Locating Sensors in the Forest - Network and Systems Laboratory

Report
Locating Sensors in the Forest:
A Case Study in GreenOrbs
Tsung-Yun Cheng
20120611
Outline
•
•
•
•
•
Introduction
Preliminary experiments
System design
Performance evaluation
Discussion
Introduction
• GreenOrbs
– http://www.greenorbs.org/
– one of the world’s largest wireless sensor
networks
– monitor the forest condition
•
•
•
•
Temperature
Humidity
Illumination
Carbon dioxide
Introduction
• GreenOrbs
– Potential application
• Canopy closure calculation
• Climate change observation
• Search and rescue in the forest
– need the location information of sensor nodes
– Environmental noise
•
•
•
•
Illustrates
Temperature
Humidity
canopy closure
Introduction
• Environmental aware localization (EARL)
– Joint Neighbor Distance (JND)
• measure the distance between sensor nodes
– Neighbor node relation verification technology
• identifies nodes with good location accuracy
– Two-phases location calibration
• Rectify the node locations
– Implementation
• 20% better than existing work
Preliminary experiments I
Preliminary experiments I
• Consider the relationship between RSSI and
three parameters in the forest
– Temperature (0.0613)
– humidity
(0.0907)
– Illumination (0.1325)
• The relationship is quite hard to capture
– Taking temperature, humidity, illumination and
RSSI into account, it is quite difficult to estimate
the distance between nodes
Preliminary experiments II
• Experiment in different environments
– Grass, Woods, Forest
• Exam the RSSI value in different power level
– Put two nodes in three environments
– Distance = 10 meter
• Exam the reachability of RSSI
– One anchor nodes in the center
– 10 nodes are deployed around in every 5 meters,
ranging from 5 meters to 50 meters
Preliminary experiments II
Preliminary experiments II
Preliminary experiments II
• Exam the RSSI value in different power level
– The variance is large
– E.g., Figure 5(a) the Grass case:
• -40dBm to -35dBm when the power level is 4
• -29dBm could range from the 9th to 14th RF power level
• Exam the reachability of RSSI
– When the RF power level increases, anchor node
could reach more neighboring nodes
– In the forest, many curves share the similar RSSI
according to the different power level
• After checking the location, they are in the same area
Preliminary experiments
• RSSI is quite susceptible to environment
– the distance cannot be well computed directly
• RSSI sensing results just can be used as an
indicator for the relative “near-far”
relationship
System design
• Determine Neighbor relationship
– A near-far ordering relationship
•
•
•
•
Obtained by RF power scanning
Neighboring sequence
e.g., {G, C, E, B, F, D}
One-hop neighbors
– Doesn’t show:
• how far the distance
• direction
System design
• Joint Neighbor Distance (JND)
– estimate the distance of each pair of nodes
–
–
= Neighbor Count of Xj with respect
to Xi
– E.g.:
• NC(A, B) = 4
• NC(B, A) = 5
• JND(A,B) = 7
System design
• Calculate the coordinate using JND
– relative distance turns to the smallest
accumulated JND
– Choose some landmark nodes
• Known position
• Calculate JND-unit
– Compute the distance to the landmark nodes
•
• Trilateration by least square estimation
System design
• Testbed in the wood
– 50 nodes, 4 landmark nodes
– 1.3 meter above the ground
System design
• Testbed in the wood
– The boundary nodes have smaller neighbor nodes
– Also the nodes near the obstacles
System design
• Calibration
– Empirically, nodes with small neighbor count will
lead to the great error of locations, e.g., boundary
nodes
– When RF power level increases, the transmission
radius increases none-linearly when obstacles
exist
• more than one neighboring nodes may be added into
the neighbor sequence at same level
System design
• Calibration for boundary nodes
– Detection
• Select every nodes as root to establish a tree
• Leafs is the possible boundary nodes
• Pi larger than certain threshold => boundary node
– Calibrated neighbor count
• Virtual NC:
• j is the nearest neighbor
• CNC = Max{VNC, NC}
System design
• Calibration for good nodes & bad nodes
– Get correct neighbor sequence: Two-step process
• Group the neighbor nodes according to the appearance
of the RF level e.g., ((A), (G), (B, C, E), (D, F))
• In the same group, RSSI value is measured to get a
precise neighbor nodes sequence
– Get a JND scheme sequence
• Use JND localization scheme to compute the distance
– Compare the two sequence
System design
• Calibration for good nodes & bad nodes
– comparison
• Longest common subsequence
• good nodes > bad nodes
• Set a certain threshold
– Calibrate bad nodes
• Don’t mention…
System design
• Calibration for good nodes & bad nodes
– Calibrate good nodes: Reverse-localization
• Iteratively choose four of good nodes as the landmark
nodes, compute the location of four original landmark
nodes
• Find the four goods nodes with minimum error
• calibrate the location
of good nodes using
8 landmarks nodes
Performance Evaluation
• Previous testbed (in the woods)
– Compare to other works: DV-Hop, CDL
– Mean error
• EARL: 5m, CDL: 9m, DV-Hop: Large (~=18m)
Performance Evaluation
• Testbed in the forest
– 230 nodes, 4 landmark nodes
– Mean error
• EARL: 9m, CDL: 12m, DV-Hop: Large(~=20m)
Performance Evaluation
• more landmark nodes help improve the
localization accuracy
Discussion
• Network is highly affected by the complex
environment factors
• Environmental aware localization
scheme ,EARL, takes the joint neighbor count
to measure the distance between two nodes
and compute the location of nodes
• EARL outperforms existing approaches in
terms high accuracy and efficiency
Discussion
• Writing
– Structure is weird
• Content
– Don’t mention how they come up with these
approaches and why they adopt these methods
– Don’t mention how to calibrate the bad nodes
Q&A
~ Thank you for your attention ~

similar documents