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Tracking Mobile Sensor Nodes in Wildlife Francine Lalooses Hengky Susanto EE194-Professor Chang.

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Presentation on theme: "Tracking Mobile Sensor Nodes in Wildlife Francine Lalooses Hengky Susanto EE194-Professor Chang."— Presentation transcript:


2 Tracking Mobile Sensor Nodes in Wildlife Francine Lalooses Hengky Susanto EE194-Professor Chang

3 Outline Type of sensors Tracking algorithm Applications of habitat monitoring ZebraNet Great Duck Island Our approach Expectations References

4 Type of Sensors Border sensors Never sleep Senses all the time Non-border sensors Sleep unless sense an event Can predict the next location Synchronization with time stamp Search for sensors using normal beam A Protocol for Tracking Mobile Targets using Sensor Networks, RPI

5 Tracking Algorithm: Characteristics No central point Use cluster-based architecture Sensor strength: Normal and high beam Assumptions: Randomly distributed sensors Default to normal beam Hibernation mode

6 Tracking Algorithm Algorithm: CH i+1 receives TD from CH CH i+1 stores TD in its own local database Based on TD, CH i+1 selects 3 sensors CH i+1 formulates TD based on input from its subordinates CH i+1 passes it to the CH i+2 Energy conservation: Only border nodes are awake Prefer to use normal beam TD only travels between CH to base station Failure: Lost ACKs between cluster heads 1. Use high beam 2. Wakeup sensors in radius r 3. Extend search to (2N-3)r CH CH i+1 CH i+2 CH = cluster headTD = target description

7 Tracking Algorithm Predictions Use at least 3 sensors per target Poisson rate algorithm p = Probability of at least 3 sensors per target  = Node density; r = normal beam radius Probability that arbitrary point inside the sensor network can be sensed simultaneously by at least 3 sensors with their normal beams should be close to 1

8 ZebraNet Track animals long term and over long distances All nodes mobile Light-weight energy efficient GPS enabled Wireless sensor net Peer-to-peer routing and data storage Plan for one year autonomous operation GPS chip and CPU8 grams Short-range radio20 grams Packet Modem140 grams Long-range radio156 grams Lithium-Ion batteries226 grams Solar cell array540 grams Total1090 grams (2.4 lbs) Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet, Princeton University

9 ZebraNet: Protocols for Data Aggregation Two peer-to-peer protocols were evaluated: Flooding: Every 3 minutes, zebras send to everyone discovered in their range. History-Based: Every 3 minutes, choose at most one peer to send within range; the one with best past history success rate of delivering data is chosen. Compared to “direct”: no peer-to-peer, just to base.

10 ZebraNet: Mobility & Protocol Summary Radio range key to data homing success: ~3-4km for 50 collars in 20kmx20km area Success rate: Ideal: flooding best Constrained bandwidth: history best Energy trends make selective protocols best

11 ZebraNet: Discovered Lifestyles Mostly: herbivores graze Sometimes: graze-walk while looking for greener pastures. Rare: To run to or away from something Water Thirsty once a day Model at random time Walk to nearest water After drink, resume ambient motion

12 Great Duck Island (GDI) On Maine's Great Duck Island, biologists put sensor devices in the underground nests (1) and on 4-inch stilts placed just outside their burrows (2). These devices record data about the birds and relay it to a gateway node (3), which transmits the information to a laptop in the research station (4), then to a satellite dish (5) and, ultimately, to an Intel Research lab at Berkeley California. Wireless Sensor Networks for Habitat Monitoring, Berkeley

13 GDI: Mica Hardware 32 UC Berkeley motes deployed (9 underground) Size: 2 x 1.5 x 0.5 inches Energy: Pair of AA batteries Monitoring runs for 9 months Interchangeability: Less than 3% variation when interchanged with others of same model Accuracy: Within 3% of the actual value Mica sensor nodeMica Weather Board - temperature - photo resistor - barometric pressure - humidity - passive infrared sensors

14 GDI: System Architecture Tiered Architecture Lowest level: Small battery powered sensor nodes collect data Gateway transmits sensor data from patch Solar powered Always on Base station provides WAN and data storage Base station connects to database replicas across the Internet Finally, data is displayed through user interface INTERNE T TRANSIT NETWOR K Client Data Browsing And Processing Base Station Base-Remote Link Gateway Sensor Node Patch Network Sensor Patch Data Service

