406  WSN Tracking: Comprehensive Review

406.1 Learning Objectives

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

  • Compare Tracking Formulations: Evaluate push, poll, and guided tracking for different scenarios
  • Apply Tracking Components: Implement detection, cooperation, and position computation algorithms
  • Design Multimedia Networks: Plan WMSN architectures combining scalar and camera sensors
  • Understand Underwater Acoustics: Apply acoustic propagation models for UWASN deployments
  • Optimize Energy: Use prediction-based sensor activation for energy-efficient tracking
  • Test Comprehension: Validate understanding through summaries and quiz questions

406.2 Prerequisites

Required Chapters: - WSN Tracking Fundamentals - Core tracking concepts - WSN Overview - Wireless sensor networks - Location Awareness - Positioning concepts

Technical Background: - Trilateration/triangulation - RSSI-based localization - Time-of-arrival concepts

Tracking Methods:

Method Accuracy Infrastructure Power
GPS 3-5 m Satellites High
Wi-Fi RSSI 5-15 m Access points Medium
BLE Beacons 1-3 m Beacons Low
UWB 10-30 cm Anchors Medium

Estimated Time: 1 hour

Tracking Series: - WSN Tracking Fundamentals - Core tracking concepts - WSN Tracking Verticals and Applications - Specialized tracking - WSN Tracking Implementation - Hands-on labs

Related WSN: - WSN Overview Fundamentals - WSN architecture - WSN Coverage Fundamentals - Coverage and positioning - Wireless Sensor Networks - Network design

Localization: - Location Awareness - Positioning systems - GPS and Positioning - Global positioning

Applications: - Application Domains - Tracking use cases

Learning: - Quizzes Hub - Tracking assessments - Simulations Hub - Tracking simulations - Videos Hub - Tracking videos

This chapter is a capstone review for wireless sensor network (WSN) tracking. It assumes you are already comfortable with the basics from:

  • wsn-overview-fundamentals.qmd – what WSNs are, typical node roles, and energy constraints.
  • wsn-tracking-fundamentals.qmd – push vs poll vs guided tracking, localization methods, and Kalman/particle filters.
  • wireless-sensor-networks.qmd – broader architectural context for large sensor deployments.

Use this review to connect and test those ideas rather than to learn them for the first time:

  • Many explanations and MCQs reference tracking components, WSN energy trade‑offs, and special environments (underwater, multimedia, nanonetworks).
  • If terms like range‑based localization, expanding ring search, or particle filter feel unfamiliar, pause here and revisit the fundamentals chapters first.
  • Beginners can still skim this chapter for inspiration, but treat it as a summary plus challenge set rather than an introductory tutorial.

406.3 WSN Tracking System Architecture

⏱️ ~20 min | ⭐⭐⭐ Advanced | 📋 P05.C38.U01

Difficulty: ⭐⭐⭐ Advanced | Prerequisites: Tracking fundamentals, localization methods

A complete WSN tracking system integrates multiple components working together to detect, localize, and follow mobile targets.

Real-World Example: Amazon warehouse tracking system (2023) deployed 50,000 sensors across 800,000 ft² facility: - Detection Layer: 12,000 RFID readers + BLE beacons achieving 99.8% asset detection - Cooperation Layer: 800 cluster heads performing real-time data fusion at 10Hz update rate - Energy Impact: Predictive activation reduced active sensors from 50,000 to 18,000 (64% savings), extending battery life from 3 months to 14 months - Cost Savings: $2.4M/year in battery replacement + labor vs always-on deployment - Performance: <500ms target acquisition, 1.2m average tracking accuracy

Graph diagram

Graph diagram
Figure 406.1: Five-layer WSN tracking system architecture. Detection Layer has three sensor nodes (Node 1: RSSI -65 dBm, Node 2: -58 dBm, Node 3: -72 dBm in teal) sending detection reports to Cooperation Layer. Cooperation Layer contains Cluster Head performing data fusion and Predictive Activation controlling wake-up (orange). Cluster Head sends fused measurements to Processing Layer. Processing Layer has Trilateration computing positions, Kalman Filter estimating state, and Trajectory Prediction forecasting future paths (navy). Processing outputs feed Management Layer containing Energy Manager for adaptive sampling, Handoff Controller for cluster transfer, and Recovery Module for target reacquisition (gray). Management sends control commands back to Cooperation Layer. Finally, Kalman Filter sends track data to Base Station Dashboard and Trajectory Prediction triggers Alert System in Output Layer (orange). Predictive Activation sends wake commands back to sensor nodes via dashed line. Complete data flow shows real-time target tracking with energy optimization and recovery mechanisms. 

