Underwater Acoustic Sensor Networks: Medium Access Control, Routing and Reliable Transfer

1. Underwater Acoustic Sensor Networks: Medium Access Control, Routing and Reliable Transfer Peng Xie Dissertation Proposal Committee: Jun-Hong Cui, Reda A Ammar, Sanguthevar Rajasekaran, Bing Wang Computer Science & Engineering University of Connecticut

2. Outline • Introduction • Motivation & challenges • Three fundamental networking problems • Medium access control • Multi-hop routing • Reliable data transfer • Conclusions and future work

3. Why Underwater? • The Earth is a water planet • About 2/3 of the Earth covered by oceans • Largely unexplored, huge amount resources to discover • Many potential applications • Long-term aquatic monitoring • Oceanography, seismic predictions, pollution detection, oil/gas field monitoring … • Short-term aquatic exploration • Underwater natural resource discovery, anti-submarine mission, loss treasure discovery …

4. Application Requirements • Desired properties • Unmanned underwater exploration • Localized and precise data acquisition for better knowledge • Tetherless underwater networking for motion agility/flexibility • Scalable to 100’s, 1000’s of nodes for bigger spatial coverage

5. The Ideal Technique: Underwater Sensor Networks (UWSNs)

6. State-of-the-Art of UWSNs • Pioneering projects: • Seaweb, AOSN, SNUSE, NIMS • Current status: • Static sensor networks • Medium/long communication range • Small scale design and deployment • Demands for mobile UWSNs (M-UWSNs) • Submarine detection, estuary monitoring, etc.

7. Radio Buoys Data Report Acoustic Sonar Transmitter Application Scenario I Submarine Detection

8. Application Scenario II Estuary Monitoring Fresh Fresh Water Current Buoyancy Control Buoyancy Control Salty Water Current Salty

9. Underwater Communication Characteristics • Narrow available bandwidth • Radio is unsuitable for underwater sensor networks • Must use acoustic channels • High attenuation • Data rate x Range = 40 Kbps x Km • Very slow acoustic signal propagation • 1.5x103 m / sec vs. 3x108 m / sec • Causes large propagation delay

10. Research Challenges • UnderWater Acoustic (UW-A) channel: • Narrow available band: hundreds of kHZ at most • Huge propagation latency • High channel error rate • Random topology and sensor node mobility(1--2m/s due to water current) • Existing protocols in terrestrial sensor networks assume stationary sensor node • Mobility & UW-A channel limitations open the door to very challenging networking issues

11. Objective & Contributions • The final objective: • Build efficient, reliable, and scalable M-UWSNs • This dissertation work address three fundamental networking issues: • Medium access control (resolving collision efficiently) • Multi-hop routing (routing data to sink efficiently) • Reliable data transfer (improving network reliability) • This is the first Ph.D. proposal in the domain of underwater sensor networksat UCONN

12. Related Publications Medium Access Control • Peng Xie and Jun-Hong Cui, Exploring Random Access and Handshaking Techniques in Large-Scale Underwater Wireless Acoustic Sensor Networks , Proceedings of IEEE/MTS OCEANS'06, Boston, Massachusetts, USA, September 18-21, 2006 • Peng Xie and Jun-Hong Cui, An Energy-Efficient MAC Protocol for Underwater Sensor Networks, to-be-submitted Multi-hop Routing • Peng Xie and Jun-Hong Cui, SDRT: A Reliable Data Transport Protocol for Underwater Sensor Networks , UCONN CSE Technical Report: UbiNet-TR06-03, February 2006 • Zheng Guo, Peng Xie, Jun-Hong Cui, and Bing Wang, On Applying Network Coding to Underwater Sensor Networks , Proceedings of ACM WUWNet'06 in conjunction with ACM MobiCom'06, Los Angeles, California, USA, September 25, 2006 Reliable Data Transfer • Peng Xie, Jun-Hong Cui, and Li Lao, VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks , In Proceedings of IFIP Networking'06, Coimbra, Portugal, May 15 - 19, 2006 • Peng Xie, Jun-Hong Cui, and Li Lao, VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks , UCONN CSE Technical Report: UbiNet-TR05-03 , February 2005

