collaborative_wireless_sensor_network_simulation

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Collaborative Wireless Sensor Network Simulation

KHEMAKHEM Ramzy | BODENAN Julien | SALIM Eliass

ENSEIRB, T3, January

{khemakhe, bodenan, salim}@enseirb.fr

Abstract.Wireless sensor networks (WSNs) are attracting increased interest for a wide range of applications such asenvironmental monitoring and vehicle tracking. Resource constraints on nodes in the network and unreliability ofradio communication mean that applications have to be developed with an eye on maximizing energy efficiency in

order to extend network life [1]. Our goal is to work on an autonomic architecture in order to minimize the energyconsumption of the nodes within the network with an adapted distributed algorithm [2] for a WSN environment thatenables nodes to stop useless communications between each other. Indeed, sensing and communications consume

energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. This

report presents our approach towards this particular problem, the algorithm we worked on and finally itsimplementation with a simplified distributed knowledge sharing model within a Visidia environment [3].

1 IntroductionRecent advances in digital electronics, embedded systems, and wireless communications have motivated a lot of

research in the direction of distributed wireless sensor networks. A wireless sensor network is a wireless network

consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or

environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations

[1]. Originally, wireless sensor network was designed for military applications such as battlefield surveillance.

Though, wireless sensor networks are now used in many civilian applications areas, including environment, traffic

control, and even in health.

Wireless sensor networks differ from conventional network systems in many aspects. They usually involve a large

number of spatially distributed, energy-constrained, and self-configuring nodes. The wireless sensor networks are

bringing a lot of new challenges and design considerations. One of these challenges is the energy consumption.

Indeed, as we have said before, due to limited energy resources, it is vital to maximize the lifetime of sensors in

order to operate over a long period of time (from 6 months to 1 year) within a difficult environment such as military

zones. Because without energy the network cannot do anything, the problem of energy consumption is the most

significant one. Thats why WSNs require careful management of energy resources. Thus, we tried in this project to

find a way that enables to limit the energy consumption in WSNs.

Thus, an autonomic management solution based on distributed algorithms for knowledge sharing was provided

within the LABRI laboratory, so the solution outcome seems interesting to be studied and applied to the problematic

we have chosen that is to say the energy consumption on WSNs. For this purpose, the simulation tool VISIDIA(Visualization and Simulation of Distributed Algorithms) was used to test the algorithm.

2 State of the art2.1 ArchitectureThere are two sorts of sensor network architectures, layered architecture and clustered architecture that are

shown in Figure 1. The first one has a single powerful base station (BS), and the layers of sensor nodes around it

correspond to the nodes that have the same hop-count to the BS. About the clustered architecture, the sensor

network is divided into small areas called clusters.


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Each cluster has a cluster-head (which functions like a base station) which is elected to manage these resources like

the communication between sensors. Within each cluster, ordinary nodecommunicates with the cluster head and the

cluster head form a backbone network to forward the data to the remote BS. Furthermore, with this architecture,

aggregation of data is easier, the management of the network is simplified and it permits to reduce the energyconsummation.

(a) (b)

Figure 1. Layered (a) and clustered (b) architecture

Because its the most used architecture, we have considered in this work a WSN with a clustered architecture.

2.2 Research Challenges in WSNAs we have seen before, wireless sensor network problems are not the same as those we can encounter in classical

networks. Because the nodes that constituted this kind of network have small size, low battery capacity

nonrenewable power supply, small processing power, limited buffer capacity, a low-power radio and lack uniqueidentifiers, WSNs provide new communication and networking paradigms.

In order to realize system that can support extreme situations, several obstacles need to be overcome.

- Coverage problem: ensure that sensors provide sufficient coverage of the sensing field- Limited source energy and therefore limited communications: Because it is impractical to recharge a

depleted battery we need for example to use specific protocols (energy-aware protocols) in order to prolong

the network lifetime. Energy is not only spent for communication, but also when sensors are moving around

the network

- How to decide which sensors need to remain active so that the area is covered as possible and we can saveenergy

- Hot spot problem: It is the long-term connectivity maintenance of networks. Some nodes work more thanothers and so they deplete their energy faster. Its because nodes that are closer to the sink also transmit

others data and not only theirs- Ad hoc deployment, for example when sensors are dropped by a plane above a forest that needs to be staked

out

- Free maintenance operation, which requires an automatic configuration (for example when the topology ofthe network changes)

- Node identification: Because of the high density of nodes, we need a long ID to avoid the ID conflict. Butmost WSNs cant afford the large overhead introduced by a long ID

- The security in sensor networks: Its the same problems as those we can encounter with wireless ad hocnetworks, like availability, confidentiality, integrity, and authentication

- Dynamic adaptation, especially for the arrival or fall of sensors, or the case where there is a stimulimodification

- Location in a repository in relation to other nodes or absolutely: How to efficiently deliver the messagewithout or with less location information


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- Quality of service: the aim is to provide better services than just best effort services

Most of these problems enounced above have not been entirely resolved and it represents a real challenge forresearchers. The bibliographical analysis on the wireless sensor networks we have done in the first phase of this

project has enabled us to become aware of the energetic problem. Thus, because it is impractical to recharge a

depleted battery we need for example to use specific protocols (energy-aware protocols) or specific algorithm in

order to prolong the network lifetime.

We also noticed that other problematic of this field are more or less linked with the energy one. So, if we success to

minimize the energy consumption of sensors it would open many barriers.

2.3 Energy consumption problemThe energy consumption problem in wireless sensor networks is investigated. Because of constrained

battery, the problem is to expend as little energy as possible receiving and transmitting data.

