1. Container Shipping Breakout Group Participants: Cory Sharp [scribe] – UC Berkeley Thomas Sereno – SAIC Ken Traub – Connecterra Ron Kyker – Sandia National Labs Malena Mesarina – Hewlett Packard Labs Robert Szewczyk – UC Berkeley Phil Buonadonna – Intel Mike Manzo – UC Berkeley Jae-Hyuk Oh – United Technologies NEST Retreat, June 2004, Santa Cruz, CA

2. Deployments Shipping container security –Intrusion detection –Adversarial, networking Container monitoring –Report if environment out of tolerance –Take immediate action –Non-adversarial, networking Radioactive detection –Adversarial monitoring Pedigree –Log environment data –Report once at end, or at least infrequently –Non-adversarial, networking

3. Value Add Over a Simple, Cheap, One-shot Sensor Data processing –Simple, cheap sensors can only detect exceeded tolerances Data collection –More data available –Manual collection consumes resources Latency –Human-inspected detectors significantly increase latency

4. Sensing Modes Monitoring –Adversarial (security) vs. non-adverserial (environmental) –Mesh networking, low latency reports Pedigree –Less significant networking What kinds of sensing?

5. Installation Options Permanent fixture of container Vendor product Consumer devices

6. Core Issues Lifetime –5 years is the far upper bound (upgrades) Cost –$50/container/port total cost Localization –Where is container 1172? (global vs. local) –Audience Note: Shipping companies know precisely what and where about every container, needed for balancing RF Connectivity –RF leaks through non-water-tight containers Data Logging Interoperability between multiple vendors

7. Core Issues 2 Security –Compromised network (authentication, encryption, …) –Denial of service, network – spamming packets toward energy exhaustion –Denial of service, sensing – bananas and kitty litter excite radioactive detectors Detecting events in variable environments –On a ship, temperature, pressure, and vibration changes –How do you detect normal variance versus exceptional variance? Calibration –Logged/reported data must be meaningful

8. Problems Validation – how do you know you get data from all the containers? Robustness, Reliability –What happens if you lose 10% of the nodes? –Do losses generate false positives? –Add redundancy? Cost? Latency of data –Certain modes may want to spend a lot of energy to guarantee reports (“fire alarm”) Data glut/trunk, energy exhaustion –10,000 nodes reporting –Address with multiple gateways / base-stations