13. Outline • Introduction • Motivation & challenges • Three fundamental networking problems • Medium access control • Multi-hop routing • Reliable data transfer • Conclusions and future work

14. Medium Access Control • General objectives: • Resolve collisions efficiently and effectively • Evaluation metrics: • Channel utilization • Energy efficiency • Fairness • Delay • … (More depending on applications)

15. Challenges in M-UWSNs • UW-A channel characteristics: • Long propagation delay • Signal cannot reach dest. instantaneously • Narrow communication bandwidth • Low data rate • Bandwidth must be shared by all nodes • Passive sensor node mobility • Dynamic neighborhood makes coordination very difficult if not impossible

16. Examine MAC Techniques • Contention-free approach • TDMA, FDMA, CDMA • Contention-based approach • Random access: ALOHA, slotted ALOHA • Collision avoidance with handshaking (RTS/CTS): MACA, MACA-W • We conducted a systematic study of random access and handshaking [Xie06:Oceans] • Random access: sparse networks & low data traffic • RTS/CTS: dense networks & high data traffic

17. Existing MAC Protocols for Underwater Sensor Networks • [Rodoplu05:Oceans]: • Network with ultra-low data traffic • Energy efficiency • Random access • [Molins06:Oceans]: • Sparse networks • Channel utilization • RTS/CTS-based

18. Our Solution • We propose R-MAC • A reservation-based MAC protocol • Targeted networks • Traffic unevenly distributed & sporadic • Energy-efficiency is the highest priority • Channel utilization is not a critical concern

19. Basic Idea of R-MAC • Each node works in cycles • Each node wakes/sleeps periodically • A node sends data to another node • Sender reserves a time slot in receiver • Receiver informs all neighbors of reserved time slot • Sender sends data in reserved time slot • How to make reservation? • Measuring propagation delays • Scheduling transmissions

20. The R-MAC Protocol • Three phases • Latency detection • Measure latencies between neighbors • Period announcement • Collect period start times of neighbors • Periodic operation • Reserve slot in intended node and send data

21. T1 Node A Node B L L T2 Phase I: Latency Detection • Latency between A and B is: L= (T1-T2)/2

22. LA A LB-LA+LAB LAB LB B Phase II: Period Announcement • Each node randomly selects period start time • Node B calculates difference of period start time of node A with its own start time LB-LA+LAB

23. Phase III: Periodic Operation (1) • Each node powers on (listen window) and off (sleep window) periodically • Data transmission is completed through REV/ACK-REV/DATA/ACK-DATA • ACK-REV is treated with the highest priority • The first part of the listen window is reserved for ACK-REV exclusively, called R-window • REV, DATA, ACK-DATA are scheduled to avoid the R-windows of all nodes in the neighborhood

24. Phase III: Periodic Operation (2) • The sender: • deliver REV to the target node in its listen window • specify the offset and duration of the reserved time slot for data transmission in REV • The receiver: • deliver ACK-REV to the sender in its R-window • reserve a timeslot for data transmission • deliver ACK-DATA after receiving data packets • Other nodes: • Back off if receiving the ACK-REVs or sensing collision in their R-windows

25. Sender in R-MAC C REV B REV A ST • Sender A schedules the transmission of REV to receiver B • Sender A specifies offset and duration of reserved time slot Reserved time slot

26. Silence C time slot B A Receiver in R-MAC • Receiver B schedules to send ACK-REVs to all neighbors • Sender A schedules the reserved time slot and Node C keeps silence in this time period ACK-REV ACK-REV

27. Performance Evaluation • Simulation settings: • Power consumption (UWM1000) • Tx:2 Watts, Rx:0.75 Watts, idle:8 mW • Data rate • 10kbps • Transmission range • 90 m • Performance metrics: • Goodput: • Number of packets successfully received by receiver • Overhead: • Energy consumption per data packet

