Wireless Sensor Networks for High Fidelity Sampling Sukun Kim Electrical Engineering and Computer Sciences University of California at Berkeley Committee: David Culler, Ion Stoica, and Gregory Fenves Dissertation Talk May 14, 2007

High Fidelity Sampling  Three categories of WSN applications Monitoring environments  Great duck island [11], Redwood forest [12]  Focus on low-duty cycle and low power consumption Monitoring objects – High Fidelity Sampling  machine health monitoring [13], condition-based monitoring, earthquake monitoring [14], structural health monitoring [15]  Focus on fidelity (quality) of sample Interactions with space and objects  Lighting control [16]  Focus on control

Structural Health Monitoring  High Fidelity Data  High Frequency Sampling with Low Jitter  Time Synchronized Sampling  Large-scale Multi-hop Network  Reliable Command Dissemination  Reliable Data Collection FTSP [8] Mint [9] Broadcast [10] Challenges

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion With Rodrigo Fonseca, Prabal Dutta, Arsalan Tavakoli, David Culler, Philip Levis, Scott Shenker and Ion Stoica

Overview  Target applications Where transfer completion time is more important than latency of each data point Structural health monitoring, volcanic activity monitoring, bulk data collection  One flow at a time Reasonable restriction for target applications Remove inter-path interference Easier to optimize for intra-path interference

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion

Algorithm  Receiver-initiated  Selective-NACK  Rate Control  Separation of Concerns Correctness (all packets are delivered) Performance (achieve high bandwidth)

Reliability , 4, 5 4, 9

Rate Control: Conceptual Model Rate: Assuming disk model N: Number of nodes, I: Interference range

Rate Control d 8 = δ 8 + H 7 = δ 8 + δ 7 + f 7 = δ 8 + δ 7 + δ 6 + δ 5 1. At each node, Flush attempts to send as fast as possible without causing interference at the next hop along the flow 2. A node’s sending rate cannot exceed the sending rate of its successor

Interference Range > Reception Range However, Signal Strength Noise Floor + SNR Threshold Noise Floor + 2 X SNR Threshold SNR Threshold – minimum SNR to decode a packet Jammer – a node which can conflict with the transmission, but cannot be heard JammerVulnerable to JammerNo problem to Jammer

Identifying the Interference Set Fraction of Nodes CDF of the difference between the received signal strength from a predecessor and the local noise floor A large fraction of interferers are detectable and avoidable

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion

Implementation  RSSI is measured by snooping  Information is also snooped δ, f, D are put into packet header, and exchanged through snooping δ, f, D take 1 byte each, 3 bytes total  Cutoff A node i considers a successor node (i− j) an interferer of node i+1 if, for any j > 1, rssi(i+1) − rssi(i−j) < 10 dBm The threshold of 10 dBm was chosen after empirically evaluating a range of values

Implementation  16-deep Rate-limited Queue Enforces departure delay D(i) When a node becomes congested (depth 5), it doubles the delay advertised to its descendants  But continues to drain its own queue with the smaller delay until it is no longer congested

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion

Packet Throughput of Different Fixed Rates Effective Throughput (pkt/s) Packet throughput of fixed rate streams over different hop counts The optimal fixed rate depends on the distance from the sink

Packet Throughput of Flush Effective Throughput (pkt/s) Effective packet throughput of Flush compared to the best fixed rate at each hop Flush tracks the best fixed packet rate

Bandwidth of Flush Effective Bandwidth (B/s) Effective bandwidth of Flush compared to the best fixed rate at each hop Flush’s protocol overhead reduces the effective data rate

Fraction of Data Transferred in Different Phases Fraction of data transferred from the 6th hop during the transfer phase and acknowledgment phase Greedy best-effort routing is unreliable, and exhibits a loss rate of 43.5%. A higher than sustainable rate leads to a high loss rate

Amount of Time Spent in Different Phases Fraction of time spent in different stages A retransmission during the acknowledgment phase is expensive, and leads to a poor throughput

Packet Throughput at Transfer Phase Effective Goodput (pkt/s) Effective goodput during the transfer phase Flush provides comparable goodput as a lower loss rate which reduces the time spent in the expensive acknowledgment phase, which increases the effective bandwidth

Packet Rate over Time for a Source Source is 7 hops away, Data is smoothed by averaging 16 values Flush approximates the best fixed rate with the least variance

