248  DSDV Evaluation: Trade-offs and Worked Examples

248.1 Learning Objectives

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

  • Evaluate DSDV Limitations: Identify scenarios where DSDV is unsuitable for IoT deployments
  • Calculate Overhead: Assess the bandwidth and energy cost of maintaining proactive routing tables
  • Compare Proactive vs Reactive: Differentiate DSDV from reactive protocols like DSR and AODV
  • Apply Decision Frameworks: Select appropriate routing protocols based on network characteristics
  • Analyze Real Scenarios: Work through detailed examples of DSDV deployment trade-offs

248.2 Prerequisites

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

248.3 DSDV Limitations for IoT

Estimated Time: ~8 min | Difficulty: Intermediate | Unit: P04.C05.U04

WarningCritical DSDV Drawbacks

1. High Overhead - Periodic route updates consume bandwidth continuously - Every node broadcasts to all neighbors regularly - Overhead grows with network size: O(n^2) messages

2. Power Consumption - Battery-powered IoT nodes must wake up for periodic broadcasts - Listening for neighbor updates drains power - Unsuitable for ultra-low-power duty-cycled nodes

3. Scalability Issues - Large routing tables for networks with hundreds of nodes - Memory-constrained IoT devices may struggle - Update propagation delay increases with network diameter

4. Wasted Effort - Maintains routes to all nodes, even if never contacted - For sparse communication patterns, most routes unused - Many IoT deployments have hub-and-spoke traffic (sensors to gateway)

5. Mobility Overhead - Frequent topology changes trigger many updates - Mobile scenarios generate excessive control traffic - Convergence time can be slow for rapid topology changes

248.3.1 When to Use DSDV

Good Fit: - Network relatively static - Most nodes communicate frequently - Network size is moderate (<50-100 nodes) - Power budget allows continuous operation - Latency-sensitive applications (routes pre-computed)

Poor Fit: - Battery-powered sensors - Sparse communication patterns - Large networks (>100 nodes) - High mobility environments - Duty-cycled sleep schedules

248.4 Proactive vs Reactive Trade-offs

Question: DSDV (proactive routing) maintains routing tables continuously, while DSR (reactive) discovers routes on-demand. For a WSN with 100 nodes where only 10% communicate actively, which is more efficient?

DSDV overhead: 100 nodes x periodic updates (every 15s) = 100 messages every 15s = 400 messages/minute network-wide, even for inactive nodes. DSR overhead: 10 active nodes x route discovery (only when needed) = 10-20 messages total. Battery impact: DSDV drains all nodes continuously, DSR only uses energy when communication occurs. Trade-off: DSDV has zero discovery latency (routes pre-computed), DSR has discovery delay but massive energy savings.

248.5 Worked Examples

248.5.1 Example 1: Route Selection with Competing Paths

NoteWorked Example: DSDV Route Selection

Scenario: A smart factory uses DSDV routing for real-time machine monitoring. Node M (machine sensor) needs to send an alert to Node G (gateway). Node M has received two route advertisements from different neighbors:

Given: - Route 1 (via Node A): Destination G, Next Hop A, 3 hops, Sequence Number 156 - Route 2 (via Node B): Destination G, Next Hop B, 2 hops, Sequence Number 152 - Node M needs to select the best route and update its routing table

Steps:

  1. Compare sequence numbers first (DSDV’s primary criterion):

    • Route 1: seq = 156
    • Route 2: seq = 152
    • Route 1 has higher sequence number (156 > 152)

  2. Apply DSDV selection rule:

    • Higher sequence number = fresher route information
    • Route 1 (seq=156) reflects more recent network topology
    • Route 2 (seq=152) is 4 update cycles old and may include stale links

  3. Do NOT compare hop counts (sequences differ):

    • Even though Route 2 is shorter (2 hops vs. 3 hops)
    • Hop count is only compared when sequence numbers are equal

  4. Install Route 1 in routing table:

    +-------------+-----------+-------+--------+
| Destination | Next Hop  | Hops  | Seq #  |
+-------------+-----------+-------+--------+
| Node G      | Node A    | 3     | 156    |
+-------------+-----------+-------+--------+

Result: Node M selects Route 1 (via A, 3 hops, seq=156) despite Route 2 being shorter. The sequence number 156 guarantees this route reflects the current network state.

Key Insight: DSDV’s “freshness first” rule prevents routing loops caused by stale information. A longer but current route is always safer than a shorter but outdated route.

248.5.2 Example 2: Overhead Calculation for IoT Deployment

NoteWorked Example: DSDV Overhead Calculation

Scenario: An industrial IoT deployment manager needs to evaluate whether DSDV is suitable for a sensor network monitoring a chemical plant.