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flowchart LR
    subgraph Input["Raw Data"]
        R1[RSSI Readings]
        R2[ToA Timestamps]
        R3[Sensor IDs]
    end

    subgraph Stage1["Stage 1: Fusion"]
        F1[Data Aggregation]
        F2[Noise Filtering]
    end

    subgraph Stage2["Stage 2: Position"]
        P1[Trilateration]
        P2[Error Bounds]
    end

    subgraph Stage3["Stage 3: State"]
        S1[Kalman Update]
        S2[Velocity Est.]
    end

    subgraph Stage4["Stage 4: Predict"]
        PR1[Trajectory Forecast]
        PR2[Wake Commands]
    end

    subgraph Output["Actions"]
        O1[Track Display]
        O2[Alerts]
        O3[Sensor Control]
    end

    Input --> Stage1
    Stage1 --> Stage2
    Stage2 --> Stage3
    Stage3 --> Stage4
    Stage4 --> Output

    style Stage1 fill:#16A085,stroke:#2C3E50,color:#fff
    style Stage2 fill:#E67E22,stroke:#2C3E50,color:#fff
    style Stage3 fill:#2C3E50,stroke:#16A085,color:#fff
    style Stage4 fill:#7F8C8D,stroke:#2C3E50,color:#fff

This pipeline view emphasizes the sequential data transformation from raw sensor readings to actionable outputs. Raw RSSI/ToA data flows through four processing stages: (1) Fusion aggregates and filters noisy measurements, (2) Position computes target location via trilateration with error bounds, (3) State applies Kalman filtering for velocity estimation and uncertainty tracking, (4) Prediction forecasts future trajectory and generates sensor wake commands. The pipeline produces three output types: real-time track display, event-triggered alerts, and proactive sensor control for energy-efficient tracking.

WSN Tracking System Architecture (Five-Layer Model)

Layer Components Function Data Flow
Detection Sensor Node 1 (RSSI: -65 dBm), Node 2 (-58 dBm), Node 3 (-72 dBm) Target detection via signal strength Detection Reports → Cooperation
Cooperation Cluster Head (Data Fusion), Predictive Activation (Wake-up Control) Aggregate measurements, coordinate sensors Fused Measurements → Processing
Processing Trilateration (Position), Kalman Filter (State), Trajectory Prediction (Future) Compute position, estimate state, predict path Position/Velocity → Management
Management Energy Manager (Adaptive Sampling), Handoff Controller, Recovery Module Optimize energy, transfer clusters, recover lost targets Control Commands → Cooperation
Output Base Station (Dashboard), Alert System (Event Triggers) Display tracks, trigger alerts Track Data to operators

Data Flow Between Layers:

Source Destination Data Type
Sensor Nodes Cluster Head Detection reports (RSSI values)
Cluster Head Trilateration Fused measurements (aggregated RSSI)
Trilateration Kalman Filter Position estimates (x, y coordinates)
Kalman Filter Trajectory Prediction State vector (position + velocity)
Trajectory Prediction Predictive Activation Predicted path (wake-up regions)
Predictive Activation Sensor Nodes Activation commands (sleep/wake)
Energy Manager Cluster Head Sampling rate adjustments
Handoff Controller Recovery Module Target lost trigger
Recovery Module Predictive Activation Search pattern (expanding ring)
Kalman Filter Base Station Real-time track data
Trajectory Prediction Alert System Event triggers (boundary crossing)

Architecture Components:

  1. Detection Layer: Distributed sensor nodes detect target presence through RSSI, motion sensors, or cameras
  2. Cooperation Layer: Cluster heads aggregate measurements and coordinate sensor activation
  3. Processing Layer: Trilateration computes positions, Kalman filter estimates state, prediction forecasts trajectory
  4. Management Layer: Energy optimization, handoff coordination, and target recovery mechanisms
  5. Output Layer: Base station monitoring and event-driven alerting

406.4 Tracking Algorithm Comparison {#arch-wsn-track-rev-algorithm-comparison} ⭐⭐

Difficulty: ⭐⭐ Intermediate | Time to Master: 30 minutes

Different ranging techniques provide varying trade-offs between accuracy, cost, and complexity for target localization.

