443  WSN Routing Labs and Exercises

443.1 Learning Objectives

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

  • Experiment with Routing Protocols: Compare AODV, DSR, and LEACH in simulation
  • Analyze Energy Consumption: Measure and optimize routing for network lifetime
  • Implement Route Discovery: Practice route request/reply mechanisms
  • Evaluate Protocol Trade-offs: Assess delivery rate, latency, and overhead

443.2 Prerequisites

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

443.3 Comprehensive Review Quiz

Test your understanding of all WSN routing concepts covered in this series.

Question 1: A temperature monitoring WSN has 100 sensors in a 100m x 100m area. Without aggregation, all 100 sensors send readings to the sink every minute (100 transmissions). With tree-based aggregation using 5 intermediate aggregation points, how many total transmissions occur?

  1. 100 transmissions - same as without aggregation since all data must reach the sink
  2. 20 transmissions - dramatic reduction through aggressive compression
  3. 50 transmissions - approximately half due to aggregation benefits
  4. ~105 transmissions - leaves to aggregators (~100) + aggregators to sink (5)
Answer

D) ~105 transmissions - leaves to aggregators (~100) + aggregators to sink (5)

Tree-based aggregation: (1) Leaf nodes to aggregators: 100 sensors organize into 5 subtrees of ~20 sensors each. Each sensor transmits to its aggregator = 100 transmissions. (2) Aggregators to sink: Each of 5 aggregators transmits aggregated result (min/max/avg) to sink = 5 transmissions. Total: 100 + 5 = 105 transmissions. Energy savings: Even though transmission count is similar, aggregation eliminates 95 long-distance transmissions (expensive!), replacing them with short-distance local transmissions (cheap). Real network: 70% energy savings despite similar transmission count.

Question 2: Directed Diffusion uses interest propagation and gradients for data-centric routing. What happens during the gradient establishment phase?

  1. Sink broadcasts data requests and sources send data directly to the sink
  2. Interest floods the network; each node establishes a gradient (direction and rate) pointing toward sinks
  3. Sources advertise available data and sinks subscribe to specific sensors
  4. Network builds shortest-path tree from sink to all nodes using hop count
Answer

B) Interest floods the network; each node establishes a gradient (direction and rate) pointing toward sinks

Directed Diffusion operation: (1) Interest propagation: Sink floods network with interest describing desired data (e.g., β€œtype=temperature, interval=1s, region=area1”). Each node caches interest. (2) Gradient establishment: As interest propagates, each node records from which neighbors it received the interest. This creates gradients - directional state pointing toward interested sinks with associated data rates. Multiple gradients possible (multiple paths to sink). (3) Data propagation: When sources detect matching events, they send data along gradients toward sinks.

Question 3: Link quality estimation uses WMEWMA (Window Mean with EWMA). A link has recent packet delivery: [100%, 100%, 100%, 0%, 0%]. What will WMEWMA estimate compared to pure EWMA?

  1. WMEWMA will respond faster to link failure, dropping estimate quickly when 0% packets arrive
  2. WMEWMA will be slower to respond, maintaining high estimate due to historical data
  3. Both will have identical estimates since they use the same input data
  4. WMEWMA is less accurate - should only use EWMA for link estimation
Answer

A) WMEWMA will respond faster to link failure, dropping estimate quickly when 0% packets arrive

WMEWMA combines two estimators: (1) Window Mean (WM): Average of last N packets (e.g., N=5). Responds quickly to changes - when recent packets fail, estimate drops immediately. WM for [100,100,100,0,0] = (100+100+100+0+0)/5 = 60%. (2) EWMA: Weighted average with parameter a (e.g., a=0.9). Smooths long-term trends but slow to respond. EWMA updates: E1=100%, E2=100%, E3=100%, E4=0.9x100+0.1x0=90%, E5=0.9x90+0.1x0=81%. (3) WMEWMA: Takes minimum of WM and EWMA = min(60%, 81%) = 60%. Why this matters: WM drops quickly when link degrades, avoiding bad routes faster than pure EWMA.

Question 4: Why does the MIN-T metric consider both forward and backward link quality?

  1. To balance network load
  2. Because acknowledgments travel on the backward link
  3. To prevent routing loops
  4. For geographical routing
Answer

B) Because acknowledgments travel on the backward link

MIN-T accounts for both forward (data) and backward (ACK) link quality because a poor backward link will cause ACK losses, triggering retransmissions even if the forward link is good. The formula is: Cost = 1/(P_forward x P_backward).

Question 5: What is the purpose of the suppression mechanism in the Trickle algorithm?

