A MODEL FOR WIRELESS SENSOR NETWORKS SUPPORTED

COOPERATIVE WORK

Lina M. Pestana Leão de Brito

and Laura M. Rodríguez Peralta

1, 2

Mathematics and Engineering Department, University of Madeira (UMa)

Campus da Penteada, 9000-390 Funchal, Madeira, Portugal

Centre for Informatics and Systems of the University of Coimbra (CISUC), Pólo II, 3030-290 Coimbra, Portugal

Keywords: Cooperative Work, Collaboration, Graph-based Model, Sensor Nodes, Wireless Sensor Networks.

Abstract: Collaboration is essential to Wireless Sensor Networks due to the typical resource limitations of the sensor

nodes. In fact, the main functions of the network cannot be accomplished without collaboration among

sensor nodes. Most of the work found in literature only focuses a specific type of collaboration, associated

with the accomplishment of a certain task, such as signal processing, computing, routing, etc. In this paper,

we present a graph-based model of cooperative work for WSNS. This model is called Wireless Sensor

Networks Supported Cooperative Work (WSNSCW) and considers the specific requirements of the WSNS.

1 INTRODUCTION

Combining the advantages of wireless

communications with some computational

capability, Wireless Sensor Networks (WSNS) allow

for a wide range of applications: environmental

monitoring, health, surveillance, traffic monitoring,

security, military, industry, agriculture, etc.

Nowadays, wireless sensor nodes are intended to

be small and cheap. Consequently, these nodes are

typically resource limited (Akyildiz et al., 2002)

They have reduced memory and limited processing

capacity. Battery is, also, restricted. Moreover, due

to short transmission range (caused by restrained

transmission power), nodes can only communicate

locally, with a certain number of local neighbours.

For these reasons, it is perfectly natural that nodes

need to collaborate among themselves in order to

accomplish their tasks. Collaboration enhances the

scalability of the network and facilitates mission

completion (Gracanin et al., 2006).

It is also worth mentioning that, according to

(Ramanathan et al., 2002), WSNS originated a new

collaboration concept. In traditional networks,

collaboration exists within the same group of nodes,

even though they move (node-centric collaboration).

In WSNs, collaboration occurs among nodes located

in a certain region, which means that the group of

nodes may not be the same (location-centric

collaboration). For instance, if a node leaves a

predefined region, it stops collaborating with other

nodes. However, besides localization-based

collaboration, it is possible to identify other ways to

collaborate, based whether in monitoring a certain

phenomenon or in the hardware characteristics of the

nodes themselves (Ranjan et al., 2005),(Medidi et

al., 2006), (Hussain et al., 2004), (Zhou et al., 2006).

At the moment, there are several works

concerning collaboration in WSNS. However, most

of them only focus a specific type of collaboration,

associated with the accomplishment of a certain task

(signal processing, routing, task scheduling, etc.).

Usually, these collaborations simply intend to

improve some parameters of the network (energy

costs, coverage, transmission costs, processing costs,

delay, etc.).

Until now, the only work that presents a model

for cooperative work in sensor networks has been

proposed by Liu et al. (Liu et al., 2006). This model

was created for sensor networks; however, it does

not consider the specific requirements of the WSNS.

In this paper, we present a graph-based model of

cooperative work for the specific case of the WSNS,

named Wireless Sensor Networks Supported

Cooperative Work (WSNSCW), which considers the

specific requirements of the WSNS. Our model

allows for not only the modelling of the cooperative

work, but also for the modelling of the entire WSN

and all its components.

505

M. Pestana Leão de Brito L. and M. Rodríguez Peralta L. (2008).

A MODEL FOR WIRELESS SENSOR NETWORKS SUPPORTED COOPERATIVE WORK.

In Proceedings of the Third International Conference on Computer Graphics Theory and Applications, pages 505-511

DOI: 10.5220/0001098705050511

 SciTePress

This paper is organized as follows. In section 2,

we briefly describe the related work. In section 3,

the model for WSNSCW is presented and

exemplified. Section 4 provides some conclusions

and perspectives of future work.