15 GDI: Protocols & Discoveries Efficient routing for low duty cycle sensor network is sending data to gateway on scheduled periods, one direction communication Data collected from weather motes and burrow motes Temperature, relative humidity, solar radiation, voltage utilization, live sensor readings

16 Our Approach Improve the border layer energy conservation Better prediction algorithm Optimization of current failure/recovery algorithm Propose layered sensor net architecture Sensor node layers

17 Approach Overview Border layer energy conservation Issue: Only border nodes are awake Our approach to solve the problem Prediction Algorithm What is Prediction Algorithm? Challenges with prediction algorithm Failure and Recovery Algorithm Space decomposition Sweeping across region Challenges with both approaches

18 Expectations To conduct a study on one of the proposed topics Improve the border layer energy conservation Better prediction algorithm Optimization of current failure/recovery algorithm To avoid working on NP-complete and unsolvable problem in two months

19 References A Protocol for Tracking Mobile Targets using Sensor Networks. H. Yang and B. Sikdar. RPI, Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet. P. Juang, H. Oki, Y. Wang, M. Martonosi, L. Peh, D. Rubenstein. Princeton University, Processing in a Tired Sensor Network for Habitat Monitoring. H. Wang, D. Estrin, L. Girod, UCLA, 2002 Wireless Sensor Networks for Habitat Monitoring. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, J. Anderson. Berkeley, Computational Geometry. M. De Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf. Utrecht University, Wireless Sensor Networks. F. Zhao, L. Guibass. Microsoft and Stanford University, Networking Issues in Wireless Sensor Networks. D. Ganesan, A. Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrin. UCLA, 2003.

20 Questions

21 Backup Slides

22 Border Layer Energy Conservation Issue: Only border nodes are awake Propose study: Divide outer layer into several layers Each layer takes turn to watch Each layer shares the border watch task The impact of multiple layers border watch How to determine which sensors belong to which layer in the cluster implementation

23 Prediction Algorithm Is it necessary to implement a prediction algorithm? Yes To conserve energy and allow only the relevant sensors to wake up Tracking the object effectively Better network performance and data mining strategy

24 Issues in Prediction Algorithm How many sensors should be involved? At least 3 sensors vs. the entire cluster The impact of landscape (river, cliffs, etc) Effectiveness tracking strategy vs. energy conservation What is the trade off? History improves the correctness of prediction algorithm. Issue: Requires more energy for computation Requires storage space to store history

25 Aspects of Prediction Algorithm Speed and velocity affects the direction of the objects Very Fast: Likely to go in a straight line Fast: More likely to turn Medium and slow: High probability to turn and potential error in predicting the next location Stop When the target is not moving or stopped How many sensors are needed? Single object Multiple objects in the area How to conserve energy? Taking turns to monitor target Tell everybody on the routing path to sleep vs. keeping everyone in the ready position

26 Aspects of Prediction Algorithm When the target is not moving or stopped When to cancel reservation if QoS is provided If no QoS, who is responsible for storing data The cluster head vs. each sensor The impact of storing data at cluster head and individual sensor Tracking multiple targets in the same location (e.g tracking a dog and zebra in the same location) How to predict their next location (assuming they are going to a different location) How multiple targets impact prediction algorithm Higher error tracking the target One object might cause the other object to choose different direction Number of sensors needed if they are idle

27 Failure and Recovery Algorithm: Approaches to Find Lost Target Space decomposition It is quicker to find the lost target It takes O(log n) running time for a successful search It guarantees to find the lost target if the target is still in the region Awake every nodes in the region Not energy efficient Costly Creates traffic in network Sweeping across the region Sweeping outward from the last seen position to the border node Only notifies their neighbor at the outer layer Perform a short overlap layer search for fault tolerance When successful: Broadcast to stop the search The founder node takes over the tracking When target is not found, border sensors report to base station

28 Failure and Recovery Algorithm Issue Problem in sweeping across the region Running time is O(n) for a successful search Target might be able to trick the algorithm While search is performed, the target might leave monitored area  Waste of searching effort Target might move faster than the sweep High chance of flooding the network High probability of waking the entire sensors

29 Failure and Recovery Algorithm Propose study: Combining Sweep and Space Decomposition search Algorithm under investigation Plane sweep algorithm Topology sweep algorithm Convex hull 3-dimension Searching for more suitable algorithm

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