Real-World Deployment Data (Smart Building 2024): - RSSI deployment: 200 sensors @ $15/unit = $3,000 total, 5-8m accuracy, 24-month battery life - ToA/UWB deployment: 80 sensors @ $85/unit = $6,800 total, 0.5-1m accuracy, 18-month battery life - Hybrid (RSSI + 20 UWB anchors): 220 total sensors @ $4,700, 2m average accuracy, 22-month lifetime - Result: Hybrid approach chosen - 30% cost vs pure UWB, 2.5× better accuracy vs pure RSSI

Graph diagram

Graph diagram
Figure 406.2: Tracking Algorithm Comparison showing four ranging techniques (RSSI, ToA, TDoA, AoA) with input measurements, calculation methods, and output accuracy ranges. RSSI uses signal power (-65 dBm) with path loss model achieving 5-10m accuracy at lowest cost. ToA uses arrival time (333ns) with speed of light calculation achieving 0.5-2m accuracy at highest cost. TDoA uses time differences (67ns) with hyperbolic positioning achieving 0.5-2m accuracy. AoA uses arrival angles (45°, 30°) with triangulation achieving 1-5m accuracy. Diagram includes cost ranking (RSSI < AoA < TDoA < ToA) and accuracy ranking (ToA/TDoA > AoA > RSSI) showing fundamental trade-offs between ranging techniques.

Tracking Algorithm Comparison (Four Ranging Techniques)

Algorithm Measurement Computation Accuracy Cost
RSSI (Received Signal Strength) Signal Power (-dBm) Path Loss Model: d = 10^((P_tx - RSSI)/(10n)) 5-10 m Free (built-in)
ToA (Time of Arrival) Arrival Time (t) Distance: d = c × t (speed of light) 0.5-2 m High
TDoA (Time Difference of Arrival) Relative Times (Δt = t2 - t1) Hyperbolic: d2 - d1 = c × Δt 0.5-2 m Medium-High
AoA (Angle of Arrival) Arrival Angle (θ) Triangulation: 2+ angle intersections 1-5 m Medium

RSSI Characteristics:

Aspect Details
Performance No sync required, free hardware, works with any radio
Limitations Multipath interference, environmental variance, obstacle attenuation
Best For Room-level localization, proximity detection, cost-sensitive deployments

ToA Characteristics:

Aspect Details
Performance High precision, first path detection, sub-meter accuracy
Limitations Nanosecond synchronization required, clock drift, expensive hardware
Best For Precision tracking, robotics, industrial automation

TDoA Characteristics:

Aspect Details
Performance No absolute sync needed, multipath robust, GPS-like accuracy
Limitations Relative sync required, complex hyperbolic geometry, needs 4+ receivers
Best For GPS-like positioning, multilateration systems, cellular localization

AoA Characteristics:

Aspect Details
Performance Range independent, 2D positioning from 2 anchors
Limitations Directional antennas required, calibration needed, angular resolution limits
Best For Direction finding, sector-based tracking, radar applications

Selection Criteria Summary:

Criterion Ranking (Best → Worst)
Cost RSSI < AoA < TDoA < ToA
Accuracy ToA/TDoA > AoA > RSSI
Complexity RSSI < ToA < AoA < TDoA

Algorithm Selection Guidelines:

  1. RSSI (Received Signal Strength)
    • Best for: Room-level localization, proximity detection, cost-sensitive deployments
    • Accuracy: 5-10 meters (environment-dependent)
    • Hardware: Built into all radios (zero additional cost)
    • Challenge: Multipath, obstacles, interference cause high variance
  2. ToA (Time of Arrival)
    • Best for: Precision tracking, robotics, industrial automation
    • Accuracy: 0.5-2 meters with nanosecond synchronization
    • Hardware: Precision oscillators, UWB radios, or GPS synchronization
    • Challenge: Requires absolute time synchronization across network
  3. TDoA (Time Difference of Arrival)
    • Best for: GPS-like positioning, multilateration systems, cellular localization
    • Accuracy: 0.5-2 meters (similar to ToA)
    • Hardware: Synchronized receiver pairs, reference stations
    • Challenge: Complex hyperbolic geometry, needs 4+ receivers in 3D
  4. AoA (Angle of Arrival)
    • Best for: Direction finding, sector-based tracking, radar applications
    • Accuracy: 1-5 meters (depends on baseline and range)
    • Hardware: Directional antennas or antenna arrays, phase measurement
    • Challenge: Calibration complexity, limited by angular resolution

Hybrid Approaches:

Modern systems often combine multiple techniques (e.g., RSSI + ToA, or TDoA + AoA) to leverage complementary strengths and improve overall accuracy.

