Practical Issues in Underwater Networks

1. Practical Issues in Underwater Networks Jim Partan, Jim Kurose, Brian Levine University of Massachusetts Amherst Supported by ONR contract N00014-05-G-0106-0008 via subcontract from WHOI, and NSF award CNS-0519881.

2. introduction My Goal: Highlight differences between terrestrial radio sensor networks and underwater acoustic sensor networks, with network design implications. Different emphasis from recent surveys: • Physical channel (already discussed) • Environment → different regimes • Different energy model → different metrics Questions and comments welcome anytime!

3. sensor costs… • Some very common, off-the-shelf oceanographic sensors: Conductivity, Temperature, and Depth (CTD): $3k-$12k Acoustic Doppler Current Profiler (ADCP): $25k Seismometer: $10k

4. → network sparsity • Sensors are expensive, so even if acoustic modems were free, sensor nodes will be expensive. • Oceanographic survey volumes can be large (though sampling is non-uniform). • Many (but not all) underwater networks will be sparse for a long time to come.

5. deployment costs… • Deployment and Recovery are often the largest expenses: Deep Ocean Ship: $25k/day Regional Ship: $12k/day Remotely-Operated Vehicle: ~$10k/day + ship Coastal Boat: $3k-5k/day

6. → high mobility • Deployment costs and node sparsity → Autonomous Underwater Vehicles (AUVs). • ~$2k/day for coastal deployment and recovery; autonomous operation. • $80k - $250k+ for typical ocean-going AUV (including some sensors).

7. enduring? Economic arguments rather than physical. Is there a fundamental basis for costs & sparsity? • Harsh, remote environment. • Ocean is huge, even with non-uniform sampling. • Deeply embedded systems. • Small market, and demanding customers. • New technologies. Cost/energy tradeoff? Some fundamental reasons, and change may be slow. → Sparsity and mobility will be enduring features of many underwater networks.

8. counter-example Good application for a traditional dense, fixed-node sensor network; likely to make sense economically: Seismic Monitoring of Existing Oilfields: (Heidemann et al) • Current method (seismic airgun surveys) is expensive, and hence is rarely done. • Frequent monitoring of oilfields is valuable. • Seismometers require fixed nodes. → No single type of underwater network.

9. network regimes Area covered # Nodes

10. network regimes Area covered Dense region: A MAC issue is Navigation QoS. # Nodes

11. network regimes Unpartitioned Multi-Hop region: MAC issues are throughput, energy, delay. Area covered Dense region: A MAC issue is Navigation QoS. # Nodes

12. network regimes DTN region: MAC issue is long-term average fairness. Unpartitioned Multi-Hop region: MAC issues are throughput, energy, delay. Area covered Dense region: A MAC issue is Navigation QoS. # Nodes

13. energy • Sensor networks often optimized to minimize energy. • Here, transmit energy dominates receive energy One example (WHOI Micromodem): • 80mW detection • 80mW – 2W receive (80bps – 5kbps) • 10W-50W transmit (~2km @ 25kHz) • as low as 1W transmit (~500m @ 25kHz) →Transmit energy is the important metric

14. energy • Propulsion energy can dominate communication energy REMUS: 1.0-2.9 m/s, 5-20 hours Hotel load: ~30W Propulsion: 15-110W Network energy negligible. Webb Research glider (electric): 0.2-0.4 m/s, ~1 month Hotel + Propulsion: ~2W Energy is important metric.

15. conclusions & questions? • Sparsity and mobility will be enduring features of many underwater networks: • New operating regimes and metrics for MAC, e.g. long-term fairness. • Protocol adaptation between dense and sparse areas. • Different energy costs: • Transmit energy dominates receive energy. • In many mobile networks, communication energy is negligible: • Optimize for new metrics, such as reliability metrics.