Underground Structure Monitoring with Wireless Sensor Networks

1. Underground Structure Monitoring with Wireless Sensor Networks Mo Li, Yunhao Liu Hong-Kong University of Science and Technology {limo,liu}@cse.ust.hk Date: 06th Dec. 2007 Presenter: KM Chen

2. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

3. Motivation • Over the last decade, collapses account for more than 50% of fatalities in U.S. in coal mine • The unstable nature of geological construction in coal mines makes underground tunnels prone to structure changes. • Environment monitoring in underground tunnels had been a crucial task to ensure safe working conditions in coal mines. • There is a need to develop a wireless sensor network system to quickly detect the collapse hole regions and accurately provide location references to evacuate workers from the dangerous zone.

4. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

5. Design of SASA (1/3) a) Stationary sensor nodes are deployed on the walls and ceiling of tunnels to form a mesh network.

6. Design of SASA (2/3) b) - Unfold 2 walls of the tunnel and builds a 2-D representation of the 3-D deployment on the inner surface of the tunnel. - Nodes are configured with 2-D coordinates on the unfolded 2-D surface then transformed into the 3-D corresponding locations with the knowledge of longitudinal section.

7. Design of SASA (3/3) c) - The distance between 2 nodes in the 3-D real environment is less than or equal to the distance between the pair in the unfolded 2-D view. - The real connectivity of the sensor network is no less than shown in the 2-D representation.

8. Definitions and Theorem • Edge node: A node defines itself as an edge node if the 2 adjacent neighbor nodes are detected lost during a time period. • Hole Polygon: The largest polygon outlined by the collapsed sensor nodes with every edge ending at 2 adjacent nodes. • Theorem: The convex hull of edge nodes in SASA encloses the hole polygon. Convex Hull Hole Polygon Edge node (2 adjacent neighbors are detected lost)

9. Detecting and locating the collapse hole • Goal: Let sensor nodes to maintain a set of their neighbors. When nodes detect a loss of neighbors, a hole is detected. Collapse hole

10. Detecting and locating the collapse hole Question: How to maintain a neighboring set? Node Beaconing Mechanism • Each node maintains a neighboring nodes list in memory. • Each node periodically broadcasts beacon messages that include its location. • Upon receiving a beacon message, the node updates the corresponding entry. • If a node fails to update an entry in a fixed time interval, then it represents the loss of the neighbor

11. Detecting and locating the collapse hole Problem: • It was observed that the neighbor set of a node is highly unstable, even if all the nodes work normally. Solution: • SASA deploys sensor nodes in a cellular hexagonal placement such that the node distribution is uniform. • Every pair of adjacent nodes are separated by the same interval . • Each node is limited to maintain a neighbor set to the 6 adjacent nodes.

12. Detecting and locating the collapse hole • How to define a hole detection? • Only failure of at least 2 adjacent nodes are necessary to define an edge node. Nevertheless, if 2 adjacent nodes fail simultaneously, a hole is detected • What about single node failure? • Unfortunately a small hole affecting only 1 sensor node can not be detected.

13. Accident reporting Goal: When edge nodes detect a hole, they report to the sink with the locations so that the hole can be illustrated by calculating the convex hull. Problem: • Create traffic peak and increase collision domain. Solution: Randomized Forward Latency and Data Aggregation • Insert a flag into the beacon messages, which indicates whether the beaconing node is an edge node. • Upon receiving other edge nodes’ beacon message, an edge node records them locally. • When this edge node sends out its report message, it aggregates all the recorded locations of its nearby edge nodes. • If an edge node receives a report message containing its own location, it simply forward this message instead of creating a new one. • The sink will send out reply to limit the number of retransmission.

14. Displaced node detection and reconfiguration Goal: Rapidly detect displaced nodes and reconfigure with correct locations in order to maintain system validity. Centralized approach • When the sink receives report messages with the edge nodes locations and approximate the hole region, it broadcasts the convex hull area. • Every node within the convex hull will start detecting its surroundings and check its location from beacon message. • If the 2 locations differ beyond some threshold, then it knows it’s being displaced.

15. Displaced node detection and reconfiguration Distributed approach • There are 3 types of edge nodes: • Edge nodes that lose neighbors but themselves do not move • Since their locations are correct, they don’t need to be reconfigured • Edge nodes that fall into an area where no normal node exists • They have no impact on normal nodes, they do not need to be reconfigured either. • Edge nodes that fall into other normal node range • Stop beaconing . This operation will lead the neighboring displaced nodes to become edge nodes, if they are not yet.

16. Displaced node detection and reconfiguration • Centralized approach Advantage: Short latency when the hole is closer to the sink • Disadvantage: May suffer long latency and low accuracy due to high link loss rate in coal mine, especially when a collapse area in a long tunnel is far from the sink. Distributed approach Advantage and disadvantage: Independent of the distance to the sink. In summary: • Combining both algorithms provides efficient and reliable for various situations • Turn them off or reconfigure their locations to conform to their new positions.