NoteCross-Hub Connections: Tracking Resources

Interactive Learning: - Simulations Hub: Explore Network Topology Visualizer to understand sensor deployment patterns for tracking coverage - Videos Hub: Watch tracking demonstrations showing RSSI vs ToA accuracy in real environments - Quizzes Hub: Test your understanding of trilateration, ranging techniques, and energy optimization

Hands-On Practice: - Simulate predictive activation algorithms and measure energy savings - Compare RSSI path loss models in different environments (indoor vs outdoor) - Implement Kalman filter tracking for linear vs non-linear target motion - Design expanding ring search patterns and calculate recovery time

Knowledge Validation: - Knowledge Gaps: Review common misconceptions about localization accuracy and energy trade-offs - Take tracking system design quiz covering algorithm selection, cluster handoff, and multi-hop energy optimization

WarningCommon Misconception: “More Sensors Always Improve Tracking Accuracy”

The Misconception: Students often believe that doubling the number of tracking sensors will double positioning accuracy, following a “more is better” intuition.

The Reality: Tracking accuracy improves with the square root of sensor count, not linearly. Quantified impact: Going from 3 sensors (minimum for 2D trilateration) to 6 sensors improves RSSI accuracy from ±8m to ±5.7m (only 29% improvement, not 2×). Going from 6 to 12 sensors improves to ±4m (30% additional improvement). Diminishing returns set in quickly.

Real-World Example: A warehouse tracking system deployed 100 sensors expecting sub-meter accuracy. Actual performance: ±4m accuracy with $50,000 investment. Analysis revealed: - Theoretical limit: RSSI multipath creates ±3m fundamental noise floor regardless of sensor density - Measurement: Each sensor contributes -65 dBm ± 8 dBm variance (128× power range!) - Geometry: All 100 sensors formed poor triangulation geometry (collinear along aisles) - Interference: Dense deployment caused self-interference, degrading RSSI measurements

Corrective Approach: Redesigned with 20 strategically-placed sensors ($10,000 cost): - Optimized geometry: sensors at room corners provide 90° triangulation angles (best) - UWB upgrade: switched to UWB ranging achieving ±0.8m accuracy (5× better than 100 RSSI sensors) - Total savings: $40,000 + 80% reduction in maintenance

Key Insight: Algorithm choice (RSSI vs ToA/UWB) and sensor geometry matter far more than sensor count. RSSI has fundamental physical limits (multipath, shadowing) that cannot be overcome by adding sensors. Investing in higher-quality ranging (ToA, UWB) provides better accuracy than densifying RSSI sensors. Focus on strategic placement over brute-force coverage.

406.6 Summary

Target tracking with wireless sensor networks enables continuous monitoring across diverse environments and scales.

Graph diagram

Graph diagram
Figure 406.3: Three WSN tracking formulations comparison. Push-Based Tracking (orange): sensor node continuously monitors, reports every 1s when target detected, provides real-time updates to base station, consumes high energy (100 mA continuous), has lowest latency (1s) and high accuracy. Poll-Based Tracking (teal): sensor nodes sleep until base station queries every 10s, wake for 50ms to measure and respond if target detected, consumes low energy (5 mA average), has highest latency (10s) and low accuracy. Guided Tracking (navy): uses Kalman filter to predict target position, selectively activates only 8 sensors in predicted zone, tracks and updates trajectory for next zone prediction (feedback loop via dashed line), achieves optimal energy (8 mA average), balanced latency (2s) and high accuracy. Comparison shows trade-offs: Push has lowest latency but highest energy, Poll has lowest energy but highest latency, Guided offers best balance between energy efficiency and tracking performance.

Key Takeaways:

  1. Tracking Formulations
    • Push-based: Proactive periodic reporting (real-time)
    • Poll-based: On-demand queries (energy-efficient)
    • Guided: Active interception with tracker guidance
  2. Six Tracking Components
    • Detection, Cooperation, Position Computation
    • Future Position Estimation (Kalman filtering)
    • Energy Management (prediction-based activation)
    • Target Recovery (expanding ring search)
  3. Wireless Multimedia Sensor Networks
    • Combine scalar and camera sensors
    • Coalition formation for coverage
    • 99% energy savings via triggered activation
  4. Underwater Acoustic Networks
    • Acoustic propagation (1-10 kbps)
    • Oceanic forces mobility model
    • Silent localization via AUV
    • Opportunistic iterative localization
  5. Nanonetworks
    • Molecular communication (slow, short-range)
    • THz electromagnetic (fast, higher-range)
    • Applications: biomedical, environmental sensing

Understanding these diverse tracking paradigms enables design of appropriate solutions for each application domain.

