Environmental Monitoring Using Sensor Networks Christos Panayiotou, and Michalis P. Michaelides Dept. of Electrical and Computer Eng. University of Cyprus

Environmental Monitoring Using Sensor Networks

Christos Panayiotou, and

Michalis P. MichaelidesDept. of Electrical and Computer Eng.

University of Cyprus

Network Research LabDepartment of Electrical and Computer Eng.University of Cyprus

Motivation

Ecosystems and habitat monitoring

Air quality monitoring Water quality monitoring …


Monitoring the Impact of Urban and Agricultural Land Use.

Directives Article 8, WFD Directive (2000/60/EC) Monitoring

each river basin district. 91/676/EEC Nitrates EC Directive …


Overview

Source localization problem Wireless Sensor Networks Related work The source localization problem using sensor

networks (a first take). Some simulation results Conclusion and future work


Complex System

Human activities contaminate the environment to the point where entire ecosystems are destroyed and risking human lives. Diffusion of toxins and chemicals

into water, soils and sediments. Industrial activities may

contaminate air, soil and water Fertilizers, sewage disposal,

manure storage (nitrates and nitrites)

Biological indicators (macro invertebrates and other microbial contamination - Streptococcus count, E-Coli, Cryptosporidium)


Objective

Can we detect the contamination?

Can we locate the source of the contamination?

Can we locate multiple sources of contamination?

Can we control pollution?


Sensor Nodes

Simple and “fairly” cheap devices They consist of

Sensing unit – multiple onboard sensors in acoustic, seismic, IR, magnetic modes, imagers, micro radars

Data processing unit - storage Communication (transceiver) unit – wireless links to neighboring

nodes Power supply unit (battery) Optionally,

Location Identification Unit (e.g. GPS), Mobility platform, Power generation unit ()

They are capable of performing simple tasks


What Are Sensor Networks?

Sink

Internet or Satellite network

Internet or Satellite network

Task Manager Node

sensor field

sensor nodes

A collection of Sensor Nodes collaborating to perform a complex task


Characteristics of Sensor Networks

Limited Resources computational capability communication bandwidth power memory

Large number of nodes densely deployed Sensor nodes may not have global identification ID because of the

large amount of overhead and large number of sensors

Prone to failure (power outage) Topology may change frequently

Ability for self-organizing and self-configuring

Depending on the application, some or all of these characteristics may become critical.


Related Work in Sensor Networks

Remote Underwater Sampling Stations (RUSS) at Syracuse lakes Non-intrusive breeding habitat monitoring on Great Duck Island Temperature and humidity monitoring at Pickberry Vineyards SENSPOL: monitoring environmental pollutants in water, soil and

sediments. Oak Ridge National Laboratory in USA: Comprehensive incident

management system that will rapidly respond to a chemical, biological or radiological event.

Los Alamos National Laboratory: Sensor Network that will detect a motor vehicle carrying a Radiological Dispersion Device.

CSIP (Collaborative Signal Information Processing) deals with the energy constrained dynamic sensor collaboration.


Related work in plume tracking using unmanned vehicles

Source localization Bio-mimetic robotic plume-tracing algorithms based on

olfactory sensing (Homing, foraging, mate seeking) Basic steps in robotic plume-tracing

Sensing the chemical and sensing or estimating fluid velocity. Generating sequence of searcher speed and heading

commands such that the motion of the vehicle is likely to locate the odor source.

J. Farrell et al. uses an autonomous vehicle operating in the fluid flow capable of detecting above threshold chemical concentration and sensing fluid flow velocity


Vehicles vs. Sensor Networks

Static sensor network If plume passes by a

sensor, it is detected. Position of the source

needs to be remotely estimated using fusion techniques.

Energy constraints Need efficient routing

techniques to relay the information hop by hop to the sink.

Low cost.

Vehicle Search Spend a good amount of

time searching for the plume in reachable areas.

Plume tracking Potentially it can go very

close to the source. All necessary computation

can be done on-board. Once source location is

identified it returns to base to report.

