Container Network Data Analysis

1. Container Network Data Analysis Garrett Wolf

2. Background • Over 90% of the world’s cargo moves via container [9] • Container security could be improved • Dept. of Homeland Security developed the Advanced Container Security Device (ACSD)[2] guidelines • Requirements: • Detect breech of container • Environmental conditions (humidity, temperature, shock, vibration, etc.) • Location of container (ship, rail, truck) • Power lifetime of 30,000 hours • Etc.

3. Problems • Ability to detect events/intrusions is needed for container security • Small containers require security too • Containers must be tracked through various environments through which they travel • Smaller containers also travel via airplane • Limited battery power requires intelligently setting the reporting frequencies • Not all sensors are created equal when it comes to detecting events/intrusions

4. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Accel. Temp Temp Temp Temp Temp Temp Temp Temp Temp Temp Light Temp Light Light Light Light Light Light Light Light Light Light Light Light Light Contributions • Past work focuses on large shipping containers whereas I focus on smaller “FedEx” sized packages • Past work focuses on oceanic or land based transportation whereas I include an analysis of air transportation • Past work [8] adjusts the reporting frequency at the node level whereas I suggest adjusting the reporting frequency at the sensor level vs. vs.

5. Goals • Analyze sensor data collected across different environments (airplane vs. automobile) • Identify events in each of the environments (loading/unloading of container, start of engine, speed/acceleration, etc.) • Detect intrusions in each of the environments • Determine which sensors are more helpful for intrusion detection given the environmental settings and prior events

6. Experimental Setup • 3 Containers • ≈1ft3 each • Slightly insulated • 1 Stargate [3] and PDA • 5 Motes • 2 Telos B – temp, humidity, microphone, visible/IR light • 2 MTS310 – temp, light, microphone, 2-axis accelerometer, 2-axis magnetometer • 1 MTS300 – temperature, light, microphone • Configuration: • Container 1: 1 MTS310 & 1 Telos B • Container 2: 1 MTS300 & 1 Stargate • Container 3: 1 MTS310 & 1 Telos B

7. Experimental Setup (cont.) • 1 Cirrus[10] SR22-GTS • 1 Honda Accord

9. Data Collection and Results • Opened 1 of the 3 containers for 10 second intervals in each environment • Same container opened each time (Container 1) • Container opened at different points in time (e.g. on the ground, in the vehicle, after engine started, while moving slowly, while moving quickly, etc.) • Took note of the time when intrusion or other event occurred • Compared the sensor readings with the recorded intrusion times

10. Humidity drops when intrusion occurs Humidity was the one of the best indicators for intrusion detection Temp also drops but its not as apparent as humidity

11. TSR (a.k.a visible and infrared light) was good but in the plane, the results were less informative PAR (a.k.a visible light) is a very good indicator

12. Thermistor in Container 2 increased steadily because of the heat given off from the Stargate MTS 300/310 light sensor gave less helpful results when compared to the Telos sensor boards

14. What I Learned • Motes needed a higher reporting frequency • Regardless of application, some sensors should report more frequently than others • E.g. changes in temp. are slower than changes in acceleration • More motes are needed to reduce noise in the data • Need to be careful that the motes are stationed level when dealing with 2-axis sensors to prevent incorrect readings caused by tilt

15. References • [1] Havinga, Paul J.M., Sensor Networks for Monitoring. IST 2004 Presentationhttp://europa.eu.int/information_society/istevent/2004/cf/document.cfm?doc_id=1234 • [2] Department of Homeland Security. Advanced Container Security Device –Broad Agency Announcement (BAA04-06). May 7, 2004. http://www.hsarpabaa.com/Solicitations/AdvContSecDev_BAA_FINAL_508.pdf • [3] 2006 Crossbow Technology. MTS/MDA Sensor, Data Acquisition Boards Datasheet.http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MTS_MDA_Datasheet.pdf • [4] XCube Communication. SEAL Cargo System. http://www.x3-c.com/downloads/Industrypaper%20Cargo%20V1R1.pdf • [5] T. Larsson, M. Taveniku, C. Wigren, P. Wiberg, B. Svensson. T4 – Telematics for Totally Transparent Transports. In Proceedings of 8th International IEEE Conference on Intelligent Transport Systems, 2005. • [6] G. Hackmann, C. Fok, C. Zuver, K. English. Agile Cargo Tracking Using Mobile Agents. In SenSys 2005. • [7] F. Ridoutt, C. Mueller-Dieckmann, P. Tucker, M. Weiss. An Automated Temperature-Monitoring System for Dry-Shippers. Journal of Applied Crystallography 2004. • [8] O. Akan and I. Akyildiz, Event-to-Sink Reliable transport in Wireless Sensor Networks, IEEE/ACM Trans. On Networking, 13(5), Oct. 2005. • [9] U.S. Customs and Border Protection. Container Security Initiative.http://www.customs.treas.gov/xp/cgov/enforcement/international_activities/csi/ • [10] Cirrus Aviation. Cirrus Design Brochure. http://www.cirrusdesign.com/downloads/pdf/brochure.pdf

16. Questions?