406.7 Further Reading

Target Tracking: - Souza, É., et al. (2016). “Target tracking for sensor networks: A survey.” ACM Computing Surveys, 49(2), 1-31. - Li, W., et al. (2013). “Collaborative target tracking in wireless sensor networks.” Ad Hoc Networks, 11(3), 1062-1077.

Wireless Multimedia Sensor Networks: - Akyildiz, I. F., et al. (2007). “Wireless multimedia sensor networks: A survey.” IEEE Wireless Communications, 14(6), 32-39. - Misra, S., et al. (2012). “Coalitional game-based distributed coverage in multimedia sensor networks.” IEEE Trans. Mobile Computing.

Underwater Acoustic Networks: - Akyildiz, I. F., et al. (2005). “Underwater acoustic sensor networks: Research challenges.” Ad Hoc Networks, 3(3), 257-279. - Mandal, S., et al. (2018). “HASL: High-speed AUV-based silent localization.” IEEE INFOCOM.

Nanonetworks: - Akyildiz, I. F., & Jornet, J. M. (2010). “Electromagnetic wireless nanosensor networks.” Nano Communication Networks, 1(1), 3-19. - Nakano, T., et al. (2012). “Molecular communication.” Cambridge University Press.

406.8 References

  1. Estrin, D., et al. (1999). “Next century challenges: Scalable coordination in sensor networks.” ACM MobiCom.

  2. Zhao, F., & Guibas, L. J. (2004). Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann.

  3. Cui, J. H., et al. (2006). “The challenges of building scalable mobile underwater wireless sensor networks for aquatic applications.” IEEE Network, 20(3), 12-18.

  4. Pierobon, M., & Akyildiz, I. F. (2013). “Diffusion-based noise analysis for molecular communication in nanonetworks.” IEEE Trans. Signal Processing, 59(6), 2532-2547.

  5. Perera, C., et al. (2014). “Context aware computing for the Internet of Things: A survey.” IEEE Communications Surveys & Tutorials, 16(1), 414-454.

ImportantChapter Summary

This chapter examined WSN tracking capabilities for monitoring mobile objects across sensor network coverage areas.

Tracking Fundamentals: Object tracking in WSNs involves continuously estimating target positions as they move through monitored spaces. Unlike static sensing, tracking requires temporal correlation of observations, prediction of future positions, and coordination among sensors. Applications include wildlife monitoring, asset tracking in warehouses, vehicle tracking in smart cities, and patient monitoring in healthcare facilities.

Localization Techniques: We explored multiple approaches to determine object positions from sensor observations. Range-based methods use distance measurements (RSSI, ToA, TDoA) combined with trilateration to calculate positions. Range-free methods use connectivity information and proximity to landmark nodes. The choice depends on available hardware (ranging capability), accuracy requirements, and computational constraints. Each method trades off accuracy, complexity, and energy consumption differently.

Energy-Efficient Strategies: Since tracking is energy-intensive, we examined techniques to minimize power consumption while maintaining acceptable accuracy. Predictive activation wakes only sensors likely to observe the target based on movement models. Adaptive sampling adjusts measurement rates based on target velocity. Cluster-based tracking distributes processing across nodes. These strategies extend network lifetime significantly while tracking mobile objects effectively.

Understanding tracking mechanisms enables WSN applications that go beyond static environmental monitoring to actively follow mobile entities.

406.9 Knowledge Check

Test your understanding of these architectural concepts.

Scenario: You’re tracking wildlife in a 500m × 500m reserve (25 hectares). Budget: $15,000 allows 10 tracking sensors at $500 each, or 6 sensors at $700 each with UWB precision. Each sensor can measure distance using RSSI (±5-10m error, free) or UWB time-of-flight (±0.5m error, add $200/sensor). Annual battery replacement: $50/sensor.

Think about: 1. What’s the minimum sensors needed for 2D trilateration? How does adding sensors improve accuracy? 2. Coverage vs precision trade-off: 10 RSSI sensors (50m spacing) or 6 UWB sensors (83m spacing)? 3. Calculate 5-year total cost of ownership: sensors, batteries, maintenance.

