Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor - - PowerPoint PPT Presentation

Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor Networks

Jun Xu, G¨ urkan Solmaz, Rouhollah Rahmatizadeh, Damla Turgut and Ladislau B¨

• l¨
• ni

Department of Electrical Engineering and Computer Science University of Central Florida - Orlando, FL

October 24, 2015

Jun Xu (UCF) LCN 2015 October 24, 2015 1 / 21

Outline

1

Introduction Motivation & problem statement

Jun Xu (UCF) LCN 2015 October 24, 2015 2 / 21

Outline

1

Introduction Motivation & problem statement

2

Problem analysis Clustering Network modeling Value of information

Jun Xu (UCF) LCN 2015 October 24, 2015 2 / 21

Outline

1

Introduction Motivation & problem statement

2

Problem analysis Clustering Network modeling Value of information

3

Proposed path planning approach Markov decision process Path planning process for UAV

Jun Xu (UCF) LCN 2015 October 24, 2015 2 / 21

Outline

1

Introduction Motivation & problem statement

2

Problem analysis Clustering Network modeling Value of information

3

Proposed path planning approach Markov decision process Path planning process for UAV

4

Simulation study Simulation setup and metrics Demo display Performance results

Jun Xu (UCF) LCN 2015 October 24, 2015 2 / 21

Outline

1

Introduction Motivation & problem statement

2

Problem analysis Clustering Network modeling Value of information

3

Proposed path planning approach Markov decision process Path planning process for UAV

4

Simulation study Simulation setup and metrics Demo display Performance results

5

Conclusion

Jun Xu (UCF) LCN 2015 October 24, 2015 2 / 21

Motivation

Animal monitoring have various goals: Tracking their migration paths Predict if specific endangered species exist Goal of this application: Providing reliable animal appearance information in large-scale areas Do not using mounting devices & not affecting animal activities

Jun Xu (UCF) LCN 2015 October 24, 2015 3 / 21

Problem statement

How to find these animals? Sensors can not directly send data to remote base station How the sink (UAV) knows which sensors have the relevant information How to use those sensed information? Latency between animal appearance and information being gathered How to quantify this information

Jun Xu (UCF) LCN 2015 October 24, 2015 4 / 21

Clustering

Real movement trajectories of 4 zebras in 3 days † Wildlife animals are more likely to having activities in a small area

†Yong Wang, Pei Zhang, Ting Liu, Chris Sadler, Margaret Martonosi, CRAWDAD dataset princetonzebranet (v. 02/14/2007), traceset: movement, downloaded from http://crawdad.org/princeton/zebranet/20070214/movement, doi:10.15783/C77C78, Feb 2007. Jun Xu (UCF) LCN 2015 October 24, 2015 5 / 21

Clustering

Real movement trajectories of 4 zebras in 3 days † Wildlife animals are more likely to having activities in a small area

†Yong Wang, Pei Zhang, Ting Liu, Chris Sadler, Margaret Martonosi, CRAWDAD dataset princetonzebranet (v. 02/14/2007), traceset: movement, downloaded from http://crawdad.org/princeton/zebranet/20070214/movement, doi:10.15783/C77C78, Feb 2007. Jun Xu (UCF) LCN 2015 October 24, 2015 6 / 21

Network model

Cluster-heads are responsible for receiving data from other sensors and submitting data to the UAV Single UAV communicates with cluster-heads

Jun Xu (UCF) LCN 2015 October 24, 2015 7 / 21

Value of information

The value of information (VoI)† Sensed information has the high- est value when event occurs Our goal is maximizing the VoI in the whole network A: the initial value of the information B: the decay speed of the VoI

†Turgut, Damla, and Ladislau B¨

• l¨
• ni. ”A pragmatic value-of-information approach for intruder tracking sensor networks.”