High cost


Sensor Network Plume Tracking

Contaminant Source

Sensor nodes


Sensor Network Plume Tracking with Current

Current Direction Contaminant Source

Sensor nodes


Propagation model

N sensor nodes stationary, randomly placed in a rectangular field R with locations known ( xi , yi ).

Contaminant source ( xs , ys ) is somewhere inside R.

Propagation model fi(.) includes the concentration at the source, speed and direction of the wind or current and other environmental parameters.

Gaussian white noise

Measurement of sensor i at time t

Propagation model

, ,

2,

, , ,

0,

i t i i i i t

i t i

z f x y x y w

w N


Simple Simulation Model

, ,

2 2

2, 0, ,

i t i ti

i s i s i

i t

cz w

r

r x x y y

w N i t

Propagation of contaminant transport is uniform in all directions.

Additive Gaussian white noise.

α=2, c=106

Radial distance of sensor i from the source

Gaussian white noise

Concentration at source


Least Squares Estimation

Centralized approach: Sensor nodes calculate the mean of M measurements and then

send the computed mean to the sink. After the sink receives the information from all sensor nodes it

employs the nonlinear least squares method to compute an estimate of the source location by minimizing function J.

2

1 12 2 2

1,

N M

i i iti t

s i is

cJ z z z

Mx x y y


Simulation Results

2 2

, , , ,1

1ˆ ˆ

K

s k s k s k s kk

Error x x y yK

MATLAB simulation package K=100 randomly placed sources for each experiment Effect of varying number of sensors, noise variance and

number of measurement samples.


Least Squares Start Position

LS max start – start the minimization in the neighbourhood of the sensor node with the highest measurement.

LS random start – randomly pick 10 start positions in the sensor field.

LS combo – choose the method that minimizes the squared 2-norm of the residual.

CPA – Closest point approach The source position is the location of the sensor that

measured the highest concentration.


Random Sensor Field





Preliminary Conclusions

A large number of sensors is required to guarantee a good enough source position estimate


0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

800

900

1000SN original fieldSN selected for estimationPlume source

Wind

sw

Threshold

Active Area


Simple Propagation Model with Current

Only sensors in the active area A may detect the plume

When a sensor node is triggered by the presence of the plume it wakes-up, it takes a number of discrete measurements and calculates the mean.

Centralized Approach: If the mean exceeds a predefined threshold T the sensor communicates this value to the sink and continues measuring otherwise it goes back to sleep.

,

,

,

2,

if

Otherwise

0,

i tii t

i t

i t i

cw i A

rz

w

w N


Results with Current

LSp Least squares estimator with initial concentration known.

LSc Least squares estimator with initial concentration unknown. Use separable least squares techniques

Further improvements: LSu Unconstrained optimization LSw Constrained search based on wind direction




Threshold considerations…

Determines the number of sensors involved in the estimation.

Needs to be large enough to minimize probability of false alarms.

Needs to be small enough to ensure maximum detection probability.

Needs to be appropriately chosen to minimize energy consumption while not compromising estimation accuracy.


No detection


Decentralized Estimation

Event

SINK

Dynamic Cluster

Formation


The More Realistic Model

Once released at its source odor is carried by the wind to form a plume.

As the plume travels further away it becomes on average more dilute due to molecular diffusion.

Dominant cause of diffusion is turbulence. Characteristics of odor plume depend on physical environment.


Single Sensor Output

Graphical interpretation of the output of an odor plume with moderate turbulence.

Sensor was stationary at 10 cm downstream of the odor source and at the geometric center of the plume.


Conclusion and Future Work

Sensor network technology will allow us to monitor and better understand the environment

Future work Estimation using the more realistic model Decentralized approach Include mobile nodes to improve the network

coverage

Documents

Environmental Monitoring Using Sensor Networks Christos Panayiotou, and Michalis P. Michaelides Dept. of Electrical and Computer Eng. University of Cyprus