Wireless Sensor Mote Telos B Ultra lowpower wireless

Wireless Sensor Mote (Telos. B) • Ultra low-power wireless module – for sensor networks, monitoring app, rapid prototyping • Key Features – – – 2. 4 GHz 802. 15. 4 radio, 250 kps 8 MHz processor, 10 k RAM, 48 k Flash Fast wakeup (6 s) 1 MB external flash (16 segments, each 64 k. B) Ultra low current consumption Tiny. OS support

Some Useful Components of Mote/TOS • • • Radio UART Timer (unique) Flash memory LEDs Sensor(s)

Some Research Area in WSN • • • Wireless Communication Localization Calibration Time Synchronization Routing (Data-Centric Routing) Deployment, Lifetime optimization Signal Processing - compression, detection Storage (Data-Centric Storage) Mobility

Wireless Communication Ideal model Real model * [Ganesan TR ’ 02][ Marco Zuniga et al. , USC]

Wireless Communication * [Zhao Sen. Sys ’ 03] [Woo Sen. Sys ’ 03] [Ganesan TR ’ 02] [Aguayo SIGCOMM’ 04] transitional distance (m) ** [Zhou Mobi. Sys’ 03] disconnected PRR – Three regions*: • Connected • Disconnected • Transitional – Transitional region : • Asymmetric links • High variance space/time • Large extent – Significant impact on some routing protocols** Empirical: link behavior *

Routing (Information Gradient-based Active Routing) Isoseismal (intensity) maps (North Palm Springs earthquake of July 8, 1986) Ref. : Southern California Earthquake Center. (http: //www. scec. org)

Routing (Information Gradient-based Active Routing) Challenges - In real life, sensors are unable to detect or measure the event’s effect below certain threshold. So, diffusion curve has finite tail - Lack of sensitivity of sensor device(s) - Creates flat information region - Erroneous reading of the malfunctioning sensors - Due to calibration error or obstacle - Cause local maxima or minima - Environmental noise

Brief Description of Routing Approaches Single-path Query forwarding with look-ahead, r = 1 41. 5 57. 4 Multiple-path Query forwarding 41. 5 57. 4 S 27. 8 32. 9 41. 5 57. 4 100 57. 4 41. 5 23. 8 27. 8 3. 4 41. 5 57. 4 41. 5 23. 8 31. 0 32. 9 41. 5 21. 1 23. 8 31. 0 32. 9 41. 5 18. 9 21. 1 30. 0 27. 5 29. 0 32. 9 23. 8 27. 5 27. 5 23. 8 4. 1 98. 1 23. 8 21. 1 18. 9 21. 1 30. 0 27. 5 29. 0 32. 9 80. 5 32. 9 67. 0 3. 2 21. 1 23. 8 27. 5 27. 5 67. 0 3. 2 21. 1 17. 2 92. 1 21. 1 23. 8 4. 1 98. 1 23. 8 17. 2 92. 1 21. 1 3. 1 18. 9 17. 2 18. 9 Active 18. 9 Node 17. 2 41. 5 23. 8 Look-ahead = 1 17. 2 57. 4 S Q 6. 9 Candidate Node 21. 1 Q 17. 2 18. 9 21. 1 17. 2 18. 9 3. 8 18. 9 17. 2 6. 9 Active 21. 1 Nodes 21. 1 9

Still Have Limitations Multiple Events Q Q Q • Flooding overhead is high for sparsely distributed events • Unable to detect sparsely located events