Locating Sensors in the Forest: A Case Study in GreenOrbs

Tsung-Yun Cheng20120611

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 ~