Communications (ICC), 2012 IEEE International Conference on. IEEE, 2012. Jun Xu (UCF) LCN 2015 October 24, 2015 8 / 21

Markov decision process model

5-tuple {S, A, P, R, γ}: S is the set of states (grids) in the network A is the set of possible actions that UAV can do P is the state transition probabil- ities R is the instant reward when the UAV enters one gird γ ∈ [0, 1) is the discount param- eter

Jun Xu (UCF) LCN 2015 October 24, 2015 9 / 21

Markov decision process model

Solved this MDP model by Q-learning 9 possible actions of S4: 8 neighbors and staying itself Q(s, a) = R(s) + γ max

a′ Q(s′, a′)

Instant reward R(s), future potential re- ward Q(s′, a′) Possible actions of S4: {Northwest, North, Northeast, West, Stay, East, Southwest, South, Southeast}

Jun Xu (UCF) LCN 2015 October 24, 2015 10 / 21

Path planning flow chart

Exploitation: deter- ministic grid selection by Q(s, a)value Exploration: random grid selection ǫ: random selection probability

Jun Xu (UCF) LCN 2015 October 24, 2015 11 / 21

Simulation setup

Movement traces of zebras:

◮ ZebraNet project † ◮ 5 zebras in June 2005 at a 10km × 10km area near Nanyuki, Kenya ◮ 5682 GPS records in total ◮ GPS sampling time interval: 1 minute

Definition of sensing events:

◮ If zebra switches grid, record the event ◮ If zebra always stays in one grid, record every ∆t time †Yong Wang, Pei Zhang, Ting Liu, Chris Sadler, Margaret Martonosi, CRAWDAD dataset princetonzebranet (v. 02/14/2007), traceset: movement, downloaded from http://crawdad.org/princeton/zebranet/20070214/movement, doi:10.15783/C77C78, Feb 2007. Jun Xu (UCF) LCN 2015 October 24, 2015 12 / 21

Simulation setup

Simulator:

◮ Java-based discrete time simulator

Performance metrics:

◮ Value of information ◮ Average message delay ◮ Number of zebras encountered

Approaches for comparison:

◮ Greedy ◮ Traveling salesman problem ◮ Random Jun Xu (UCF) LCN 2015 October 24, 2015 13 / 21

Simulation setup

Network size 10km × 10km Number of grids (states) 16 Grid size 2500m × 2500m Unit experimental time (round) 10s UAV speed 100m/round Decay speed of VoI (parameter B) 0.05 Radius r for direct observation 200m Initial reward IR (σ, Ci, Idist, Iduration) 10.0 (10.0, 1.0, 1.0, 1.0)

Jun Xu (UCF) LCN 2015 October 24, 2015 14 / 21

Demo display

Jun Xu (UCF) LCN 2015 October 24, 2015 15 / 21

Value of information

500 1000 1500 2000 2500 3000 200 400 600 800 1000 1200 1400 Simulation time (min) Value of information MDP Greedy TSP Random MDP (Markov decision process) Greedy (Greedy total number of pre- vious events) TSP (Traveling salesman problem) Random (Random selection from all grids) Jun Xu (UCF) LCN 2015 October 24, 2015 16 / 21

Average message delays

MDP Greedy TSP Random 10 20 30 40 50 60 Average delay (min) MDP (Markov decision process) Greedy (Greedy total number of pre- vious events) TSP (Traveling salesman problem) Random (Random selection from all grids)

TSP: 0 deviation because fixed route

Jun Xu (UCF) LCN 2015 October 24, 2015 17 / 21

Number of zebras encountered

MDP Greedy TSP Random 50 100 150 200 250 Number of zebras encountered MDP (Markov decision process) Greedy (Greedy total number of pre- vious events) TSP (Traveling salesman problem) Random (Random selection from all grids)

Direct observation radius (r)

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Performance stability

500 1000 1500 2000 2500 3000 500 1000 1500 Simulation time (min) Value of information MDP−1 MDP−2 MDP−3 MDP−4 Results from 4 time experiments Same parameters Jun Xu (UCF) LCN 2015 October 24, 2015 19 / 21

Exploration

500 1000 1500 2000 2500 3000 500 1000 1500 Simulation time (min) Value of information ε = 0.2 ε = 0.4 ε = 0.6 ε = 0.8 ǫ − Greedy policy The probability of random grid selec- tion by UAV

Impact of exploration

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Conclusion

We focused on the animal monitoring in large area We proposed a MDP-based approach for UAV path planning The evaluation indicated significant improvement compared to Greedy, TSP and Random Future work:

◮ Other species, other dataset ◮ Multi-UAVs Jun Xu (UCF) LCN 2015 October 24, 2015 21 / 21