hop Routing
Review all ad hoc network protocols	Ad Hoc Networks Review