2 RELATED WORK

Most of the works concerning collaboration in

WSNS only focus a specific type of collaboration,

which is associated with the accomplishment of a

certain task, such as: sensing (Gracanin et al., 2006),

(Krohn et al., 2005), (Wang and Ramanathan, 2005),

(Reich, 2002), signal processing (Sheng and Yu,

2003), (Ramanathan et al., 2002), (Bergamo et al.,

2004), (Li and Yu, 2003), (Broxton et al., 2005),

(Asis and Kai, 2006), (D’Costa and Saveed, 2003),

(Wang and Wang, 2007), computing (Al-Omari and

Weisong, 2006), (Iftode et al., 2004), (Singh and

Prasanna, 2003), transmission (Yang et al., 2006),

(Krohn et al., 2006), (Krohn et al., 2006), (Yang and

Tong, 2005), (Yang and Tong, 2005), routing (Chen

et al., 2006), (Yang and Tong, 2005),(Fang et al.,

2004) localization (Sheng and Hu, 2003), (Reghelin

and Fröhlich, 2006), (Bergamo et al., 2004),

(Dardari and Conti, 2004), (Li and Hu, 2003),

(Broxton et al., 2005), security (Chadha et al., 2005),

task scheduling (Sanli et al., 2005), calibration

(Reghelin and Fröhlich, 2006), (Bychkovskiy et al.,

2003), heuristics (Reghelin and Fröhlich, 2006),

target tracking (Onel et al., 2006), (Wang and Wang,

2007), resource allocation (Giannecchini et al.,

2004), time synchronization (Hu and Servetto,

2005), knowledge building (for smart sensors) (Bove

and Mallet, 2004), etc. There are also works

concerning the collaboration between wireless

sensor nodes and other kind of devices

(heterogeneous groupware collaboration) to support

some specific applications (eg. collaboration

between sensor nodes and PDAs, in a fire fighting

scenario) (Cheng et al., 2004), (Chassot et al., 2006),

(Chaczko et al., 2005).

Until now, the only work that presents a model for

cooperative work in sensor networks has been

proposed by Liu et al. (Liu et al., 2006). It is the

SNSCW (Sensor Networks Supported Cooperative

Work) model.

The hierarchical SNSCW model divides the

cooperation in a sensor networks in two layers. The

first one relates to the cooperation between humans

and sensor nodes (user-executor relationship, being

initiated either by the user or by the sensor node),

and the other layer relates to the cooperation

between the sensor nodes (represented by an

activity-task-cooperation layered abstract model;

considering two main subtypes of cooperation: peer-

to-peer and master-to-slave).

This model was designed for sensor networks.

However, it does not consider the specific

requirements of the WSNs, for instance, its scale, its

auto-configuration requirements, the resource

limitations of wireless sensor nodes, etc (Akyildiz

and Su, 2002). Also, it only allows for modelling of

collaboration itself.

3 WSNSCW MODEL

In this paper, we present a model of cooperative

work for the specific case of WSNS, named

Wireless Sensor Networks Supported Cooperative

Work (WSNSCW). As WSNSCW is a model of

cooperative work created specifically to WSNS, it

considers the particular requirements of WSNS. It is

a graph-based model; nevertheless, it includes other

objects in order to make possible the modelling of

all the components of a WSN.

The SNSCW model, proposed in (Liu et al.,

2006), only focuses cooperation in WSNs, more

precisely, the different types of cooperation that can

occur in a WSN. Our model not only allows for the

modelling of cooperation within the network, but

also for the modelling of the entire WSN and all its

components (different types of nodes, clusters, base

stations, etc.). In what concerns to collaboration,

both the model and the example presented in this

paper only include the concepts of session and

relationships among nodes. However, we intend to

improve our model, including other concepts related

to CSCW (Computer Supported Cooperative Work)

(Vin and Rangan, 1992), (Mills, 2003).