Given: - Network size: 80 sensor nodes - DSDV update interval: 15 seconds - Routing table entry size: 12 bytes (destination, next-hop, hops, seq#) - Wireless link capacity: 250 kbps (typical 802.15.4) - Each node has routes to all other nodes: 79 entries - Maximum acceptable control overhead: 15% of link capacity

Steps:

  1. Calculate routing table size per node:
    • Entries: 79 (routes to all other nodes)
    • Size per entry: 12 bytes
    • Total table size: 79 x 12 = 948 bytes
  2. Calculate update frequency per node:
    • Update interval: 15 seconds
    • Updates per minute: 60/15 = 4 updates/minute
    • Updates per hour: 4 x 60 = 240 updates/hour
  3. Calculate network-wide broadcast overhead:
    • Each node broadcasts 948 bytes every 15 seconds
    • Per node per minute: 948 x 4 = 3,792 bytes/minute
    • Network total: 80 nodes x 3,792 = 303,360 bytes/minute
    • Converting to bits: 303,360 x 8 = 2,426,880 bits/minute
    • Per second: 2,426,880 / 60 = 40,448 bps
  4. Calculate overhead percentage:
    • Link capacity: 250,000 bps
    • Control overhead: 40,448 bps
    • Percentage: 40,448 / 250,000 = 16.2%
  5. Compare to threshold:
    • Calculated: 16.2%
    • Acceptable: 15%
    • DSDV exceeds acceptable overhead by 1.2%

Result: DSDV routing overhead is 16.2% of link capacity, exceeding the 15% threshold. Options include: (1) increase update interval to 20 seconds (reduces overhead to 12.1%), (2) use incremental updates instead of full table broadcasts, or (3) switch to reactive routing if traffic is sparse.

Key Insight: DSDV overhead scales as O(N^2) - both table size and number of broadcasting nodes grow with N. For networks exceeding 50-100 nodes, carefully calculate overhead.

248.5.3 Example 3: Energy-Aware Route Selection

NoteWorked Example: Energy-Aware Routing Under Battery Constraints

Scenario: A precision agriculture deployment uses 45 soil sensors across a 20-hectare vineyard. After 6 months of operation, several central relay nodes have critically low batteries.

Given: - 45 sensor nodes, 3 gateway nodes - Current routing: DSDV with pure hop-count metric - Node battery status after 6 months: - 12 nodes: >80% battery (peripheral sensors) - 18 nodes: 50-80% battery (moderate traffic) - 12 nodes: 20-50% battery (busy relay nodes) - 3 nodes: <20% battery (critical central relays: N7, N12, N23) - Critical relay node N12 forwards 40% of all network traffic - Path A (current): Sensor S - N5 - N12 - Gateway (3 hops, N12 at 15% battery) - Path B (alternate): Sensor S - N8 - N15 - N22 - Gateway (4 hops, minimum battery 65%)

Steps:

  1. Calculate current path energy distribution:
    • Path A (3 hops): N5, N12, Gateway
    • N12 energy/day: 2,880 packets x 1.2mJ = 3.46J
    • N12 also relays for 40% of network: Total drain ~15J/day
  2. Estimate remaining lifetime on Path A:
    • N12 battery: 15% of 2000mAh = 3,888J (at 3.6V)
    • Remaining days: 3,888J / 15J = 259 days before N12 dies
    • N12 dying partitions network into 3 isolated segments!
  3. Design energy-aware routing metric:
    • Modified metric: cost = hop_count x (1 / remaining_battery^2)
    • Path A cost: 3 x (1/0.15^2) = 3 x 44.4 = 133.3
    • Path B cost: 4 x (1/0.65^2) = 4 x 2.37 = 9.5
    • Path B is 14x cheaper despite being 1 hop longer!
  4. Project network lifetime improvement:
    • Without energy-aware routing: N12 dies in 259 days
    • With energy-aware routing: Offload N12 traffic to Path B
    • N12 new drain: 9J/day (60% of original) - 432 days remaining

Result: Energy-aware DSDV extends network lifetime from 259 days to 432+ days (67% improvement) by routing traffic away from battery-critical nodes.

Key Insight: Standard DSDV’s hop-count metric optimizes for latency, not lifetime. Incorporating residual energy into the routing metric dramatically improves lifetime.

248.5.4 Example 4: Convergence Time After Major Topology Change

NoteWorked Example: DSDV Convergence Time Calculation

Scenario: A smart city traffic monitoring network uses DSDV routing among 60 intersection nodes. A construction project disables 5 adjacent nodes (N15-N19) simultaneously.

Given: - 60 intersection nodes in grid topology - DSDV update interval: 15 seconds (periodic full dump) - Triggered update delay: 200ms (incremental on topology change) - Network diameter: 8 hops (longest shortest path) - Nodes disabled: N15, N16, N17, N18, N19 (contiguous block) - Routes affected: 23 nodes had paths through disabled region - Packet processing time: 5ms per routing table entry - Wireless propagation: ~1ms per hop

Steps:

