The National Weather Sensor Grid

∗

Hock Beng Lim, Keck Voon Ling, Wenqiang Wang, Yuxia Yao, Mudasser Iqbal,

Boyang Li, Xiaonan Yin, Tarun Sharma

Intelligent Systems Centre

Nanyang Technological University

limhb@ntu.edu.sg

http://nwsp.ntu.edu.sg/nwsp

Categories and Subject Descriptors

C.2.4 [Distributed Systems]: Distributed applications

General Terms

Design, Experimentation, Measurement

Keywords

Sensor Networks, Grid Computing, Sensor Grid, Sensor

Data Management, Data Visualization, Web Integration

1 Introduction

With the rapid advances in technologies such as MEMS

sensors, low-power embedded processing and wireless net-

working, sensor networks are becoming more powerful in

terms of data acquisition and processing capabilities. Sensor

networks can now be deployed in the physical world for var-

ious important applications such as environmental monitor-

ing, weather monitoring and modeling, military surveillance,

healthcare monitoring, tracking of goods and manufacturing

processes, smart homes and ofﬁces, etc.

The ﬁeld of sensor networks has grown dramatically in

recent years. However, it remains a daunting challenge to

deploy large-scale sensor networks. It is also difﬁcult to in-

tegrate sensor networks with existing IT infrastructures such

as the Internet. Thus, sensor networks often operate as sepa-

rate information silos, and the sensor resources and data can-

not be easily shared. In fact, with a large number of sensor

devices potentially deployed over a wide area, sensor net-

works are important distributed computing resources that can

be shared by different users and applications.

Grid computing is an established standards-based ap-

proach to solve large-scale problems through coordinated

sharing of distributed and heterogeneous resources in dy-

namic virtual organizations. Most existing developments in

grid computing focus on compute grids, which provide dis-

tributed computational resources for compute-intensive ap-

plications, and data grids, which provide seamless access to

large amounts of distributed data and storage resources.

∗

This work is supported in part by Microsoft Research under

the SensorMap Request for Proposals (RFP) 2007.

SenSys’07,

November 6–9, 2007, Sydney, Australia.

ACM 1-59593-763-6/07/0011

Most recently, the concept of sensor grids [1] has received

increasing attention from the research community. Sensor

grids extend the grid computing paradigm to the sharing of

sensor resources in sensor networks. A sensor grid inte-

grates sensor networks with the computational and storage

resources in the conventional grid fabric. The vast amount

of data collected by the sensors can be stored, processed and

analyzed by the computational and data storage resources of

the grid. Sensor resources can be efﬁciently shared by differ-

ent users and applications through the resource sharing and

coordination capabilities of the grid.

The National Weather Study Project (NWSP) is a large-

scale community-based environmental initiative in Singa-

pore that aims to promote the awareness about weather pat-

terns, climate change, global warming and the environment.

In this project, hundreds of mini weather stations are de-

ployed in schools throughout Singapore. Each weather sta-

tion contains several sensors for measuring weather param-

eters such as temperature, rainfall, humidity, wind speed

and direction, etc. Since the geographical locations of these

school weather stations cover most parts of Singapore, the

microclimate weather data from these stations provide a

good proﬁle of the weather patterns within Singapore.

We are designing and building the National Weather Sen-

sor Grid (NWSG) to support and to realize the full poten-

tial of the NWSP. The NWSG has several important fea-

tures. First, it connects the weather stations via the Inter-

net to automatically collect and aggregate weather data in

real-time. Second, the weather data are logically stored in a

Central Data Depository (CDD) which can be implemented

using distributed data storage resources. Third, the NWSG

integrates computational resources for the compute-intensive

processing of weather data. Fourth, the weather data can be

easily accessible and shared via the web through mash-ups,

blogs, and other user applications. We are developing tech-

niques and tools to efﬁciently publish, query, process, visu-

alize, archive and search the vast amount of weather data.

Finally, the NWSG should be scalable to handle hundreds

of weather stations, and also extensible to handle different

types of sensors besides weather stations.