WSNSCW is also a heterogeneous model, in the

sense that it can be applied to any type of wireless

sensor regardless its size, its hardware

characteristics, types of signals it can measure, etc. It

can also be applied to any WSN despite of the

specific application. However, in this paper we are

going to illustrate the use of this model, applying it

to the case of an environmental monitoring

application.

3.1 Definitions

In this section, the entities of our model are defined.

The entities are all the components than might exist

in a WSN. Table 1 shows the symbol, the concept

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and the description of all the entities included in the

model.

A WSN can be composed by different types of

nodes: ordinary wireless sensor nodes (which can be

in one of two possible states, in order to save energy:

active or inactive – sleep mode), anchor nodes

(which support the other sensor nodes in the

localization process), one or more sink nodes (also

known by base stations, which are responsible for

sending data to the gateway) and a gateway

(responsible to send data to the user, through the

Internet). If nodes are grouped in clusters, one of the

cluster members become the cluster head; and all the

wireless sensor nodes have to send data to the cluster

head (usually, the more powerful node of the cluster,

responsible for sensing data to the sink node).

There is a relationship between nodes that

collaborate with each other. A relationship can be

established based on: localization of the nodes,

existence of clusters, phenomenon to monitor,

hardware characteristics of the sensor node, etc.

Associated with this relationship there is always an

exchange of data (data flow entity), which may be of

several different types (light, sound, temperature,

image, acceleration, etc.). It is also relevant to

identify the type of signal that was used by the

sensor node to collect this data (radio frequency,

ultrasound, acoustical or light).

There can be established several collaborative

sessions when monitoring a WSN, and they can exist

simultaneously or not. A session may be established

based on the objective of the WSN (type of

phenomenon to monitor, geographical area to

monitor, on monitoring time, etc.)

Table 1: Definition of the entities that can constitute a Wireless Sensor Network.

Symbol Concept Description

Sensor node

Wireless sensor nodes, typically with limited resources. They can be stationary or

mobile.

Sink node/

Base Station

Node to which data collected by ordinary nodes is sent; being responsible to send

data to the gateway.

Gateway

Node responsible to send the data to the user, through the Internet.

Anchor node

Node with known localization.

Active sensor

node

Node which is in the active state.

Inactive sensor

node

Node which is in the sleep mode, in order to save energy.

Cluster

Group of nodes, created according to: geographical area, type of sensor, type of

phenomenon, task, etc.

Cluster Head

Sensor node to whom all sensor nodes in the cluster send the collected data; it is

responsible for sending the received data to the Sink node.

Data flow

This label identifies both the type of signal being used (radio frequency,

ultrasound, acoustical or light) and the type of data being transmitted between

nodes (temperature, humidity, light, sound, video, internal voltage, etc.).

Relationship

The arrow represents a relationship between nodes A and B. Node A transmits

data to node B; so, node B consumes information from node A.

A relationship can be established based on: localization, phenomenon, type of

sensor node, etc.

Session

In a certain moment, there may be several collaborative sessions in a WSN. A

session can be established based on the objective (type of phenomenon to

monitor, geographical area to monitor) of the WSN.

Battery

It represents the remaining battery of the sensor node.

User

Person that interacts with the WSN, querying the network, visualizing data, etc.

The user customizes the work of the sensor nodes; the data collected by sensor

nodes is used by the users’ application.

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507

As the battery is the most critical resource of a

sensor node, it is really important that the user

knows the state of the battery of each sensor. That is

why the battery is also an entity of our model.

Finally, the user is the entity who interacts with the

WSN, defining the application, querying the

network, visualizing data, customizing the work of

the sensor nodes, etc.

3.2 Example Scenario

In this section, we exemplify the use of our model,

by applying it to the specific case of an

environmental monitoring application.

