416  WSN Coverage: Review

416.1 Learning Objectives

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

  • Implement OGDC Algorithm: Deploy Optimal Geographical Density Control for coverage
  • Analyze k-Coverage: Verify and optimize multi-coverage requirements
  • Detect Coverage Holes: Build algorithms to identify and repair sensing gaps
  • Schedule Sensor Sleep: Implement energy-aware sleep/wake protocols maintaining coverage
  • Compare Coverage Types: Evaluate area, point, and barrier coverage strategies
  • Deploy Production Systems: Apply the framework to real-world coverage optimization

416.2 Prerequisites

Required Chapters: - WSN Coverage Fundamentals - Coverage concepts - WSN Coverage - Coverage types - WSN Coverage Implementations - Algorithms

Technical Background: - Sensor range models - Geometric coverage - Connectivity requirements

Coverage Types:

Type Goal Metric
Area Cover region % coverage
Point Monitor targets Detection prob
Barrier Detect crossings Path coverage
k-Coverage Redundancy Fault tolerance

Estimated Time: 45 minutes

Deep Dives: - WSN Coverage Fundamentals - Core coverage concepts and models - WSN Coverage Implementations - Practical coverage algorithms

Related WSN Topics: - WSN Overview Fundamentals - Network architecture and node roles - WSN Tracking Fundamentals - Target tracking with coverage considerations - Wireless Sensor Networks - Broader WSN context

Advanced Topics: - WSN Stationary Mobile Fundamentals - Mobile sink coverage optimization - WSN Routing - Energy-aware routing for coverage maintenance

Learning: - Quizzes Hub - Test your coverage knowledge - Simulations Hub - Visualize coverage patterns

What is this chapter? Comprehensive review of WSN coverage concepts and deployment strategies.

When to use: - After studying WSN coverage fundamentals - When planning sensor network deployments - For exam preparation

Key Coverage Concepts:

Concept Description
Area Coverage Monitoring geographic regions
Target Coverage Covering specific points
Barrier Coverage Detecting boundary crossings
k-Coverage Redundant coverage for reliability

Prerequisites: - WSN Coverage Fundamentals - Understanding of sensing models - Basic deployment planning

Recommended Path: 1. Complete coverage fundamentals first 2. Review concepts here by section 3. Complete quiz questions

416.3 Chapter Overview

This review chapter consolidates WSN coverage concepts into a comprehensive reference. The content is organized into focused sub-chapters for efficient learning:

NoteSub-Chapter Navigation
Chapter Focus Time
Production Framework Enterprise-ready optimization, real-world adoption, decision trees, misconceptions 25 min
Knowledge Checks Quizzes 1-4 covering all coverage concepts with detailed explanations 35 min

416.4 Quick Reference: Coverage Types

Area Coverage: Monitors continuous 2D regions with percentage-based metrics. Use grid or random deployment. Primary application: agriculture, environmental monitoring.

Point Coverage: Monitors discrete critical locations using set cover algorithms. Minimizes sensor count for POI coverage. Primary application: industrial equipment, infrastructure.

Barrier Coverage: Detects border crossings with linear deployment strategies. Weak barrier detects at k points; strong barrier provides continuous tracking. Primary application: security perimeters.

K-Coverage: Provides redundancy with k sensors covering each point. Enables fault tolerance and rotation scheduling. Primary application: critical infrastructure.

416.5 Quick Reference: Key Theorems

Zhang-Hou Theorem (2005): If communication range Rc >= 2 x sensing range Rs, then complete coverage implies network connectivity. This simplifies deployment by reducing verification to coverage alone.

OGDC Optimal Spacing: Triangular lattice with sqrt(3) x Rs spacing provides near-optimal coverage for disk sensing models. Achieves 95-99% coverage with 40-60% active sensors.

Crossing-Based Verification: If all crossing points (where sensor coverage circles intersect) have k-coverage, the entire area has k-coverage. Reduces verification from O(infinity) to O(N^2).

416.6 Summary

Wireless sensor network coverage is fundamental to ensuring effective monitoring of areas, points, or barriers.