Maximum Queue Occupancy across All Nodes for Each Packet Flush exhibits more stable queue occupancies than Flush-e2e

Sending Rate at Lossy Link Both Flush and Flush-e2e adapt while the fixed rate overflows its queue Packets were dropped from hop 3 to hop 2 with 50% probability between 7 and 17 seconds

Queue Length at Lossy Link Flush and Flush-e2e adapt while the fixed rate overflows its queue

Route Change Experiment We started a transfer over a 5 hop path Approximately 21 seconds into the experiment forced the node 4 hops from the sink to switch its next hop Node 4’s next hop is changed, changing all nodes in the subpath to the root No packets were lost, and Flush adapted quickly to the change Flush adapts when the next hop changes suddenly 0 1a 2a 3a 1b 2b 3b 4 5

Scalability Test Effective Bandwidth (B/s) Effective bandwidth from the real-world scalability test where 79 nodes formed 48 hop network Flush closely tracks or exceeds the best possible fixed rate across at all hop distances that we tested

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion

Discussion  High-power node reduces hop count and interference Not an option on the Golden Gate Bridge due to power and maintenance problems  Interactions with Routing Link estimator of a routing layer breaks down under heavy traffic

Related Work  Li et al – capacity of a chain of nodes limited by interference using  ATP, W-TCP – rate-based transmission in the Internet  Wisden – concurrent transmission without a mechanism for a congestion control  Fetch – single flow, selective-NACK, no mention about rate control

Conclusion  A reasonable assumption (single flow) simplifies a problem (eliminates inter-path congestion control)  Light-weight solution reduces complexity Overhearing is used to measure interference and to exchange information Two rules to determine a rate  At each node, Flush attempts to send as fast as possible without causing interference at the next hop along the flow  A node’s sending rate cannot exceed the sending rate of its successor

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion With Shamim Pakzad, David Culler, James Demmel, Gregory Fenves, Steve Glaser, and Martin Turon

Node Layout (1 st phase)  Distance between nodes on the span is either 100ft or 50ft  Initially designed as 150ft Difference in MicaZ radio output power was up to 7.5dBm 8 nodes 56 nodes 1125 ft4200 ft 500 ft 246 ft SF (south) Sausalito (north)

Environment Fog Strong and salty wind Rapidly changing... high and scary

Node Node (Mote + Accelerometer Board) Battery (4 X 6V Lantern Battery) Bi-directional Patch Antenna

Node Extreme Rusting of C-clamp Zip tie around Antenna

Base Station Laptop Students At Work

Installation Hard Hat Harness Sharp Edge Ouch However… Crawling and Installing Done! Strong Wind

Table of Contents  Introduction  Flush: A Reliable Bulk Transport Protocol Algorithm Implementation Evaluation Discussion Related Work Conclusion  Deployment at the Golden Gate Bridge  Data from the Golden Gate Bridge  Conclusion

Reliable Data Collection at GGB Data is collected reliably over a 46-hop network, with a bandwidth of 441B/s at the 46th hop

Vibration Data of GGB The vertical modal properties match among (1) simulation model, (2) previous study, and (3) this study (1) (2) (3)

Conclusion  As a concrete example of HFS, SHM is designed, implemented and deployed  Requirements are identified and solutions are proposed  The system satisfied requirements, and provided meaningful data for the research of structural analysis

Bonus – Spectacular Views

Acknowledgement  David Culler  GGB – Shamim Pakzad, James Demmel, Gregory Fenves, Steve Glaser, and Martin Turon  Reliable Data Collection – Rodrigo Fonseca, Prabal Dutta, Arsalan Tavakoli, Philip Levis, Scott Shenker and Ion Stoica  Jaein Jeong, Xiaofan Jiang, Jay Taneja, Jorge Ortiz, Robert Szewczyk, Tom Oberheim, Anthony Joseph, Joe Polastre, Alec Woo, Kamin Whitehouse, Phil Buonadonna

Average Number of Transmissions per node for sending 1,000 packets

Bandwidth at Transfer Phase Effective Goodput (B/s) Effective goodput during the transfer phase Effective goodput is computed as the number of unique packets received over the duration of the transfer phase

Details of Queue Length for Flush-e2e

Memory and Code Footprint

The Golden Gate Bridge

More on Node Signal Splitter Antenna Cable To Base Station

(1) (2) (3) The torsional modal properties match among (1) simulation model, (2) previous study, and (3) this study

0 1a 2a 3a 1b 2b 3b 4 5