Let’s consider a forest monitoring WSN with a

total of 45 nodes. Among these nodes there are 3

sink nodes, 5 anchor nodes and 37 wireless sensor

nodes. The nodes were deployed in an ad hoc

manner, in two different geographical areas of a

forest.

The user works in a place, which is far away

from the forest being monitored. So, he monitors the

WSN through the Internet. As shown in Fig. 1, in

this example there are three simultaneous

collaborative sessions. These sessions where

initiated by the user, after he defined three different

objectives: to monitor the temperature of area A1

(session CS1), to monitor the humidity of area A2

(session CS2), and to monitor the light of the same

area (session CS3).

Within each area, clusters have been created

according to the geographical localization of sensor

nodes, being based in the proximity between nodes.

The cluster head was chosen, among nodes in the

cluster, as the node with more battery. In this case, 5

clusters have been created and, hence, there are 5

cluster heads. The nodes in the cluster automatically

start to collaborate to collect data and send it to the

cluster head. Also, the cluster head starts sending

data to the sink node, which, in turn, send it to the

user, through the gateway. Only the nodes of the

cluster need to be in the active state, as they need to

monitor the phenomenon and also need to send the

data to the cluster head. The remaining nodes are in

the sleep mode, in order to save energy. Nodes can

also become inactive if their batteries end.

As this scenario relates to an environmental

monitoring application, it is very important to be

able to correlate collected data in time. So, anchor

nodes had to be deployed in the WSN. As the

localization of anchor nodes is known, they can help

the cluster heads (as well as the remaining nodes of

the cluster, if needed) in determining their own

position.

As the battery is the most critical resource of a

sensor node, it is really important that the user

knows the state of the battery of each node. This

way, the user gets to know when he has to go to the

field in order to change the batteries of the sensor

nodes.

Any changes that might occur on this scenario

(new collaborative sessions, new clusters, nodes

changing from sleep mode to the active state or vice

versa, etc.) can be represented by a succession of

figures analogous to Fig. 1.

Figure 1: Applying the WSNSCW model to a WSN,

considering the specific case of forest environmental

monitoring.

Figure 2: Screenshots of the implementation of the web-

based 2D visualization tool.

Fig. 2 shows a prototype of a web-based

visualization tool. This tool was created to visualize

some of the components of a forest environmental

monitoring WSN. At the moment, it only shows one

type of nodes and one of the parameters they can

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measure (temperature, noise, acceleration,

magnetism, light and the battery level), at a time

(according to the user selection). However, this tool

should be based in the WSNSCW model.

Consequently, it should show all the entities defined

in the model: different types of nodes, relationships

between them, different clusters, data flows, etc.

In this initial phase, it is still a 2D visualization

tool. However, we intend to develop it into a 3D

visualization tool, since it is more appropriated for

representing a WSN deployed in different types of

terrains. In the specific a forest environment, for

instance, we can have different types of terrains

(flat, mountainous, etc.), which might interfere with

the collaboration established between nodes.

4 CONCLUSIONS

AND FUTURE WORK

WSNSCW is a model of cooperative work

specifically designed to WSNs. It is a graph-based

model that can be applied to a heterogeneous

network, in the sense that it can be applied to any

type of sensors and any type of application. The

great advantage of this model lies in the fact that,

besides modelling the cooperation of the network, it

also allows for modelling the whole network and all

its components. In this paper, we applied this model

to the specific case of a forest environmental

monitoring application.

In the near future, we intend to include more

concepts of CSCW in the WSNSCW model. We

also intend to formalize this model using graph

theory as well as to use the model to create a web-

based visualization tool for WSNs in a 3D

environment. As in a forest environment, we can

have different types of terrains (flat, mountainous,

etc.), it is important that the visualization tool allows

for the 3D visualisation of the whole network,

including all its components, as they are the

deployed in the forest terrain. For instance, it will be

possible to visualize all the nodes in their real

position, and also where they are deployed

considering all the terrain irregularities and obstacles

that might exist. This will lead to a more realistic

view of the network.

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