22  WSN Exercises and Resources

Putting Numbers to It

Lab execution time can be estimated before starting runs:

\[ T_{\text{total}} = N_{\text{runs}} \times (t_{\text{setup}} + t_{\text{run}} + t_{\text{review}}) \]

Worked example: With 5 runs and per-run times of 4 min setup, 6 min execution, and 3 min review, total lab time is \(5\times(4+6+3)=65\) minutes. This prevents under-scoping and helps schedule complete experimental cycles.

In 60 Seconds

WSN lab exercises focus on three practical skills: calculating battery lifetime under duty cycling (a 2xAA node at 1% duty cycle lasts ~1.5 years), designing topologies that avoid the hotspot problem (deploy multiple sinks or use LEACH clustering), and comparing routing protocol energy efficiency (hierarchical saves 40-67% over flat routing at 500+ nodes).

Prerequisites: Complete the WSN series

Part of: Wireless Sensor Networks series

22.1 Learning Objectives

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

• Calculate WSN battery lifetime under different duty cycling strategies
• Design network topologies for real-world WSN deployment scenarios
• Compare routing protocols by analyzing their energy efficiency and message overhead
• Create deployment plans with cost analysis and energy budgets for WSN projects

Key Concepts

• Core Concept: Fundamental principle underlying WSN Exercises and Resources — understanding this enables all downstream design decisions
• Key Metric: Primary quantitative measure for evaluating WSN Exercises and Resources performance in real deployments
• Trade-off: Central tension in WSN Exercises and Resources design — optimizing one parameter typically degrades another
• Protocol/Algorithm: Standard approach or algorithm most commonly used in WSN Exercises and Resources implementations
• Deployment Consideration: Practical factor that must be addressed when deploying WSN Exercises and Resources in production
• Common Pattern: Recurring design pattern in WSN Exercises and Resources that solves the most frequent implementation challenges
• Performance Benchmark: Reference values for WSN Exercises and Resources performance metrics that indicate healthy vs. problematic operation

22.2 MVU: Minimum Viable Understanding

If you only have 5 minutes, understand these three things:

1. Duty cycling extends battery life by 100-600x – without it, batteries die in days instead of years
2. The hotspot problem kills WSN deployments – nodes near the sink relay everyone’s traffic and die first
3. Multiple sinks distribute relay load – the simplest architectural fix for network lifetime

Everything else in this chapter builds on applying these principles to real scenarios.

For Beginners: WSN Exercises and Resources

Wireless sensor networks are groups of small, battery-powered devices that cooperate to monitor their surroundings. Think of them as a team of tiny reporters spread across a field, a building, or even a forest, each measuring things like temperature, vibration, or motion and relaying their observations to a central point. Understanding WSNs is fundamental to many IoT applications.

Sensor Squad: WSN Exercises Adventure!

Hey there, future network engineers! Sammy the Sensor has a challenge for you!

Imagine you’re building a sensor network to protect a nature reserve. Sammy and his friends (Lila the Light sensor, Max the Motion detector, and Bella the Battery monitor) need YOUR help figuring out:

• How long will their batteries last? Bella says: “If I sleep most of the time and only wake up to check things, my battery lasts WAY longer – like years instead of days!”
• Where should everyone stand? Max says: “If we all crowd near the ranger station, the sensors in the middle get tired carrying everyone’s messages!”
• How should messages travel? Lila says: “Should everyone shout to the ranger station, or whisper to their neighbors who pass it along?”

Fun Challenge: Try Exercise 1 below and see if you can figure out how long a battery lasts. It is just division – battery size divided by how much power is used!

Worked Example: Battery Lifetime Calculation with Duty Cycling

Scenario: Calculate network lifetime for a 50-node temperature monitoring WSN under three duty cycling strategies.

