nittel keynote wagening08

Geosensor Networks: New Challenges in Environmental Monitoring

using Wireless Sensor Networks

Silvia NittelSpatial Information Science & Engineering

University of Maine, USA

Motivation

� Trends:� Miniaturization of microelectronics

� Wireless communication

� Developments of new materials & sensors

� Consequences:� Embedding devices into almost any man-made and some natural devices,

and

� connecting the device to an infinite network of other devices, to perform tasks, without human intervention.

� Information technology becomes omnipresent.

� �”Ubiquitous spatial computing”: The idea that technology is to move to everyday devices with embedded technology and connectivity as computing devices become progressively smaller and more powerful.

Silvia NittelSensing in a Changing World, 2008

Sensors and sensor material

� Microsensors (MEMS)� 1/1,000 mm size� Thin films of materials, photolithography and etching� Plethora of novel biological and chemical sensors

� Soon: Nanotechnology sensors� 1/1,000,000 mm size� Molecular structure and assembly� Physical chemistry, materials science

or biomedical engineering

� Small sensors + tiny computers� Sensor Networks

Carbon

nanotubeSilvia NittelSensing in a Changing World, 2008

Sensor Nodes

� Sensing, processing, communication

� Nodes can just be relaying information, or locally process information

� Reusable, re-programmable systems

CPU

Battery

Radio FlashMemory

“Smart dust”Mote

It’s just the beginning…

Crossbow IRIS Mote

Crossbow Mica Mote

weC Mote


Sensor Networks

� Building of “sensor networks” and interoperable “sensor webs”

Thanks to S. Madden, MIT


� Sensor networks for environmental phenomena in geographic space� Real-time data at novel spatio-temporal scale �“environmental microscope”

� Example applications:� Coastal monitoring and ocean exploration

� Mapping ocean floor� Coastal monitoring

� Drought management� Forest fire detection� Precision agriculture� Habitat monitoring

� A component of Sensor Webs

Geosensor Networks


Impact

� Novel information technology, new micro-sensor technology, and scalable wireless sensor networks lead to a

� � Major paradigm shift:� Spatially and temporally dense monitoring allows to

1. observe phenomena that were not observable before, and

2. study complex real-world systems and processes across a wide range of spatial and temporal scales

3. Due to power and computation constraints “compute data in the network”.

4. Analyze data in real-time

5. Actuate in small spaces and at fine-grained scale


Sensor Database System


Data collectionin sensor networks

Data Collection Tasks

� Today major geosensor network tasks: data collection1. Monitor the entire covered region and report data

2. Report “interesting data”� Events and phenomena

� Types of monitored phenomena:

� Continuous: toxic cloud within a city area� Discrete: did a truck pass or not?

� Types of data collection tasks:

� declarative spatial queries� Raw data queries

� Aggregation queries (min, max, avg)

� Data estimation queries � Qualitative queries (find event)

� Typical:� Communication >> computation (cost!)

� Push computation to the nodes andnetwork

SELECT MAX(temperat) FROM sensors

WHERE temperat > threshAND sensor.loc within

rect(p1, p2)SAMPLE PERIOD 64ms


Data Processing in Sensor Networks

A

B C

D

FE

Query

{D,E,F}

{B,D,E,F}

{A,B,C,D,E,F}

Data stream processing :

•Sample rate

•Temporal aggregation

•Uncertainty/inaccuracy

Each sensor node: �production of sensor data stream�processing of data stream locally�processing of aggregated data

�minimize communication

�Computation is pushed to data collection points:

�Local and locally-coordinated processing of data “in the network”

In-Network Data Aggregation

A

B C

D

FE

Query

{B’}

{A’} Each sensor node: •tree-based query routing & processing•each node is parent node and child node in the tree topology•child nodes send local results to their parent node•Parent node computes a partial state record (a partial aggregate based on all children, and grandchildren)

•forwarding of fixed-size messages•� In-network sensing and processing

{D’}

Partial state

record

Precision Agriculture with GSN

� Soil moisture monitoring in orchards� Fruit tree orchards in Victoria, Australia

� Soil moisture sensors in depth of 20cm, 40cm, and 90cm (not wireless!)