Key Insight: Theoretical minimum: 3 sensors for unique 2D position (trilateration). Practical deployment: 4-6 sensors enable least-squares averaging, outlier rejection, redundancy. Coverage analysis: 10 sensors at 50m spacing → full 500×500m coverage with 100m RSSI range. 6 sensors at 83m spacing → coverage gaps, animals can move between sensors untracked. Accuracy needs: Wildlife monitoring (“which meadow has the elk?”) needs 5-10m room-level accuracy → RSSI sufficient. Precision robotics needs 0.5m accuracy → UWB required. 5-year TCO: 10 RSSI sensors: $5,000 initial + ($50 × 10 × 5 years) = $7,500. 6 UWB sensors: $4,200 initial + ($50 × 6 × 5 years) = $5,700. Recommendation: Deploy 10 RSSI sensors for better coverage ($1,800 more) rather than 6 UWB for precision. Wildlife doesn’t need sub-meter accuracy, but gaps lose tracking entirely.

Verify Your Understanding: - If budget increases to $20,000, should you buy UWB upgrades or deploy 15 RSSI sensors for 33m spacing?

Scenario: Your warehouse (200m × 100m) has 100 sensors monitoring 15 forklifts. Always-on mode: all 100 sensors listen continuously at 15mA draw, consuming 1,500mA total. Battery capacity: 2,000 mAh = 8 months life. Battery cost: $20 each, replacement labor: $15/sensor = $35 per replacement. Annual cost: 100 sensors × $35 × 1.5 replacements/year = $5,250/year.

Think about: 1. If forklifts follow predictable aisle routes, can Kalman filter predict position 10 seconds ahead? 2. Energy savings: only 8 sensors active (tracking zone) while 92 sleep at 1mA standby? 3. What happens when forklift takes unexpected turn - how fast can you wake backup sensors?

Key Insight: Predictive activation: Kalman filter estimates forklift velocity 2 m/s → predicts 20m travel in 10s → wake 8 sensors in predicted zone. Active power: 8 × 15mA + 92 × 1mA = 120mA + 92mA = 212mA total (86% reduction). Battery life: 2,000 mAh / 212mA = 9.4 months → but duty cycling extends to 67 months (5.6 years). Annual cost: 100 × $35 / 5.6 years = $625/year vs $5,250 always-on = $4,625/year savings. Risk mitigation: Forklift turns unexpectedly → 2m off predicted path → trigger expanding ring search → wake 20 sensors in 500ms → relocate target → resume prediction. Tracking lost <1% of time, energy savings 86%. Hybrid approach: Straight aisles (8 sensors, tight prediction), intersections/turns (20 sensors, conservative), loading docks (30 sensors, unpredictable motion). Adaptive duty cycling matches environment dynamics.

Verify Your Understanding: - If warehouse has 30 forklifts instead of 15, does predictive activation scale or do colliding predictions cause thrashing?

Scenario: Parking lot (200m × 150m) has 50 sensors monitoring vehicle occupancy, transmitting to base station in corner. Direct transmission: sensors 100m away consume 10 mA transmit power (E ∝ distance⁴ in multipath). Battery: 2,000 mAh, 1 transmission/minute, 100ms duration. Current battery life: 6 months. Annual replacement: 50 sensors × $25/battery × 2 cycles/year = $2,500/year.

Think about: 1. Calculate energy per transmission at 100m distance vs 20m distance (E ∝ d⁴)? 2. Clustered architecture: 10 cluster heads (5 members each) relay to base. What’s total energy? 3. Cluster head rotation: If CH role rotates weekly among 5 members, does this equalize battery drain?

Key Insight: Energy scaling: E ∝ distance⁴ (multipath environment, worse than free-space d² law). 100m: E = k × 100⁴ = k × 100,000,000. 20m: E = k × 20⁴ = k × 160,000 → 625× less energy. Direct transmission: All 50 sensors at 100m average → 50 × 100⁴ = 5,000,000,000k energy units. Clustered (10 CHs, 5 members): Members to CH (20m): 50 × 20⁴ = 8,000,000k. CHs to base (100m): 10 × 100⁴ = 1,000,000,000k. Total: 1,008,000,000k (80% reduction). Battery impact: 80% savings → 6-month life extends to 30 months. Annual cost: 50 × $25 / 2.5 years = $500/year (down from $2,500 = $2,000 savings). CH rotation: Each sensor is CH 20% of time (1 week per 5 weeks). Energy: 80% as member (low) + 20% as CH (5× higher) = average 1.8× base energy → equalized drain, all sensors last ~27 months.

Verify Your Understanding: - What if 5 sensors are in coverage dead zone 150m from any CH - do you add relay sensor or accept direct 150m transmission?

Question 7: When a tracked target moves out of a sensor cluster’s coverage, the cluster must perform a handoff to transfer tracking responsibility. What information must be transferred to maintain continuous tracking?