Key Takeaways:

  1. Coverage vs. Connectivity
    • Coverage: Monitoring quality
    • Connectivity: Data delivery paths
    • If Rc >= 2Rs, coverage implies connectivity
  2. Coverage Types
    • Area: Monitor continuous 2D region
    • Point: Monitor discrete critical locations
    • Barrier: Detect border crossings
  3. Deployment Strategies
    • Deterministic: Planned grid placement
    • Random: Aerial drop, emergency deployment
    • Trade-offs: Cost vs. coverage quality
  4. Algorithm Approaches
    • Centralized: Optimal but not scalable
    • Distributed: Scalable, near-optimal
    • Localized: Most energy-efficient
  5. OGDC Algorithm
    • Achieves optimal triangular lattice spacing
    • Distributed, localized execution
    • 40-60% energy savings vs. always-on
  6. Practical Considerations
    • Redundancy for fault tolerance (k-coverage)
    • Rotation schedules extend lifetime
    • Crossing-based verification reduces complexity

Understanding coverage theory enables design of efficient IoT deployments that balance monitoring quality, energy consumption, and cost.

ImportantChapter Summary

This chapter explored coverage properties of WSNs, examining how sensor placement and activation affect monitoring effectiveness.

Coverage Fundamentals: Coverage quantifies how well the deployed sensors monitor the target area. Complete coverage ensures every point is monitored by at least one sensor, while K-coverage provides redundancy by requiring K sensors per point. Coverage quality depends on node density, sensing ranges, deployment patterns, and environmental factors affecting sensor performance. Understanding coverage is essential for guaranteeing that critical events won’t be missed.

Deployment and Analysis: We examined deployment strategies ranging from random scatter (simple but potentially inefficient) to optimized placement (computationally intensive but guaranteed coverage). The chapter covered analytical methods for calculating coverage probability based on sensor density and geometric analysis techniques for identifying coverage holes. Virtual force algorithms can improve coverage by having nodes adjust positions based on simulated forces.

Dynamic Coverage Management: Static full coverage drains batteries quickly, so practical WSNs use dynamic strategies. Scheduled activation rotates coverage responsibility among redundant nodes, extending lifetime while maintaining coverage. Coverage-preserving sleep scheduling ensures at least one sensor covers each region while others sleep. These approaches balance the competing goals of complete coverage, energy efficiency, and network longevity.

Coverage analysis provides the foundation for reliable WSN deployment, ensuring monitoring objectives are met while optimizing resource utilization.

416.7 Sub-Chapters

Continue your learning with these focused chapters:

416.7.1 Production Framework

Enterprise-ready WSN coverage optimization including: - Cisco Smart Cities deployment statistics - Coverage model comparison and selection decision tree - OGDC algorithm and optimization strategies - Hole detection and repair process - Common misconception: “More Sensors = Better Coverage”

416.7.2 Knowledge Checks

Comprehensive quizzes covering: - Quiz 1: Coverage optimization (fault tolerance, scheduling) - Quiz 2: Comprehensive review (Zhang-Hou, OGDC, barrier) - Quiz 3: Deployment analysis (grid spacing, point coverage) - Quiz 4: Advanced concepts (theorems, algorithms)

416.8 References

  1. Cardei, M., & Wu, J. (2006). “Energy-efficient coverage problems in wireless ad-hoc sensor networks.” Computer Communications, 29(4), 413-420.

  2. Xing, G., et al. (2005). “Integrated coverage and connectivity configuration for energy conservation in sensor networks.” ACM Transactions on Sensor Networks, 1(1), 36-72.

  3. Zhang, H., & Hou, J. C. (2005). “Maintaining sensing coverage and connectivity in large sensor networks.” Ad Hoc & Sensor Wireless Networks, 1(1-2), 89-124.

  4. Megerian, S., et al. (2005). “Worst and best-case coverage in sensor networks.” IEEE Transactions on Mobile Computing, 4(1), 84-92.

  5. Perera, C., et al. (2014). “Sensing as a service model for smart cities supported by Internet of Things.” Transactions on Emerging Telecommunications Technologies, 25(1), 81-93.

416.9 What’s Next?

Building on these architectural concepts, the next section examines WSN Stationary Mobile systems.

Continue to WSN Stationary Mobile ->