Wireless Sensor Networks 2009-2019

Table of Contents

EXECUTIVE SUMMARY AND CONCLUSIONS

1. INTRODUCTION

• 1.1. Active vs passive RFID
• 1.2. Three generations of active RFID
• 1.3. Second Generation is RTLS
• 1.4. Third Generation is WSN
  • 1.4.1. Managing chaos and imperfection
  • 1.4.2. The whole is much greater than the parts
  • 1.4.3. Achilles heel - power
  • 1.4.4. View from UCLA
  • 1.4.5. View of Institute of Electronics, Information and Communication Engineers
  • 1.4.6. View of the International Telecommunications Union
  • 1.4.7. View of the Kelvin Institute
  • 1.4.8. Contrast with other short range radio
  • 1.4.9. A practical proposition
  • 1.4.10. Mobile phones
  • 1.4.11. Wireless mesh network structure
• 1.5. Three waves of adoption
  • 1.5.2. Subsuming earlier forms of active RFID?
• 1.6. Ubiquitous Sensor Networks (USN) and TIP
• 1.7. Defining features of the three generations
• 1.8. WSN paybacks
• 1.9. Supply chain of the future

2. PHYSICAL STRUCTURE, SOFTWARE AND PROTOCOLS

• 2.1. Physical network structure
• 2.2. Power management
  • 2.2.1. Power Management of mesh networks
• 2.3. Operating systems and signalling protocols
  • 2.3.1. 802.15.4 ZigBee
  • 2.3.2. Protocol structure of ZigBee
  • 2.3.3. WirelessHART, 6lowplan, ISA100
  • 2.3.4. IEEE 802.15.4a
  • 2.3.5. DecaWave - a new 802.15.4a chip
  • 2.3.6. TinyOS
  • 2.3.7. Associated technologies and protocols
• 2.4. Dedicated database systems
• 2.5. Programming language nesC, JAVA

3. ACTUAL AND POTENTIAL WSN APPLICATIONS

• 3.1. General
• 3.2. Precursors of WSN
• 3.3. Intelligent buildings
• 3.4. Military and Homeland Security
• 3.5. Oil and gas
• 3.6. Healthcare
• 3.7. Farming
• 3.8. Environment monitoring
• 3.9. Transport and logistics

4. EXAMPLES OF DEVELOPERS AND THEIR PROJECTS

• 4.1. Geographical distribution of WSN practitioners and users
• 4.2. Profiles of 141 WSN suppliers and developers
• 4.3. Ambient Systems
  • 4.3.1. Introduction
  • 4.3.2. How Ambient Product Series 3000 works
  • 4.3.3. The power of local intelligence: Dynamic Event Reporting
  • 4.3.4. How SmartPoints communicate with the Ambient wireless infrastructure
  • 4.3.5. Ambient Wireless Infrastructure - The power of wireless mesh networks
  • 4.3.6. Ambient network protocol stack
  • 4.3.7. Rapid Reader for high-volume data communication
  • 4.3.8. Ambient Studio: Managing Ambient wireless networks
  • 4.3.9. Comparing Ambient to wireless sensor networks (incl. ZigBee)
  • 4.3.10. Comparing Ambient to active RFID and Real-Time Locating Systems
  • 4.3.11. Summary and conclusion
• 4.4. Arch Rock
• 4.5. Auto-ID Labs Korea/ ITRI
• 4.6. Berkeley WEBS
  • 4.6.1. Epic
  • 4.6.2. SPOT - Scalable Power Observation Tool
• 4.7. Chungbuk National University Korea
• 4.8. Dust Networks
  • 4.8.1. Smart Dust components
  • 4.8.2. Examples of benefits
  • 4.8.3. KV Pharmaceuticals
  • 4.8.4. Milford Power
  • 4.8.5. Fisher BioServices
  • 4.8.6. PPG
  • 4.8.7. Wheeling Pittsburgh Steel
  • 4.8.8. SmartMesh Standards
  • 4.8.9. US DOE project
• 4.9. Crossbow Technology
• 4.10. Emerson Process Management
  • 4.10.1. Grane offshore oil platform
• 4.11. GE Global Research
• 4.12. Holst Research Centre
  • 4.12.1. Body area networks for healthcare
• 4.13. Intel
• 4.14. Kelvin Institute
• 4.15. Laboratory for Assisted Cognition Environments LACE
• 4.16. Millennial Net
• 4.17. Motorola
• 4.18. National Information Society Agency
  • 4.18.1. The vision for Korea
  • 4.18.2. First trials
  • 4.18.3. Seawater - oxygen, temperature
  • 4.18.4. Setting concrete - temperature, humidity
  • 4.18.5. Greenhouse microclimate - temperature, humidity
  • 4.18.6. Hospital - blood temperature, drug temp and humidity
  • 4.18.7. Recent trials
  • 4.18.8. Program of future work
• 4.19. Newtrax Technologies
  • 4.19.1. Canadian military
  • 4.19.2. Decentralised architecture
  • 4.19.3. Inexpensive and expendable sensors
• 4.20. Sensicast
• 4.21. ScatterWeb
  • 4.21.1. Hardware modularity
  • 4.21.2. Flexible routing
  • 4.21.3. Documented software interfaces
  • 4.21.4. Energy management
  • 4.21.5. Structural health monitoring of bridges
• 4.22. TelepathX
• 4.23. University College of Los Angeles CENS
• 4.24. University of Virginia NEST
  • 4.24.1. NEST: Network of embedded systems
  • 4.24.2. Technical overview
  • 4.24.3. Programming paradigm
  • 4.24.4. Feedback control resource management
  • 4.24.5. Aggregate QoS management and local routing
  • 4.24.6. Event/landmark addressable communication
  • 4.24.7. Team formation
  • 4.24.8. Microcell management
  • 4.24.9. Local services
  • 4.24.10. Information caching
  • 4.24.11. Clock synchronization and group membership
  • 4.24.12. Distributed control and location services
  • 4.24.13. Testing tools and monitoring services
  • 4.24.14. Software release: VigilNet
• 4.25. Wavenis and Essensium
  • 4.25.1. Essensium' s WSN product vision
  • 4.25.2. Fusion of WSN, conventional RFID, RTLS and low power System on Chip integration
  • 4.25.3. Concurrent skill sets to be applied
  • 4.25.4. Integration with end customer.