💡 Explanation: Handoff information transfer: (1) Target state: Current position (x,y) and velocity (vx,vy) estimates from tracking filter (Kalman, particle filter). Enables new cluster to initialize its filter with accurate state instead of restarting from scratch. (2) Tracking filter state: Kalman filter covariance matrix representing estimate uncertainty. Particle filter: particle distribution. Preserves tracking history. (3) Predicted trajectory: Expected path based on motion model. Helps new cluster anticipate target arrival, activate appropriate sensors proactively. (4) Target characteristics: ID, classification (vehicle type, animal species), any learned behavior patterns. Why complete handoff matters: (1) Tracking continuity: Starting from accurate state avoids “tracking loss” during handoff. Without state transfer, target could be lost during re-acquisition period. (2) Prediction accuracy: Velocity information enables new cluster to predict future positions immediately. Without velocity, must accumulate multiple observations before useful prediction. (3) Filter convergence: Kalman filter needs several measurement cycles to converge to accurate estimate. Transferring filter state provides instant convergence. Handoff process: (1) Trigger: Old cluster detects target approaching boundary (based on position + trajectory). (2) Neighbor identification: Determine which neighbor cluster target will enter (based on predicted path). (3) State transfer: Old cluster transmits complete state to new cluster head. (4) Activation: New cluster pre-activates sensors along predicted entry trajectory. (5) Confirmation: New cluster detects target, confirms handoff successful, acknowledges to old cluster. (6) Deactivation: Old cluster deactivates sensors, releases tracking responsibility. Failure modes: If handoff fails (message lost, wrong neighbor selected), expanding ring search initiated to re-acquire target.

Question 9: RSSI (Received Signal Strength Indicator) is commonly used for range estimation in WSN tracking. A sensor receives signal with RSSI = -70 dBm from a target. Why is this less accurate than Time-of-Arrival (ToA) ranging?

💡 Explanation: RSSI ranging challenges: (1) Path loss model: Theoretical relationship: RSSI (dBm) = P_tx - 10×n×log₁₀(d/d₀) where n=path loss exponent (2-4), d=distance. Solve for d given RSSI. (2) Environmental variation: Path loss exponent n varies dramatically: free space (n=2), indoor (n=3-4), foliage (n=4-6). Without knowing actual n, distance estimate can be off by 2-5×. (3) Obstacles: Walls, furniture, people attenuate signal unpredictably. Same RSSI could indicate 5m with clear path OR 15m through wall. (4) Multipath: Reflections cause signal to arrive via multiple paths, constructive/destructive interference creates RSSI fluctuations ±10 dBm at same location. (5) Shadowing: Random RSSI variation (fading) due to environment changes. (6) Result: RSSI-based ranging error typically 5-10 meters. ToA ranging accuracy: (1) Physical measurement: Measures signal propagation time t. Distance d = c × t where c=speed of light. (2) Timing precision: Synchronization to nanosecond accuracy → 30 cm ranging resolution (1 ns = 0.3 m). (3) Multipath mitigation: Can identify first arriving path (direct), rejecting delayed reflections. (4) Result: ToA error typically 0.5-2 meters. Why still use RSSI: (1) Hardware cost: RSSI is free (built into all radios). ToA requires precision oscillators, time sync (GPS or ultrawideband radios). (2) Complexity: RSSI trivial to measure. ToA requires complex synchronization protocols. (3) Applications: When 5-10m accuracy acceptable (room-level localization, proximity detection), RSSI sufficient. Precision tracking (robotics, healthcare) requires ToA/TDoA.

Question 10: Expanding ring search is used to recover a lost target (tracking failed due to prediction error or handoff failure). How does the search radius expand, and why?

💡 Explanation: Expanding ring search: (1) Last known position: Target was at (x₀, y₀) at time t₀, then lost. (2) Maximum displacement: Target cannot travel faster than v_max (maximum possible speed). After time Δt = t_current - t₀, target must be within circle of radius r = v_max × Δt centered at (x₀, y₀). (3) Progressive activation: Start with sensors within radius r₁ = v_max × Δt₁ (small Δt₁). If not found, expand to r₂ = v_max × Δt₂ (larger Δt₂), continuing until target detected. (4) Energy efficiency: Activating sensors in expanding rings rather than all at once conserves energy - if target found early, distant sensors never activated. Example: Target lost at (100, 50), v_max = 5 m/s. After 10 seconds: r = 5 m/s × 10s = 50m. Activate all sensors within 50m of (100,50). If not found, after 20s: r = 100m. Activate sensors in 50-100m annulus. Ring vs. flood search: (1) Ring search: Progressive, energy-efficient, finds nearby targets quickly. Average sensors activated: ~πr²/A × total_sensors where A=area. (2) Flood search: Activate ALL sensors immediately. Guarantees finding target (if in coverage) but wastes massive energy. Optimization: (1) Direction bias: If target’s last velocity known, search preferentially in movement direction (elliptical expansion vs. circular). (2) Adaptive expansion: Increase ring growth rate if target not found (faster expansion if urgent). (3) Multi-hop coordination: Cluster heads coordinate to avoid redundant activation at ring boundaries. Recovery probability: Depends on search delay. If target exits network coverage before search radius catches up, recovery fails. Trade-off: faster expansion uses more energy, slower risks permanent loss.