At present, we have developed a prototype of the NWSG

with several connected weather stations. This prototype en-

ables us to improve the design of the sensor grid architecture.

It also provides several useful services for the users to access

and visualize the weather data.

369

Figure 1. Sensor Grid Architecture

2 System Architecture

Figure 1 shows the system architecture of the National

Weather Sensor Grid (NWSG). It consists of several major

components.

Sensor Resource. In the NWSG, the sensor resources are

different types of weather stations. There are two types of

weather stations deployed in this project; namely, the Davis

Vantage Pro II and the WeatherHawk. Each weather sta-

tion integrates several sensors that directly measure weather

parameters like temperature, rainfall, humidity, wind speed

and direction, solar radiation, barometric pressure, etc. A

weather station or a group of weather stations are connected

to the sensor grid through a proxy system, which is currently

implemented as a desktop or laptop PC running a set of mid-

dleware services. The proxy may be wired or wireless.

The proxy will automatically retrieve weather data from

the weather stations and send the data to the sensor grid via

the Internet. The proxy performs some data processing tasks.

For example, it computes several indirect weather parame-

ters such as heat index and dew point using the direct mea-

surable weather parameters. It is also possible for the proxy

to control the operation of the weather station.

Computational/Data Storage Resource. A computa-

tional/data storage resource refers to any resource that per-

forms computational and/or data management tasks. These

resources are capable of efﬁciently handling large-scale

computational or data management jobs. They are man-

aged by middleware services that we are developing based

on standard grid protocols such as Globus.

In our current prototype, these resources consist of a sin-

gle database and several grid resource nodes. Although the

single database can handle the weather data for now, we plan

to extend it to a distributed database for better scalability as

more weather stations are connected to our system. With

the microclimate weather data collected throughout Singa-

pore, we can develop weather models and perform sophisti-

cated computations. The computational resources are there-

fore essential to perform such computational-intensive jobs

with relatively low cost.

Service Provider. The term service is used in the grid com-

puting context. Apart from conventional computational and

data processing jobs, the sensor grid can provide some useful

services to the users. The service provider makes use of the

weather stations, computational resources, and data storage

resources to provide the services. The users can subscribe to

these services through standard grid computing mechanisms.

Currently, we provide rain and temperature alert services

to users. Once a user has subscribed to these services, the

user will be notiﬁed if it is raining or if the temperature is

too high in certain locations. We also provide a weather dis-

play service which displays the weather conditions at user

speciﬁed locations in a ﬂexible manner on web sites, blogs,

or other user applications.

3 Demonstration Highlights

We will demonstrate several features of our National

Weather Sensor Grid (NWSG) prototype. First, we will show

the distribution of the connected weather stations in Singa-

pore. We currently use two geo-centric web interfaces to

display and visualize the weather station information.

The ﬁrst interface is Microsoft SensorMap [2], which is

specially designed for sensor information publishing. We

publish the weather station information using SensorMap’s

supported sensor types, and graphically display the weather

data using dynamically generated HTML pages. The sec-

ond interface is Google Earth. We generate kml script which

overlays the weather stations on the satellite map of Sin-

gapore, together with dynamically updated snapshots of the

weather data last captured from the weather stations.

We will demonstrate how a user submits a request for a

particular type of computational or data processing job, and

how the sensor grid completes the job and returns the results

to the user. Finally, we will show the weather alert services

and how users can subscribe to these services on the web.

4 References

[1] H. B. Lim, Y. M. Teo, P. Mukherjee, V. T. Lam, W. F.

Wong, and S. See, “Sensor grid: Integration of wire-

less sensor networks and the grid,” Proc. of the 30th

IEEE Conference on Local Computer Networks (LCN

2005), pp. 91-98, Nov 2005.

[2] S. Nath, J. Liu, F. Zhao, “SensorMap for Wide-Area

Sensor Webs,” IEEE Computer, Vol. 40, No. 7, pp.

90-93, Jul 2007.

370