  1. Calculate triggered update propagation:
    • t=0ms: N15-N19 go offline
    • t=100ms: Neighboring nodes detect missing HELLO
    • t=200ms: Neighbors generate triggered updates with infinity metric
  2. First wave of route invalidation:
    • t=200ms: N10 broadcasts: <N15, infinity, seq+1>, ...
    • Processing time at each recipient: 5 entries x 5ms = 25ms
    • Propagation to next hop: 1ms
    • t=226ms: 8 second-tier neighbors receive invalidation
  3. Calculate full convergence time:
    • Maximum distance from disabled zone to affected node: 6 hops
    • Per-hop delay: 25ms processing + 1ms propagation = 26ms
    • Invalidation complete: t = 200ms + (6 x 26ms) = 356ms
  4. Calculate new route discovery time:
    • New routes propagate from edge nodes toward center
    • Maximum propagation: 4 hops from alternate route source
    • New routes installed: t = 356ms + (4 x 26ms) = 460ms
  5. Consider periodic update alignment:
    • Worst case: Must wait for next periodic cycle (15 seconds)
    • Worst-case convergence: 15.2 seconds
  6. Calculate packet loss during convergence:
    • Traffic rate: 10 packets/second through affected routes
    • Best case (460ms): ~5 packets lost
    • Worst case (15.2s): 152 packets lost

Result: DSDV converges in 460ms best-case (triggered updates) or 15.2 seconds worst-case (periodic updates).

Key Insight: For critical infrastructure, reduce periodic update interval (e.g., 5 seconds) and implement reliable triggered updates.

248.6 Knowledge Check

Question: For a highly mobile network where nodes exchange messages frequently and require sub-100 ms reaction time (e.g., collision avoidance), which routing approach is generally the better fit?

When communication is continuous and latency constraints are tight, proactive protocols reduce per-message delay because forwarding can start immediately using precomputed routes (at the cost of periodic control traffic).

Scenario: You’re deploying a fleet of 50 delivery drones that must coordinate routes in real-time to avoid collisions. Drones communicate within 200m range and move at 10 m/s. Communication is frequent (every 2 seconds).

Think about: 1. Should you use DSDV (proactive) or DSR (reactive) routing for this network? 2. What are the energy and latency trade-offs? 3. How does mobility affect your routing protocol choice?

Key Insight: DSDV is well-suited for this scenario despite its overhead. Why? (1) Frequent communication (every 2s) means reactive route discovery would occur almost continuously. (2) Low latency requirement - collision avoidance requires <100ms response time. DSDV provides instant forwarding. (3) Mains-powered or frequently-recharged drones can afford the energy overhead.

248.7 Common Misconceptions

Misconception: In DSDV, the route with the fewest hops is always selected as the best path.

Reality: DSDV prioritizes sequence numbers over hop count. A route with a higher sequence number will be selected even if it has significantly more hops.

Why This Matters:

Route A Route B
2 hops 5 hops
seq=100 seq=105
Older Fresher

DSDV selects Route B because: 1. Sequence 105 reflects more recent topology 2. Route A might traverse failed links detected at seq=101-104 3. A longer but current path beats a shorter broken path

Selection Algorithm:

if (seq_A > seq_B): select A
elif (seq_A == seq_B):
    if (hops_A < hops_B): select A
    else: select B
else: select B

Impact: In mobile networks, prioritizing freshness reduces packet loss from 15-20% to <2%.

248.9 Summary

This chapter covered DSDV evaluation, trade-offs, and practical application:

  • Key Limitations: High overhead (O(n^2)), power consumption, scalability issues, and wasted effort maintaining unused routes
  • Optimal Use Cases: Static networks with frequent communication, moderate size (<50-100 nodes), adequate power budgets, and latency-sensitive applications
  • Overhead Calculation: Control traffic grows quadratically with network size; calculate before deploying
  • Energy-Aware Extensions: Incorporating battery levels into routing metrics can extend network lifetime by 67%+
  • Convergence Analysis: Best-case convergence uses triggered updates (sub-second); worst-case waits for periodic interval
  • Protocol Selection: Choose DSDV for frequent, latency-critical traffic; choose reactive (DSR) for sparse, energy-constrained deployments

Deep Dives: - DSDV Fundamentals - Routing tables and sequence numbers - DSDV Protocol Operation - Update mechanisms and topology changes - Ad Hoc Routing: Reactive (DSR) - On-demand routing for sparse traffic

Comparisons: - Ad Hoc Routing: Hybrid (ZRP) - Combining proactive and reactive approaches - Routing Fundamentals - Distance-vector vs link-state routing - RPL Fundamentals - Proactive routing for low-power networks

Implementation: - Ad-hoc Production and Review - Production frameworks and multi-path routing - Ad-hoc Labs and Quiz - Protocol simulation exercises

Interactive Learning: - Simulations Hub - Network topology visualizer for comparing DSDV routing patterns - Knowledge Map - Explore relationships between routing protocols

Common Challenges: - Knowledge Gaps Hub - Clarifications on proactive vs reactive routing trade-offs

Video Resources: - Videos Hub - Visual explanations of distance-vector algorithms

248.10 What’s Next

The next chapter explores Ad Hoc Routing: Reactive (DSR), covering on-demand route discovery, route caching strategies, and how reactive protocols reduce overhead for sparse communication patterns while tolerating initial discovery delays.