Question 11: Particle filter tracking represents target position as a collection of weighted particles (hypotheses). How does this handle non-linear target motion better than Kalman filters?

💡 Explanation: Kalman filter limitation: Assumes Gaussian (normal) distribution with single peak (mode). State uncertainty represented by mean μ and covariance Σ. Works well for linear motion with Gaussian noise. Fails for: (1) Multi-modal uncertainty: Target could be in multiple possible locations (e.g., at intersection - went left OR right?). Single Gaussian cannot represent this. (2) Non-linear motion: Abrupt direction changes, circular paths violate linear motion assumption. (3) Non-Gaussian noise: Measurement outliers, systematic biases create skewed distributions. Particle filter approach: (1) Particle representation: Maintain N particles {x₁, x₂, …, xₙ} each representing possible target state (position, velocity). (2) Weights: Each particle has weight w_i indicating probability that hypothesis x_i is correct. (3) Multi-modal capability: Particle cluster at intersection represents bimodal distribution (some particles went left, others right). Kalman filter forced to pick single average (going straight through intersection!) (4) Resampling: Low-weight particles (unlikely hypotheses) die, high-weight particles (matching measurements) survive and replicate. Algorithm: (1) Prediction: Move each particle according to motion model + noise. (2) Update: Receive measurement, compute weight for each particle (how well it matches observation). (3) Resampling: Probabilistically kill low-weight, replicate high-weight particles. (4) Estimate: Weighted average of particles provides position estimate. Computational cost: Kalman filter: O(d³) where d=state dimensions (typically 4-6). Particle filter: O(N×d) where N=particles (100-1000s). More expensive but handles complex scenarios. Application: Wildlife tracking with unpredictable movement, urban vehicle tracking with turns, indoor pedestrian tracking with multiple path choices.

Question 12: Silent localization in underwater acoustic networks uses mobile AUVs (Autonomous Underwater Vehicles) with known positions. Why is this “silent” approach preferred over traditional beacon-based localization?

💡 Explanation: Traditional beacon-based localization: (1) Fixed beacons: Anchor nodes at known positions periodically broadcast their locations. (2) Sensor localization: Unknown nodes measure distance to ≥3 beacons (ToA, RSSI), compute position via trilateration. (3) Beacon overhead: Continuous broadcasts consume energy, create channel congestion. In acoustic networks: very slow data rates (1-10 kbps) → beacon messages consume significant bandwidth. (4) Beacon deployment: Difficult to install and maintain fixed beacons in harsh underwater environment. Silent localization with AUVs (HASL - High-speed AUV-based Silent Localization): (1) Mobile references: AUVs (autonomous submarines) with GPS-accurate positioning (surfacing for GPS fixes). (2) Opportunistic localization: AUV broadcasts position only when passing near sensor (detected by sensor listening). (3) Passive sensors: Sensors don’t transmit, just listen for AUV announcements. Receive positions from multiple AUV passes → triangulate own position. (4) Silence benefit: No continuous beacon overhead, minimal channel usage. Sensors are passive (extremely energy-efficient). (5) AUV path planning: Optimize AUV trajectory to provide coverage of all sensors while minimizing travel distance/time. Energy comparison: Beacon approach: 100 sensors × 100 beacon messages/hour × 1s transmission = 10,000 s acoustic transmission/hour. Silent approach: 5 AUV passes/hour × 1s announcement = 5s acoustic transmission/hour → 99.95% reduction in acoustic channel usage. Localization accuracy: Comparable to beacon-based (0.5-2m) achieved through multiple AUV encounters from different angles. Applications: Military underwater surveillance (stealth critical), environmental monitoring networks, offshore infrastructure monitoring. AUVs perform dual role: data mule + localization reference.

406.10 What’s Next?

Building on these architectural concepts, the next section examines Wsn Coverage.

Continue to Wsn Coverage →