Given:

• Battery: 2× AA (3,000 mAh at 3V)
• Active current: 20 mA (sensing + radio)
• Idle listening: 15 mA (radio on, not transmitting)
• Sleep current: 1 µA (deep sleep mode)
• Active period per cycle: 1 second (sample + transmit)

Strategy A: No Duty Cycling (Always-On Listening)

• Radio stays in idle listening mode 24/7
• Active 1 second every 60 seconds for transmission
• Average current: 15 mA + (20-15) mA × (1/60) = 15.08 mA
• Battery lifetime: 3,000 mAh / 15.08 mA = 199 hours = 8.3 days

Strategy B: Basic Duty Cycling (1% Active)

• Active 1 second per 100 seconds, sleep otherwise
• Average current: 0.01 × 20 mA + 0.99 × 0.001 mA = 0.20 + 0.001 = 0.201 mA
• Battery lifetime: 3,000 mAh / 0.201 mA = 14,925 hours = 622 days (1.7 years)
• Improvement: 622 / 8.3 = 75× longer than always-on

Strategy C: Aggressive Duty Cycling (0.1% Active)

• Active 1 second per 1,000 seconds, sleep otherwise
• Average current: 0.001 × 20 mA + 0.999 × 0.001 mA = 0.02 + 0.001 = 0.021 mA
• Battery lifetime: 3,000 mAh / 0.021 mA = 142,857 hours = 5,952 days (16.3 years)
• Improvement: 5,952 / 8.3 = 717× longer than always-on

Network-Level Analysis (First Node Death):

• All nodes start with same battery
• Hotspot nodes near sink relay 10× more traffic than edge nodes
• Hotspot average current: 0.201 mA × 10 = 2.01 mA (forwarding neighbors’ data)
• Hotspot lifetime: 3,000 / 2.01 = 1,493 hours = 62 days
• Network fails at 62 days despite edge nodes at 90% battery

Solutions:

1. Multiple sinks: Distribute relay load, extend to 200+ days
2. Rotating cluster heads: Share hotspot burden, achieve near-ideal lifetime
3. Solar-powered relays: Eliminate hotspot problem entirely

Decision Framework: WSN Exercise Selection by Learning Objective

Learning Goal	Exercise Type	Tools Needed	Time Required
Energy calculations	Battery lifetime analysis	Calculator, spreadsheet	30-60 min
Topology design	Network layout planning	Graph paper, NetworkX	1-2 hours
Protocol comparison	Routing simulation	NS-3, Cooja, or Python	2-4 hours
Deployment planning	Cost and coverage analysis	Spreadsheet, mapping tool	2-3 hours

Recommended exercise sequence for beginners:

1. Exercise 1 (Energy): Build intuition about duty cycling impact
2. Exercise 2 (Topology): Understand coverage vs. connectivity trade-offs
3. Exercise 3 (Routing): Compare protocol energy efficiency quantitatively
4. Exercise 4 (Deployment): Integrate all concepts into real-world plan

Tools by complexity:

• Basic (no install): Spreadsheet, calculator, hand calculations
• Intermediate: Python + NetworkX for graph modeling
• Advanced: Cooja/Contiki or NS-3 for packet-level simulation

Grading rubric (educational):

• Correct calculations (40%): Battery lifetime, sensor count, coverage percentage
• Design justification (30%): Why this topology? Why this protocol?
• Trade-off analysis (20%): Energy vs. latency, cost vs. reliability
• Presentation (10%): Clear diagrams, organized report

Common Mistake: Forgetting the Hotspot Problem in Exercises

Misconception: “If I calculate average battery lifetime across all nodes, that’s the network lifetime.”

Reality: In N-to-1 convergecast topologies, the network dies when the first hotspot node fails, not when the average node fails. Edge nodes may have 95% battery remaining when the network becomes disconnected.