  1. To prevent malicious code propagation
  2. To reduce unnecessary transmissions when neighbors have same code version
  3. To compress the code before transmission
  4. To prioritize critical updates
Answer

B) To reduce unnecessary transmissions when neighbors have same code version

Trickle’s suppression mechanism (based on threshold k) prevents nodes from broadcasting metadata if they’ve heard enough neighbors already broadcasting the same version. This achieves zero maintenance cost when the network is consistent.

Question 6: In Directed Diffusion, what happens during gradient reinforcement?

  1. Physical signal strength is increased
  2. Sinks increase the data rate on preferred paths
  3. New nodes join the network
  4. Energy levels are balanced
Answer

B) Sinks increase the data rate on preferred paths

Gradient reinforcement allows sinks to request higher data rates on paths that deliver data successfully with low latency. This focuses traffic on good paths while letting poor paths expire, adapting the routing to network conditions.

Question 7: WMEWMA combines which two link quality estimation techniques?

  1. RSSI and LQI
  2. Window Mean and Exponentially Weighted Moving Average
  3. Packet delivery rate and hop count
  4. Forward and backward link quality
Answer

B) Window Mean and Exponentially Weighted Moving Average

WMEWMA combines a Window Mean (short-term packet reception count) with EWMA (long-term smoothed average) to balance responsiveness to recent changes with stability against short-term variations.

Question 8: What triggers a Trickle interval reset to tau_min?

  1. Network congestion
  2. Hearing metadata with a newer version
  3. Reaching maximum interval length
  4. Low battery level
Answer

B) Hearing metadata with a newer version

When a node hears metadata with a newer code version than it has, it immediately resets its Trickle interval to tau_min (minimum). This enables rapid propagation of new code through the network.

Question 9: Which metric would hop-count routing consider superior: a 2-hop path with 50% link quality or a 3-hop path with 95% link quality?

  1. 2-hop path (fewer hops)
  2. 3-hop path (better links)
  3. Both are equal
  4. Cannot determine
Answer

A) 2-hop path (fewer hops)

Hop-count routing only considers the number of hops, ignoring link quality. It would choose the 2-hop path even though the 3-hop path would require fewer total transmissions (including retransmissions). This is why hop-count is suboptimal in lossy WSNs.

Question 10: In data aggregation, what does the β€œcompleteness” metric measure?

  1. How accurate the aggregated value is
  2. The percentage of sensor readings included in the aggregate
  3. The time taken to complete aggregation
  4. The compression ratio achieved
Answer

B) The percentage of sensor readings included in the aggregate

Completeness measures what fraction of sensor readings successfully contributed to the aggregated result at the sink. It’s calculated as: Completeness = Readings_Included / Total_Readings. High completeness means the aggregate represents most of the network.

443.4 WSN Routing Lab: Multi-Hop Routing Simulation

~45 min | Advanced | Hands-On Lab

443.4.1 Lab Overview

This hands-on lab simulates a Wireless Sensor Network with multiple ESP32 nodes demonstrating different routing protocols. You’ll experiment with routing decisions, energy-aware path selection, and compare the performance of AODV, DSR, and LEACH protocols in real-time.

What You’ll Learn:

  • How multi-hop routing works in wireless sensor networks
  • Differences between reactive (AODV, DSR) and proactive (LEACH) routing
  • Energy-aware routing decisions and their impact on network lifetime
  • Route discovery, maintenance, and recovery mechanisms
  • Cluster-based routing and aggregation strategies

443.4.2 Lab Setup

The simulation creates a 9-node WSN topology with:

  • 3 Sensor Nodes (S1, S2, S3) - Generate temperature/humidity data
  • 4 Intermediate Nodes (R1, R2, R3, R4) - Forward packets and aggregate data
  • 1 Cluster Head (CH) - Coordinates LEACH protocol
  • 1 Sink Node (SINK) - Destination for all data

Each node has a simulated battery level that depletes with transmission/reception, demonstrating energy-aware routing.

443.4.3 Network Topology

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β”‚ Network Topology                    β”‚
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β”‚                                     β”‚
β”‚   S1 ──┬── R1 ──┬── CH ── SINK     β”‚
β”‚        β”‚        β”‚    β”‚              β”‚
β”‚   S2 ──┼── R2 ───    β”‚              β”‚
β”‚        β”‚        β”‚    β”‚              β”‚
β”‚   S3 ──┴── R3 ──┴── R4              β”‚
β”‚                                     β”‚
β”‚ Links: S1-R1, S1-R2, S2-R2, S2-R3  β”‚
β”‚        S3-R3, S3-R4, R1-R2, R2-R3  β”‚
β”‚        R3-R4, R1-CH, R2-CH, R4-CH  β”‚
β”‚        CH-SINK                      β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

443.4.4 Challenge Exercises

WarningHands-On Challenges

Try these modifications to deepen your understanding:

  1. Energy-Aware Path Selection
    • Modify getNextHop() to prioritize high-energy nodes
    • Implement adaptive energy thresholds based on network-wide energy levels
    • Compare network lifetime with and without energy awareness
  2. Route Quality Metrics
    • Add link quality estimation based on signal strength (RSSI)
    • Implement Expected Transmission Count (ETX) metric
    • Update calculateLinkQuality() to factor in packet loss
  3. LEACH Cluster Rotation
    • Implement probabilistic cluster head election (5% probability)
    • Rotate cluster heads based on residual energy
    • Compare energy consumption across nodes
  4. Multipath Routing
    • Modify DSR to maintain multiple routes
    • Implement route splitting for load balancing
    • Measure improvement in delivery rate
  5. Route Recovery
    • Add RERR (Route Error) packet handling for broken links
    • Implement local route repair before triggering new discovery
    • Measure reduction in route discovery overhead
  6. Protocol Comparison
    • Run each protocol for 2 minutes and record statistics
    • Compare delivery rate, latency, and energy consumption
    • Analyze which protocol performs best under different network densities

443.4.5 Expected Learning Outcomes

After completing this lab, you should be able to:

  • Explain the differences between reactive (AODV, DSR) and proactive (LEACH) routing
  • Identify when energy-aware routing improves network lifetime
  • Analyze the trade-offs between hop count and energy consumption
  • Implement route discovery and maintenance mechanisms
  • Evaluate protocol performance under different network conditions
  • Design hierarchical routing strategies for large-scale WSNs

443.4.6 Key Observations

Protocol Route Discovery Energy Efficiency Scalability Best Use Case
AODV On-demand (flooded RREQ) Moderate Good Mobile, dynamic networks
DSR On-demand (source routing) Low overhead Limited Small networks, stable topology
LEACH Cluster-based (periodic) High (aggregation) Excellent Dense, static sensor deployments
TipReal-World Applications
  • AODV: Smart city IoT with mobile nodes (vehicles, wearables)
  • DSR: Indoor sensor networks with stable topology
  • LEACH: Agricultural monitoring with thousands of static sensors
  • Energy-Aware Routing: Battery-powered environmental monitoring

443.4.7 Further Exploration

To extend this lab:

  • Add geographic routing using GPS coordinates
  • Implement Quality of Service (QoS) routing for priority traffic
  • Simulate network partitioning and reconnection
  • Add data aggregation functions (min, max, median) for LEACH
  • Implement duty cycling where nodes sleep to save energy

443.6 Chapter Summary

This series explored specialized routing approaches for Wireless Sensor Networks:

Key Takeaways:

  1. WSN Routing is Different: Data-centric, energy-aware, application-specific vs traditional address-centric routing

  2. Directed Diffusion: Interest propagation -> gradient establishment -> data delivery -> reinforcement creates efficient data-driven paths

  3. Data Aggregation: Combining data from multiple sensors reduces transmissions, saving energy while maintaining acceptable accuracy

  4. Link Quality Matters: Hop count is insufficient; MIN-T metric accounts for expected retransmissions on lossy links

  5. WMEWMA: Effective link estimation balances short-term responsiveness (Window Mean) with long-term stability (EWMA)

  6. Trickle Algorithm: Polite gossip achieves zero maintenance cost when consistent, rapid propagation when updated

  7. Trade-offs: Message overhead vs latency vs energy consumption requires protocol selection based on application requirements

WSN routing protocols must navigate unique constraints - extreme energy limits, dense deployment, unreliable links - to achieve application objectives efficiently.

443.7 Further Reading

  1. Intanagonwiwat, C., et al. (2003). β€œDirected diffusion for wireless sensor networking.” IEEE/ACM Transactions on Networking, 11(1), 2-16.

  2. Woo, A., Tong, T., & Culler, D. (2003). β€œTaming the underlying challenges of reliable multihop routing in sensor networks.” ACM SenSys, 14-27.

  3. Levis, P., et al. (2004). β€œTrickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks.” USENIX NSDI, 15-28.

  4. Fasolo, E., et al. (2007). β€œIn-network aggregation techniques for wireless sensor networks: A survey.” IEEE Wireless Communications, 14(2), 70-87.

  5. Gnawali, O., et al. (2009). β€œCollection tree protocol.” ACM SenSys, 1-14.

443.8 What’s Next?

Building on these architectural concepts, the next section examines Cloud Computing for IoT deployments.

Continue to Cloud Computing ->

WSN Routing Series: - WSN Routing Overview - Series index - WSN Routing Challenges - Why traditional routing fails - Directed Diffusion - Data-centric routing - Data Aggregation - In-network processing - Link Quality Routing - ETX and metrics - Trickle Algorithm - Network reprogramming

Deep Dives: - Wireless Sensor Networks - WSN architecture - Routing Fundamentals - Core routing concepts - RPL Operation - RPL for IoT