17. Displaced node detection and reconfiguration Location Calculation: • Suppose A and B drop into a new area surrounded by 3 resident nodes. • When A first detect the surrounding 4 nodes, it calculates a new location as (32.5, 19.25) and replaces the original locations. • When B detects its surroundings, it utilizes the new location of A and calculate a new location as (15.63, 11.56). • When A iteratively calculates its new location, it will get a more accurate result of (11.41, 7.14)

18. Displaced node detection and reconfiguration (10, 17) A (100, 100) A (32.5, 29.25) A (11.41, 7.14) (20, 0) (0, 0) B (100, 120) B (15.63, 11.56)

19. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

20. Hardware • Mica2 platform developed at UC Berkeley • The MPR400 radio board employed has a 7.3 MHz microprocessor. • 128K bytes of program flash memory. • 512K bytes of measurement flash memory. • 868/916 MHz tunable chipcon CC1000 multi-channel transceiver with a 38.4 kbps transmitting rate is employed for wireless communication with a 500 foot outdoor range

21. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

22. Application Scenario • Cooperate with S.H. Coal Corporation and selected the D.L. coal mine as the experimental environment. • D.L. coal mine is the is one of the mist automated coal mines, yielding the second largest production of coal worldwide. • Slightly sloped 14-kilometer long main tunnel from the entrance above the ground surface and goes 200 meter deep underground.

23. Application Scenario Requirements for SASA implementation in D.L. coal mine Remote management: Remotely maintain and manage the entire monitoring system, efficient and robust communications and routing mechanisms are required under all conditions. In-Situ interactions: Besides stationary sensors deployed on the walls, poles and floors, miners carry mobiles sensors providing real-time geographical references. Awareness of structure variations: Using node collaborating mechanism for collapse detection. Maintenance of system validity: Maintaining the validity of the monitoring system in extreme situation.

24. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

25. Experiment and Performance • A prototype system with 27 Mica2 motes is implemented in the D.L. coal mine. • It is distributed on a tunnel wall about 12 meters wide and 5 meters high. • Nodes are pre-configured with their location coordinates. • Nodes are placed in hexagonal mesh regulation

26. Experiment and Performance • Hole detection percentage: A hole is counted as undetected if less than 3 nodes reports are received by the sink. • Hole detection error: The error in distance between the real and detected position of the hole region. • Reconfiguration error (2-D or 3-D): Localization error in the reconfiguration process.

27. Experiment and Performance • Over 80% of the detected holes are located within 1 meter from its real position and 99+% are less than 2 meters. • All the 2-D reconfiguration errors and over 80% of the 3-D reconfiguration errors are below 3 meters

28. Experiment and Performance • Detection latency: Time from when the hole emerges until it is detected • Turn-off latency: The latency when the displaced nodes were turned off. • Reconfig latency: Latency when reconfiguring the displaced nodes according to the normal nodes surrounding them. • Short beacon interval leads to short latency. However, frequent beaconing brings large overhead, heavy collisions and increased packet loss

29. Experiment and Performance • The packet loss rate rapidly drops as the beacon interval increases while under short beacon intervals (0.8s), then becomes stable around a fixed level. • The loss rate is heightened as the exerted traffic overhead increases.

30. Experiment and Performance • Observations: • We can carefully select a proper beacon interval for a specific application workload to balance communication quality and the processing latency • Shorter beacon interval to reduce the processing latency if the application workload is light. • Longer beacon interval to reduce the packet loss rate if the application workload is heavy.

31. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

32. Simulation • 2000 nodes were simulated on a 1000m x 20m plane. • Nodes were placed in a hexagonal mesh regulation with 3 meter interval between each node. • A transmitting rate of 16 packet/s is used in the simulation for the nodes’ communication channels.

33. Simulation • Detection error is stable as slightly decreases as the hole size increases. • Larger hole includes more edge nodes, giving a more accurate outline of the hole region. • When the hole size increases, the outline of the edge nodes becomes tighter therefore the precision is dramatically increased.

34. Simulation • When the hole is close to the sink, the centralized algorithm benefits from rapid information collection and reaction from the sink. • When the hole is far away from the sink, centralized algorithm suffers from the round-trip time from the sink. The distributed algorithm is not affected. • As the packet loss rate between any 2 communicating nodes, and the random node failure rate increase, the misreport ratio also increases. • Need to decrease the beacon frequency in order to preserve a better communication channel.

35. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

36. Related Work • Wireless sensor networks for habitat monitoring [A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J. Anderson] • A Wireless Sensor Network for Structural Monitoring [N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad ] • Hole problem in wireless sensor network [N. Amed, S. S. Kanhere and S. Jha] • Coverage hole • Routing hole • Jamming hole • Sink/black/worm holes

37. Outline • Motivation • Overview of Structure-Aware Self-Adaptive sensor system (SASA) • Detecting and locating the collapse hole • Accident reporting • Displaced node detection and reconfiguration • Hardware • Application Scenario • Experiment and Performance • Simulation • Related Work • Conclusion

38. Summary and Future Work • By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on the regular beacon strategy, SASA is able to rapidly detect structural variations caused by underground collapses • The collapse holes can be located and outlined, and the detection accuracy is bounded. We also provide a set of mechanisms to discover the relocated sensor nodes in the hole region. • How to organize mobile nodes to form efficient collaborative groups is a challenging issue. • Single hole detection.