Exercise pitfall example:

• Student calculates: 200 nodes × 1-year average lifetime = “Network lasts 1 year”
• Reality: Hotspot nodes (5% of network) fail at 2 months
• Network unusable at 2 months despite avg 90% battery

Correct approach for exercises:

1. Identify hotspot nodes (within 2 hops of sink)
2. Calculate relay burden (how much neighbor traffic they forward)
3. Compute hotspot lifetime separately
4. Network lifetime = min(hotspot lifetime), not average

Quantification:

• Edge node (5 hops from sink): Transmits own data only
• Hotspot node (1 hop from sink): Relays for ~50 nodes behind it
• Hotspot transmissions: 1 (own) + 50 (relay) = 51× more traffic
• Hotspot energy: 51× edge node consumption
• Hotspot lifetime: edge lifetime / 51 = 1 year / 51 = 7 days

Solutions to include in exercises:

• Deploy 3-5 sinks → distributes hotspot load
• Rotate cluster heads → shares burden across all nodes
• Mains-powered relays → eliminates hotspot battery constraint
• Mobile sink → moves periodically, rotating hotspot location

Grading: Deduct points if student reports “network lifetime” without analyzing hotspot problem. Real deployments fail this way constantly.

Test Yourself: Match WSN Lab Concepts

Test Yourself: WSN Energy Analysis Steps

Common Pitfalls

1. Prioritizing Theory Over Measurement in WSN Exercises and Resources

Relying on theoretical models without profiling actual behavior leads to designs that miss performance targets by 2-10×. Always measure the dominant bottleneck in your specific deployment environment — hardware variability, interference, and load patterns routinely differ from textbook assumptions.

2. Ignoring System-Level Trade-offs

Optimizing one parameter in isolation (latency, throughput, energy) without considering impact on others creates systems that excel on benchmarks but fail in production. Document the top three trade-offs before finalizing any design decision and verify with realistic workloads.

3. Skipping Failure Mode Analysis

Most field failures come from edge cases that work in the lab: intermittent connectivity, partial node failure, clock drift, and buffer overflow under peak load. Explicitly design and test failure handling before deployment — retrofitting error recovery after deployment costs 5-10× more than building it in.

🏷️ Label the Diagram

Code Challenge

22.3 Summary

This chapter provided hands-on exercises and worked examples for WSN analysis and design:

• Battery Lifetime Calculation: Duty cycling transforms node lifetime from days (15 mA idle listening) to years (0.2 mA at 1% duty cycle), a 75-717× improvement depending on aggressiveness
• Hotspot Problem: In N-to-1 convergecast networks, nodes near the sink relay all neighbors’ traffic and deplete 10-100× faster than edge nodes; network lifetime equals the first hotspot node death, not the average
• Topology Design Trade-offs: Star topology is simple and low-energy but limited by gateway range; mesh provides redundancy but introduces routing overhead and relay energy costs; cluster-tree balances both
• Routing Protocol Comparison: Hierarchical protocols (LEACH) save 40-67% energy over flat flooding at 500+ nodes by limiting long-distance transmissions
• Deployment Planning: Practical sizing follows: sensor count = area ÷ (spacing²); gateways = sensor count ÷ 50-100; total cost must include hardware, installation labor, connectivity, and maintenance (typically 40-60% above hardware alone)
• Data Aggregation Bugs: A common implementation error is double-counting the cluster head’s reading, inflating reported averages by approximately 10% in 10-node clusters

Apply these skills in the practice exercises above to build confidence before tackling real WSN deployment projects.

Related Chapters & Resources

WSN Deep Dives:

• WSN Coverage - Coverage algorithms and strategies
• WSN Tracking - Object tracking in WSNs
• WSN Overview Review - Comprehensive reference

Wireless Technologies:

• Zigbee - WSN protocol stack
• 802.15.4 - Physical layer
• LoRaWAN - Long-range WSN

Architecture Context:

• Edge Fog Computing - Data processing tiers
• IoT Reference Models - Architecture layers
• Architectural Enablers - Enabling technologies

IoT Products:

Learning Hubs:

• Quiz Navigator - WSN quizzes

22.4 Interactive Quiz: Test Your Understanding

Test your knowledge of wireless sensor networks with this interactive auto-grading quiz.