5. POWER FOR TAGS

• 5.1. Batteries
• 5.2. Energy Harvesting
  • 5.2.1. Photovoltaics
  • 5.2.2. Other options
• 5.3. Field delivery of power

6. IMPEDIMENTS TO ROLLOUT OF USN

• 6.1. Concerns about privacy and radiation
• 6.2. Slowness
• 6.3. Competing standards and proprietary systems
• 6.4. Lack of education
• 6.5. Technology improvement and cost reduction needed
  • 6.5.1. Error
  • 6.5.2. Scalability
  • 6.5.3. Sensors
  • 6.5.4. Locating Position
  • 6.5.5. Spectrum congestion and handling huge amounts of data
  • 6.5.6. Optimal routing, global directories, service discovery
• 6.6. Niche markets lead to first success

7. MARKETS 2009-2019

• 7.1. Background
• 7.2. Assessments
• 7.3. History and forecasts.
  • 7.3.1. IDTechEx forecasts 2009-2019
  • 7.3.2. IDTechEx forecast for 2029
  • 7.3.3. Market and technology roadmap to 2029
  • 7.3.4. The overall markets for ZigBee and wireless sensing.

APPENDIX 1: IDTECHEX PUBLICATIONS

APPENDIX 2: GLOSSARY

TABLES

• 1.1. Defining features of the three generations of active RFID
• 4.1. 141 WSN suppliers and developers tabulated by country, website and activity
• 4.2. Comparison of wireless sensor networks
• 4.3. Comparison of traditional Active RFID and Ambient series 3
• 5.1. Power supply options for WSN
• 5.2. Features of the new Planar Energy devices batteries
• 5.3. The new photovoltaic options compared.
• 7.1. WSN and ZigBee node numbers million 2009, 2019, 2029 and market drivers
• 7.2. Average number of nodes per system 2009, 2019, 2029
• 7.3. Number of systems
• 7.4. WSN node price dollars 2009, 2019, 2029 and cost reduction factors
• 7.5. WSN node total value $ million 2009, 2019, 2029
• 7.6. Price-volume projections in 2009 for RF devices
• 7.7. WSN systems and software excluding nodes $ million 2009, 2019, 2029
• 7.8. Total WSN market value $ million 2009, 2019, 2029

