An Intelligent and Adaptable Grid-Based Flood Monitoring

and Warning SystemPhil Greenwood

The Problem Flooding is becoming a more common

occurrence Climate change Land use

Cost of damage correlates with Rate of flow Depth of water Warning time given

To cope with this problem initiatives taken to:

Improve flood defences Raise public awareness Improve flood warning systems

Traditional Approaches

Deploy sensors at flood prone sites Collect data manually or transmitted

using GSM technology Data then processed using spatial or

point-based prediction algorithms The results from these algorithms

can be used to issue flood warnings

Limitations Rigid separation between on-site sensor

network and off-site computation Grid Tends to be bottleneck

The sensors used are computationally dumb They simply record and store/transmit data Data holds valuable information on how the

sensors should behave No variation in the sensor behaviour

possible Turn device off when un-interesting events

occurring Increase frequency of measurements made No dissemination of warnings

Proposed Approach Increase local computational power of

sensors Allow the local execution of flood

prediction algorithms i.e. light-weight Grid Adaptation of the wireless sensor network

Support a wider range of hardware Novel techniques for flood prediction and

analysis Timely distribution of flood warnings

Proactive and passive warnings SMS/Audio-Visual/Web

The GridStix Platform

Consists of a variety of hardware and software: Gumstix hardware platform Lancaster’s GridKit middleware

platform Various networking technologies Flood prediction algorithms

Gumstix (1)

Gumstix (2) Specs

400Mhz Intel XScale processor 64Mb RAM 16Mb Flash Memory

Variety of I/O Mechanism Standard Ethernet port Compact Flash slot

• Storage• 802.11b Networking • GPRS

GPIO Lines for sensor connectivity On-board Bluetooth Radio

Power Consumption

Significantly higher power consumption than devices used in traditional sensor networks Berkley Motes typically use 54mW Gumstix use 1W

Can be powered using a combination of batteries and solar panels One 15cm2 solar panel output of 1.9W 6v 10AH battery Aggressive power management

GridKit (1) Provides key functionality to implement

Grid behaviour Service Binding Resource Discovery Resource Management Security

Based on the OpenCOM component model Rich support Stripped-down deployments

Overlay support Used to implement networking service not

provided by the underlying network type

Overlay (1)

CONTROL

…CREATE

JOIN

LEAVE

DELIVER…

FORWARD

…ROUTE

SEND

RECEIVE…

STATE

IForward

I{Overlay}State

IDeliver IForward IForward

IDeliver

IForward

Overlay (2)

CONTROL

JOIN

LEAVE

FORWARD

ROUTE

SEND

STATE

LEAF SET

NEIGHBOUR SET

ROUTING TABLE

CONTROL

PING

PONG

FORWARD

QUERY

PUSH

STATE

NEIGHBOURS

Gnutella CF

Pastry CF

ISearch

IForward

IForward IForward

IDeliver

IControl

Supported Adaptations

CPU Adaptation Throttle CPU frequency

Overlay Adaptations Swap overlay components to alter

topology Physical Network Adaptations

Switch network types

Adaptation Scenario 1

Changes in Criticality Need to conserve power in normal

operating conditions Operate at lowest CPU frequency Poll sensor infrequently

Potential Flooding Detected GridStix can increase CPU frequency to

execute prediction algorithms quicker Data can also be collected more

frequently to improve the accuracy of the predictions

Adaptation Scenario 2

Adapting to Node Failure Need to increase the robustness of

network when flooding is predicted Do this without changing network

type Switch overlay types

Shortest path trees consume less power

Fewest hop trees are more robust

Adaptation Scenario 2 cont.

Root

Node B Node C

Edg

e x E

dge x

Node D

Edg

e x

Node E

Edge x

Node F

Edg

e x

Root

Node B Node DNode C

Node FNode E

Shortest Path Fewest Hops

Trigger: Flooding predicted by a Gumstix.

• Bluetooth used by default due to lower power requirements.

•Shortest-Path tree overlay used due to its power conservation characteristics.

• Bluetooth continues to be used.

• Fewest Hop tree overlay applied to increase the robustness of the tree.

Adaptation Scenario 3

Node Submersion Likely that nodes will become submerged

during flooding Want nodes to remain connected for as

long as possible Switch network types when

submersion is predicted Bluetooth -> Wifi or Wifi -> GPRS High power consumption and increased

range

Adaptation Scenario 3 cont.

Root

Node B Node DNode C

Node FNode E

Fewest Hops

Trigger: Submersion predicted by a Gumstix.

• Switch from Bluetooth to Wifi

• Same overlay type used.

• However, the different characteristics of Wifi causes a new topology to be created.

Root

Node B Node FNode D Node ENode C

Fewest Hops

Current Status Small test-bed of nodes currently deployed:

Three Gumstix Nodes Depth Sensors Image-based Flow Sensors

Additional nodes are being added, to initial deployment size of 13 nodes.

Performance of network hardware, solar panels and other hardware is being tested in the field.

Future Work Development of a simulator to test

deployment approaches with past and predicted flood events.

Bringing in more highly embedded hardware running the RUNES GridKit implementation.

Integration with Lancaster’s main NW Grid Deployment.

Summary Proposes a more automatic and

sophisticated mechanism for collecting and processing flood data

Convergence of Grid and Wireless Sensor Network functionality

Uses this sophisticated mechanisms for performing adaptations

Can customise the configuration and behaviour of sensors to the current environmental conditions