viewof q1 = Inputs.radio([
  "5.5 days - idle listening drains battery rapidly",
  "45 days - moderate transmission consumption",
  "18 months - sensor design enables long operation",
  "2 years - batteries rated for multi-year use"
], {
  label: "1. A WSN sensor node has a 2000 mAh battery. The radio consumes 15 mA in idle listening mode and 20 mA transmitting. Transmit duty cycle is 1%, but the radio stays in idle mode (not sleep). How long does the battery last?",
  value: null
})

viewof q2 = Inputs.radio([
  "Increase transmission power to reduce hops",
  "Replace hotspot batteries every 3 months",
  "Deploy 3 strategically placed sinks to distribute load",
  "Reduce all nodes' duty cycle equally"
], {
  label: "2. In a 100-node WSN, nodes near the sink deplete batteries 50x faster than edge nodes. After 3 months, these 'hotspot' nodes die while edge nodes have 95% battery. What's the most effective solution?",
  value: null
})

viewof q3 = Inputs.radio([
  "Mesh cannot cover areas >0.5 km²",
  "Full mesh requires expensive enterprise hardware",
  "Mesh provides insufficient reliability",
  "Routing overhead and relay traffic drain batteries"
], {
  label: "3. A smart agriculture WSN covers 1 km² with 200 nodes. The team proposes full mesh topology where every node maintains routing to all others. What's the primary concern?",
  value: null
})

viewof q4 = Inputs.radio([
  "WSNs use wireless; IoT systems require wired connections",
  "WSNs optimize for energy/longevity; IoT prioritizes features/performance",
  "WSNs are obsolete legacy systems being replaced by IoT",
  "No real distinction - terms are interchangeable"
], {
  label: "4. What is the primary design philosophy distinction between Wireless Sensor Networks (WSNs) and general IoT systems?",
  value: null
})

viewof q5 = Inputs.radio([
  "1 second - event must wait until next wake cycle",
  "0.1 seconds - nodes wake during active period",
  "0 seconds - critical events trigger immediate wake",
  "10 seconds - multi-hop propagation delays"
], {
  label: "5. A WSN uses synchronized sleep scheduling (S-MAC) where nodes sleep 90% of time and wake simultaneously every 1 second. A critical event occurs right after nodes sleep. What's the maximum notification delay?",
  value: null
})

viewof checkBtn = Inputs.button("Check Answers")

results = {
  checkBtn;

  const correct = {
    q1: "5.5 days - idle listening drains battery rapidly",
    q2: "Deploy 3 strategically placed sinks to distribute load",
    q3: "Routing overhead and relay traffic drain batteries",
    q4: "WSNs optimize for energy/longevity; IoT prioritizes features/performance",
    q5: "1 second - event must wait until next wake cycle"
  };

  const answers = {q1, q2, q3, q4, q5};
  let score = 0;
  let feedback = [];

  Object.keys(correct).forEach(q => {
    if (answers[q] === correct[q]) {
      score++;
      feedback.push(`✓ Question ${q.substring(1)}: Correct!`);
    } else {
      feedback.push(`✗ Question ${q.substring(1)}: Incorrect`);
    }
  });