FIGURES

• 1.1. Typical RTLS tags with 3-10 years battery life. Top left and right WiFi 2.45GHz. Bottom left UWB. Bottom right 2.45GHz. Center ultrasound.
• 1.2. MicroStrain WSN node with 55 day battery life
• 1.3. WSN compared with Bluetooth and WiFi in respect of power and data rate.
• 1.4. WSN compared with other short range radio in respect of range and data rate typically available
• 1.5. Detailed view of range vs data rate.
• 1.6. A basic wireless mesh network
• 1.7. WSN backhaul
• 1.8. Diagrammatic illustration of the three waves of adoption of active RFID.
• 1.9. Possible area of deployment vs system cost
• 1.10. Tolerance of faults and unauthorised repositioning vs system cost
• 1.11. Tag cost today vs system cost
• 1.12. Number of tags per interrogator vs system cost
• 1.13. Infrastructure cost vs system cost
• 1.14. Figure RTLS progress towards the ultimate supply chain
• 2.1. WSN with conventional star network at outside edge to save power.
• 2.2. More complex networks that are only partially meshed
• 2.3. Protocol structure of ZigBee
• 2.4. Figure DecaWave Scensor product brief
• 3.1. RFID meets sensor network
• 3.2. Some possibilities for WSN in buildings
• 3.3. Mesh network in military applications
• 3.4. Requirements for sensor networks in health management of missiles
• 3.5. Future fundamental technology development areas for "Health Management of Munitions" in the US Navy.
• 3.6. In-body WSN for healthcare
• 3.7. Environment monitoring.
• 3.8. Intelligent container
• 4.1. Geographical distribution of 141 profiled WSN practitioners
• 4.2. Ambient Wireless Infrastructure
• 4.3. Ambient SmartPoints - Making objects intelligent
• 4.4. SmartPoints communicate with the Ambient wireless infrastructure
• 4.5. Ambient wireless mesh network
• 4.6. Ambient network protocol stack
• 4.7. Ambient Studio: Managing Ambient wireless networks
• 4.8. Active RFID and RTLS compared to Ambient
• 4.9. Organisation for promoting USN
• 4.10. Research focus at Auto-ID Labs Korea
• 4.11. Related work on sensors
• 4.12. A Framework of In-situ Sensor Data Processing System for Context Awareness
• 4.13. Smart Dust components
• 4.14. Controlled environment
• 4.15. SmartMesh IA-500™
• 4.16. Smart Dust Intelligent Networking System
• 4.17. Holst Centre body area network node
• 4.18. New logos of Intel
• 4.19. MeshScape® 5.0 "Best of Sensors" Award Winner!"
• 4.20. IAP4300 - Intelligent Access Point for MOTOMESH Duo
• 4.21. IAP6300 - Intelligent Access Point for MOTOMESH Solo
• 4.22. IAP7300 - Intelligent Access Point for MOTOMESH Quattro
• 4.23. USN in Korea
• 4.24. Concept of USN in Korea
• 4.25. Timeline of USN development in Korea
• 4.26. Marine environment data collection using USN
• 4.27. Fishery monitoring test
• 4.28. Marine environment data collection system
• 4.29. Concrete structure and sensor installation for field test.
• 4.30. Concrete curing history management
• 4.31. Microclimate in industrial greenhouses.
• 4.32. Field test of monitoring blood and anti-cancer agents
• 4.33. Development of the necessary software and hardware
• 4.34. SensiNet
• 4.35. ScatterWeb system diagram
• 4.36. Bridge monitoring
• 4.37. NEST node architecture
• 4.38. Essensium' s WSN product vision
• 4.39. Wavenis view of its market for wireless sensing
• 4.40. Three skill sets to be applied.
• 4.41. Integration with end customer
• 5.1. Planar Energy Devices battery
• 5.2. Field delivery of power demonstrated by Intel
• 6.1. RTLS operational options using electromagnetic emissions or, more rarely, ultrasound.
• 7.1. Number of projects by sector in the IDTechEx RFID Knowledgebase.
• 7.2. IDTechEx WSN Forecast 2009-2019 with RTLS for comparison
• 7.3. Meter reading nodes number million 2009-2019
• 7.4. Meter reading nodes unit value dollars 2009-2019
• 7.5. Meter reading nodes total value dollars 2009-2019
• 7.6. Other nodes number million 2009-2019
• 7.7. Other nodes unit value dollars 2009-2019
• 7.8. Other nodes total value dollars 2009-2019
• 7.9. Total node value billion dollars 2009-2019
• 7.10. WSN systems and software excluding nodes billion dollars 2009-2019
• 7.11. Total WSN market million dollars 2009-2019
• 7.12. WSN and ZigBee node numbers million 2009, 2019, 2029
• 7.13. Average number of nodes per system 2009, 2019, 2029
• 7.14. Number of systems 2009, 2019, 2029
• 7.15. WSN node price dollars 2009, 2019, 2029
• 7.16. WSN node total value $ million 2009, 2019, 2029
• 7.17. Price sensitivity curve for RFID
• 7.18. WSN systems and software excluding nodes $ million 2009, 2019, 2029
• 7.19. Total WSN market value $ million 2009, 2019, 2029
• 7.20. WSN adoption roadmap by Crossbow Technologies in 2006
• 7.21. Dynamics of WSN market 2009 to 2029
• 7.22. ZigBee chipset shipment market share in 2009