� sampling every 30min

� Remote area observation, and ‘health’ alerts by SMS

� Commercial tomato greenhouse, Backyard Farms, Madison, Maine� Vertical and horizontal wireless geosensor network

to

� capture light penetration and

� mix of natural and artificial growing light with regard

� to new and mature canopy

�


Habitat Monitoring

� Badger monitoring, UK (Oxford/Cambridge)� WildSensing project

� Zoologists detecting social behaviorial patterns of wild animals (e.g. movement patterns), in combination with microclimate conditions to protect the animals' habitat and ensure their well-being

� Badgers with RFID, static RFD readers, zoologist act as mobile sinks with PDAs

� Sheep flock management, NZ� FLAGS project, Australia (Flocking amongst GSN)

� Sheep herd monitoring and tracking

� Detecting flocks in order to disperse them (soil protection)


Challenges in (Geo-)Sensor Networks

� Characteristics:

� Highly constrained w.r.t. energy, computing power, communication and bandwidth. They are untethered, failure-prone.

� Now think a really large sensor network..

� Challenges:

� Design, deploy, and manage robust massively distributed systems composed of hundreds or thousands of physically-embedded devices

� Ad-hoc computation and collaboration of sensor nodes

� Adaptation and self configuration of sensor networks based on events (pre-configuration and global knowledge are not applicable)

� Self healing in case of hardware failure

� Pitfalls:

� Systems like this are hard to program

� Centralized, or even distributed control likely does not scale with large sensor networks (>1000 nodes)


Challenges in (Geo-)Sensor Networks

� Multi-objective optimization in GSN:

� Each limited sensor node:

� Local energy management

� Local sensing, data collection and processing

� Collaboration and coordination with neighboring nodes

� The sensor network as a whole:

� System life time and energy management

� Large phenomena sensing, and tracking

� Global change detection processing

� Paradigm shift in sensor networks:

� Decentralized decision making and collaboration

� Emergent global behavior and adaptation

local optimum

global optimum

Self organization


synchronize

colony behavior

Individual ant

Individual decisions:Self preservation & task participation

Emergent behaviorCollaborative tasks

Adapation

Feedback? effects


Ant Colony Routing

Leavingpheromone

markers

Initiallyrandom

path selection

Path selection based on strongest

pheromonemarkers


Self Organization in Geosensor Networks

� Small limited sensor nodes with local sensing and knowledge

� But large global phenomena

� A large toxic cloud

� A collapsing dike

� Formation of a flock pattern

� Challenge:

� How to implement local sensors intelligently to be able to make decision about local state and correct derivation about global state?


Boundary Detection

� Observing continuous phenomena

� Toxic cloud represented as a spatial field

� Approaches:1. Stream all sensing samples to a central base (expensive!)

2. Detect only the cloud boundary, but in the network (efficient)

� Activate only nodes around boundary as area of interest

3. Track topological changes of boundary in the network (more efficient)

� Topological changes: Expansion, shrinking, split, merge

� But: Global topological changes, but local knowledge and decisions – self organization

[GISience08]


Sheep Flocks

� FLAGS project, AUS

� Continuously moving herds of sheep in large, but dry spaces with little vegetations

� Motes attached to sheep

� Objective:

� A) monitor distance between sheep

� B) find occurring flocking patterns in real-time

� C) � locally actuate and disperse

� Notes:

� Unnecessary and inefficient to continuously stream all data to a central server since flock pattern detection is a local phenomenon

� Compute flock pattern “in-the-network”

� Actuate locally in the network

� How does a node know it is part of a flock?

Thanks to Laube/DuckhamSensing in a Changing World, 2008

Sensor Networks Everywhere!

� Sensing the environment, with continually smaller sensors

� Networked sensing of the environment

� Massive amounts of near real-time sensed data

� Large amount of distributed data sources

� Highly-dynamic localized information processing(people in public or private spaces)

� Decision making in a highly dynamic environment

� Autonomous and self-organizing sensor environments


Summary

� Ad-hoc, self organizing geosensor networks� Novel applications at increasingly smaller scale

� Plethora of new small-scale geosensors

� Self organization at small scale

� A middleware infrastructure to monitor physical space at various spatial and temporal scales with near-real-time data

� Integration with existing sensor environments

3rdConference on

Geosensor NetworksSummer 2009

in Oxford


Silvia NittelGI-Days, Muenster

Documents

nittel keynote wagening08