  const explanations = {
    q1: "Battery life = 2000 mAh / 15 mA = 133 hours = 5.5 DAYS. This demonstrates the critical problem of idle listening—the radio consuming 15 mA continuously while waiting to receive, nearly as expensive as transmitting (20 mA). Many developers underestimate this, assuming 'not transmitting = low power.' With proper duty cycling (1% active, 99% deep sleep at 1 µA), the same battery lasts: (0.01 × 20 mA + 0.99 × 0.001 mA) ≈ 0.2 mA → 2000/0.2 = 10,000 hours = 416 days! Lesson: Aggressive duty cycling is MANDATORY for WSN deployments.",
    q2: "MULTIPLE SINKS distribute relay traffic across the network. With 3 sinks, each hotspot zone handles ~33% of traffic instead of 100%, extending lifetime from 3 months to 2+ years. Why other options fail: (A) Higher power increases ALL nodes' consumption. (B) Battery replacement is impractical for 100s of remote sensors. (D) Reducing duty cycle helps but doesn't fix fundamental load imbalance—hotspots still relay 50x more. Real example: Agricultural WSN extended from 3 months to 2+ years using 3 sinks. Additional strategies: mobile sinks, energy-aware routing, or mains-powered relay nodes in hotspot zones.",
    q3: "Full mesh imposes SEVERE energy and complexity costs: (1) Routing overhead—each node maintains tables for 199 neighbors, consuming memory and processing. (2) Relay traffic—nodes forward packets for neighbors, draining batteries even when not sensing. (3) Broadcast storms—route discovery floods network with messages. (4) Complexity—mesh protocols (AODV, DSR) are hard to debug. For most applications, simpler topologies provide 90% of benefits at 10% of cost. Use mesh ONLY when: deployment genuinely requires multi-hop beyond gateway range, or critical redundancy justifies cost. Real case: Agricultural WSN switched from mesh to clustered star, reducing energy 70% while maintaining 95% coverage.",
    q4: "WSNs prioritize ENERGY EFFICIENCY as paramount constraint, optimizing every protocol layer for multi-year battery operation. Design decisions: aggressive duty cycling, in-network aggregation, simplified protocols. IoT systems prioritize FUNCTIONALITY and USER EXPERIENCE, often with mains power or frequent charging. Modern convergence: WSN principles at battery-powered sensor tier + IoT infrastructure (cloud platforms, rich apps). Example: Smart agriculture uses WSN techniques (duty cycling, mesh) for sensors but connects to AWS IoT cloud for management. Neither is 'better'—they address different constraints.",
    q5: "With synchronized sleep (S-MAC), nodes wake together every 1 second. If event occurs right after sleep, detecting node must WAIT UP TO 1 SECOND until next wake cycle to transmit. This is the fundamental LATENCY vs. ENERGY trade-off in duty-cycled WSNs. Solutions: (1) Increase duty cycle—wake every 0.1s (10x energy cost). (2) Adaptive listening (T-MAC)—stay awake after receiving packet. (3) Wake-up radios—ultra-low-power radio (10 µA) monitors continuously while main radio sleeps. (4) Asynchronous protocols (B-MAC, X-MAC)—allow anytime transmission with preambles. Application-specific: Fire detection tolerates 1s delay; gas leak may need faster response (use wake-up radio)."
  };

  return {score, total: 5, feedback, explanations, answers, correct};
}

md`### Results: ${results.score}/${results.total} Correct

${results.feedback.map((f, i) => `
${f}

**Explanation:** ${results.explanations[Object.keys(results.explanations)[i]]}

`).join('\n---\n\n')}`

---

22.5 Practice Exercises

Test your understanding of Wireless Sensor Networks with these hands-on exercises.

Exercise 1: Network Lifetime Calculation

Objective: Calculate and compare network lifetime under different duty cycling strategies.

Scenario: You’re deploying a 50-node agricultural WSN. Each node has a 2000 mAh battery and consumes: - Active (sensing + transmitting): 20 mA for 1 second - Idle listening: 15 mA - Sleep mode: 0.01 mA

Tasks:

1. Calculate battery lifetime for a node that transmits every 60 seconds without duty cycling (radio stays in idle listening mode)
2. Calculate lifetime with 1% duty cycle (active 1 second per 100 seconds, sleep otherwise)
3. Determine how long the network maintains 100% coverage (First Node Death metric)
4. Calculate lifetime extension if hotspot nodes near the sink are replaced with solar-powered nodes

Expected Outcome:

• Understand the dramatic impact of duty cycling on battery life (days vs years)
• Recognize the hotspot problem and its effect on network lifetime
• Be able to perform basic WSN energy calculations