28. Node 2 60 m Node 0 20 m 80 m Node 4 Node 3 20 m Node 1 Topology for Fairness

29. Fairness • All the nodes have almost equal goodputs

30. Node 2 40 m Node 0 20 m 30 m Node 4 Node 3 20 m Node 1 Topology for Energy Efficiency

31. Energy Efficiency • R-MAC is more energy efficient than T-MAC

32. Summary • R-MAC • is energy-efficient • can achieve fairness • guarantees data packets collision-free (formal proof)

33. Future Work • Improve robustness of R-MAC against noisy channels • Design efficient MAC solutions for mobile networks

34. Outline • Introduction • Motivation & challenges • Three fundamental networking problems • Medium access control • Multi-hop routing • Reliable data transfer • Conclusions and future work

35. Challenges in M-UWSNs • Hardest network environments for routing • Dynamic network topology • Large network scale • 3-dimensional space • High error probability • Energy constraint • Routing “voids”

36. Existing Routing Protocols for Terrestrial Sensor Networks • Protocols for terrestrial sensor networks: • Directed Diffusion (DD) • GRADient Broadcast (GRAB) • Two-Tier Data Dissemination (TTDD) • They are unsuitable for M-UWSNs • Dynamic network topology • 3-dimensional deployment

37. Our Solution • We propose Vector-Based Forwarding (VBF) • A scalable, efficient and robust geo-routing approach • The basic idea of VBF • Forwarding path represented by a vector • Node receiving packets • Calculate its relative position • Forward packets if close to the vector • Qualified nodes are in “routing pipe” • Controlled by pipe radius: W

38. VBF – An Illustration

39. VBF Enhancement • Observations in dense networks • Too many nodes involved in data forwarding • Solution: self-adaptation • Each node weighs the gain to forward a packet • Forwards packets adaptively • Benefits of self-adaptation • Reduce energy consumption • Reduce packet collision • Can find optimal path (formal proof)

40. Self-Adaptation Algorithm Sink(s0) D Ad Pd R B F W W Source(s1)

41. Performance Evaluation • Simulation settings: • 100×100×100 m3 cube • Transmission range: 20m • Source and sink are fixed • Other nodes are mobile • Performance metrics: • Success rate (measure robustness) • Communication time (measure energy cost)

42. Impact of Density and Mobility • VBF handles node mobility efficiently and effectively, and node density affects success rate and energy consumption significantly

43. Impact of Pipe Radius • When the pipe radius is large enough, VBF has the same success rate as naive flooding but with much less energy consumption

44. Robustness • VBF is robust against packet losses and node failures

45. Summary • VBF is • Energy efficient • Scalable • Robust (formal analysis)

46. Future Work • Improve VBF • Adapt to non-uniformly distributed networks • Propose solutions to avoid routing “voids”

47. Outline • Introduction • Motivation & challenges • Three fundamental networking problems • Medium access control • Multi-hop routing • Reliable data transfer • Conclusions and future work

48. Challenges in M-UWSNs • Hardest network environments for RT • Highly error-prone communication channel • Long end-end propagation delay • Half-duplex acoustic channel • Dynamic network topology • Energy constraint

49. Examining Common Wisdoms • End-to-end approach • not work well due to large RTT & high error probability • Half-duplex channels limit complex ARQ • can only use Stop & Wait protocols • enhanced version to improve channel utilization • S & W protocols with many feedbacks • have low energy efficiency • Pure FEC approach • usually not energy efficient

50. Our Solution • We propose segmented data reliable transport (SDRT) • A hybrid approach of FEC and ARQ • The basic idea of SDRT • Data are first grouped into blocks at source • Each block encoded in simple & efficient codes • Source keeps pumping encoded data into network till receiving a positive feedback in half-duplex channels • Block-by-block and hop-by-hop