Solution Hints:

• Without duty cycling: Average current = 15 mA (idle) + (20 mA × 1s / 60s) ≈ 15.3 mA
• With duty cycling: Average current = (20 mA × 1s / 100s) + (0.01 mA × 99s / 100s) ≈ 0.21 mA
• Lifetime = Battery capacity / Average current
• Hotspot nodes relay traffic, consuming ~5-10× more energy than edge nodes

Quick Check: Battery Lifetime Fundamentals

A sensor node uses 15 mA in idle listening mode continuously (no sleep). With a 2000 mAh battery, approximately how long will it last?

1. About 5 days
2. About 55 days
3. About 1 year
4. About 5 years

Answer

a) About 5 days. Battery life = 2000 mAh / 15 mA = 133 hours = approximately 5.5 days. This demonstrates why idle listening (radio always on) is devastating to battery life – duty cycling is essential to achieve multi-year operation.

Exercise 2: Network Topology Design and Simulation

Objective: Design and simulate different WSN topologies for a real-world scenario.

Scenario: Smart building with 100 temperature/humidity sensors across 3 floors (200m × 50m per floor). Sensors have 50m communication range, 10m sensing range. Gateway located on ground floor center.

Tasks:

1. Calculate minimum number of sensors needed for full coverage (every point within 10m of a sensor)
2. Design three topologies: (a) Star with multiple gateways, (b) Mesh network, (c) Cluster-tree
3. For each topology, calculate: average hop count to gateway, number of relay nodes, maximum node degree
4. Use a network simulator (e.g., Cooja, ns-3, or simple Python script) to model packet delivery under 10% link failure rate
5. Compare energy consumption: count total transmissions per data collection round

Expected Outcome:

• Understand trade-offs between topologies (complexity vs. reliability vs. energy)
• Gain hands-on experience with network simulation tools
• Learn to calculate coverage and connectivity metrics

Tools: Python + NetworkX library for graph modeling, or Cooja/Contiki for WSN simulation

Solution Approach:

• Coverage: Area / (π × Rs²) with overlap factor ≈ 1.2-1.5
• Star: Simple but limited by gateway range, needs multiple gateways
• Mesh: Most reliable but highest energy cost due to route discovery
• Cluster-tree: Good balance, 20-30 nodes per cluster recommended

Exercise 3: Routing Protocol Comparison

Objective: Compare energy efficiency of different routing protocols through analysis and simulation.

Scenario: 100-node WSN with single sink. Compare flooding, geographic routing, and LEACH clustering.

Tasks:

1. Analyze message overhead for each protocol when 10 nodes simultaneously send data to sink
2. Calculate energy consumption: assume 1 unit energy per transmission, 0.5 units per reception
3. Identify bottleneck nodes (hotspots) in each protocol
4. Simulate 100 rounds of data collection and plot: (a) Total network energy, (b) First node death time, (c) Average latency
5. Propose a hybrid protocol combining best features of each

Expected Outcome:

• Understand routing protocol behavior in multi-hop networks
• Learn to identify and analyze hotspot problems
• Develop protocol comparison and optimization skills

Implementation:

# Pseudocode for routing comparison
def flooding(nodes, source, sink):
    messages = 0
    for node in nodes:
        if node.has_data:
            messages += broadcast_to_neighbors(node)
    return messages

def geographic_routing(nodes, source, sink):
    current = source
    hops = 0
    while current != sink:
        next_node = get_closest_to_sink(current.neighbors, sink)
        transmit(current, next_node)
        current = next_node
        hops += 1
    return hops

# Compare total transmissions and energy

Exercise 4: Deployment Planning and Budget Analysis

Objective: Design a complete WSN deployment with cost analysis and energy planning.

Scenario: Precision agriculture for 100-hectare vineyard. Monitor soil moisture, temperature, and humidity. Data collection every 10 minutes. 3-year battery lifetime required.

Tasks:

1. Calculate sensor density: nodes per hectare based on 50m sensing range
2. Select hardware: Arduino + LoRa ($35/node) or ESP32 + Wi-Fi ($25/node + gateway costs)
3. Design network topology and calculate infrastructure needs (gateways, routers)
4. Create energy budget: calculate required battery capacity for 3-year lifetime with 1% duty cycle
5. Calculate total deployment cost: sensors, gateways, installation, connectivity (cellular/Wi-Fi), maintenance
6. Create deployment map showing node placement, cluster heads, and gateway positions

Expected Outcome:

• Learn to translate requirements into WSN design specifications
• Understand cost-performance trade-offs in real deployments
• Gain experience with hardware selection and energy planning

Deliverable: 1-page deployment plan with: - Network diagram - Bill of materials (BOM) with costs - Energy budget spreadsheet - Installation timeline

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22.6 Academic Resource: Smart Grid Power Distribution WSN

Academic Resource: Power Grid Distribution Network with WSN (NPTEL IoT Course)

Smart grid power distribution network showing sensor nodes at substations and homes connected via mesh wireless links

Source: NPTEL Internet of Things Course, IIT Kharagpur

This diagram demonstrates a real-world WSN deployment for smart grid power distribution:

• Generation Layer: Power plant with environmental monitoring sensors
• Transmission Layer: High-voltage substations with fault detection sensors
• Distribution Layer: Residential endpoints with smart meters and demand sensors
• Communication Mesh: Wireless links providing redundant paths for reliable data delivery

Key WSN Design Considerations Illustrated:

Layer	Sensors	Communication	Data Rate
Generation	Temperature, emissions, output	Wired/Fiber	High (1 kbps+)
Transmission	Current, voltage, faults	LoRa/Cellular	Medium (100 bps)
Distribution	Smart meters, demand	Zigbee/Wi-SUN	Low (10 bps)

---

22.7 Test Your Understanding

Test Your Understanding

Question 1: A WSN node has a 3000 mAh battery and consumes 0.3 mA on average with duty cycling enabled. What is the approximate battery lifetime?

1. 10 days
2. 100 days
3. 416 days (about 14 months)
4. 1000 days

Answer

c) 416 days (about 14 months). Battery life = 3000 mAh / 0.3 mA = 10,000 hours = 416 days. This demonstrates how effective duty cycling is at extending battery life from days to over a year.

Question 2: In a 100-node WSN with a single sink, nodes near the sink die 10x faster than edge nodes. What is the most effective architectural solution?

1. Use larger batteries for nodes near the sink
2. Increase transmission power so edge nodes can reach the sink directly
3. Deploy multiple sinks to distribute the relay traffic load
4. Reduce the sensing frequency of all nodes equally

Answer

c) Deploy multiple sinks to distribute the relay traffic load. The hotspot problem is caused by many-to-one traffic patterns where inner nodes relay traffic for all outer nodes. Adding sinks distributes this burden. Larger batteries only delay the problem, higher power increases all energy consumption, and reduced sensing does not fix the load imbalance.

Question 3: Which routing protocol comparison approach gives the most realistic energy efficiency results?

1. Counting only transmission energy per packet
2. Measuring total network energy over multiple data collection rounds
3. Comparing theoretical throughput from protocol specifications
4. Testing with a single source-destination pair

Answer

b) Measuring total network energy over multiple data collection rounds. Real WSN energy consumption includes routing overhead, relay traffic, idle listening, and retransmissions – not just per-packet transmission cost. Multiple rounds reveal hotspot problems and cumulative effects that single-round tests miss.

22.8 Knowledge Check

Quiz: WSN Exercises and Resources

22.9 What’s Next?

Topic	Chapter	Description
WSN Tracking	WSN Tracking Fundamentals	Object and target tracking in wireless sensor networks
WSN Coverage	WSN Coverage	Coverage algorithms and deployment strategies
WSN Review	WSN Overview Review	Comprehensive reference and assessment