Several works have been done in the last few years in order to find a way to improve the routing performance,especially the lifetime for WSNs. The authors in [4] present a taxonomy about most of the routing protocols for

WSNs and categorize them into three classes which are data-centric [5, 6], hierarchical [7-9], and location based [10,

11] protocols.

To reduce the energy consumption, an important technique is the data aggregation which is a technique adopted by

the data-centric routing protocols [5, 6]. It uses the fact that that many nearby sensor nodes might sense and collect

similar information. This method enables to reduce both the size and the number of transmissions. SPIN (Sensor

Protocols for Information via Negotiation [6]) can be viewed as the first data-centric routing protocol which uses

data negotiation method among sensor nodes to reduce data redundancy and save energy. Direct Diffusion [6] is a

famous and representative data-centric routing protocol for WSNs.

In WSNs with hierarchical structure [7-9], the whole network is further divided into smaller areas called clusters. In

this way, the energy consumption can be greatly reduced since the communication range is largely reduced within

one cluster and the ordinary sensor nodes can be set to sleeping mode according to certain Time Division Multiple

Access (TDMA) schedule, which is sent by cluster head.

Location-based routing protocols [10, 11] require sensor location information which can be gained either through

global positioning system (GPS) devices or through certain estimation algorithms based on received signal strength.

The advantage of this type of routing protocols is that there is no need to make blind broadcasting, so the routing

overhead can be largely reduced, and so it reduces the energy consumption.

3 Our ApproachThe major stake of WSN technology is to deploy this kind of network on a large scale environment, and to make

sensors cooperating while taking into account problematic we can encounter in this environment, but to do so we

need to introduce an autonomic notion. IBM was the first to develop an autonomic architecture [13].

However, in all autonomic architectures which have been developed there was no cognitive infrastructure for

knowledge management, a knowledge plane.

This knowledge plan would provide the ability to the networks elements to be self-organizing and to follow the

global objectives provided by administrators while optimizing network resources like energy. As we said before,

communication between nodes is the main energy consumer, thats why we tried in a clustered architecture to limit

useless communications between nodes.

To do so, we have adapted a distributed algorithm for knowledge sharing in the context of wireless sensor network.


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3.1 Initial AlgorithmHaving a self-organized sensor network implies designing a distributed collaborative environment in which

all the component should be able to share their knowledge and to take decisions according to the information theyreceived from other components. Those components should also be able to analyze shared knowledge and eventually

reject it if it is necessary.

To realize the knowledge sharing, a distributed algorithm that is shown Figure 2 is proposed. The main objective of

this algorithm is to define the situated view of the knowledge that a component wants to share with the others. The

situated view means the domain limits where a piece of knowledge is useful and then should be propagated. The

notion of situated view is similar to the notion of neighborhood.

Figure 2: Knowledge sharing algorithm with situated view

In fact, when receiving knowledge, a component decides either to be the neighbor of the root component by

accepting the knowledge and by propagating it, or not by stopping it propagation. This is the notion of acceptance.

This notion is necessary to avoid useless information exchanges between nodes, and by this way the knowledge plan

is constructed progressively. Then, there is the notion of neighborhood. It is necessary that elements have the ability

to identify their direct neighbors. Several criteria can be used to define the neighborhood. Without going into details

there is the physical neighborhood and the logical one (based on similarities between nodes, e.g. the type of

equipment, the role of the equipment).

3.2 Adapted algorithmIn our work, we have considered that this notion of neighborhood corresponds to the physical neighbor that is

somewhat reducing but sufficient for our simulation. So, when a sensor gets an information that it picks up from its

area coverage or from another node, it decides or not to spread it to its neighborhood. Thus, before the node

retransmits the knowledge (or the information) to all its neighbors, it first has to determine if the knowledge is

coherent with its state and if the knowledge is pertinent or not. The criterion we used to check if the knowledge is

pertinent or not will be seen in the next part.

So, the procedure defined in Figure 2 is executed by all nodes when receiving the knowledge and it is according to

this procedure that the knowledge is propagated in the network.


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4 Implementation4.1 The chosen scenario

The performance of the algorithm is not yet approved. Thus, to come over this, a concrete scenario of a sensor

network use should be fixed in order to measure its auto-organization performance.

The chosen scenario deals with a fire scenario in forest. The architecture envisaged for this scenario consists of a

network composed by several temperatures sensors and one based station.

In our simulation, at a given instant one temperature sensor detects an abnormal high temperature and decides to

transmit this information. As we have said before, we want here to reduce the energy consumption by limiting

communications. Thus, we have to compare the propagation of the information in the wireless sensor network with

and without the algorithm. To do so we have used a model of fire propagation. That is to say, that according to this

model we assigned a given temperature to each node in the network as a function of its position from the node where

the fire has been detected. So, we know how far the information needs to be spread from the fire source, and weknow which nodes are not concerned by this event.

The model of fire propagation we used is shown in Figure 3.

Figure 3: Model of fire propagation

In our simulation, we have considered that the knowledge was the information of a detected fire by a given sensor (or

node). So what we propagate in the network is that the sensor number X (each sensor is defined by an Id number) has

detected an abnormal temperature. Then, each node that receives this information checks if this information is

pertinent, and if it needs to be propagated, the criterion we have chosen is to compare the temperature that the node

receives with the temperature it gets from its area coverage. Thus, if the difference between temperatures is to

important, the information is considered as not pertinent, and so it is not spread to the neighborhood.

To test the algorithm and to set up our scenario, we had to use a network simulator that has specific criteria.

4.2 The chosen simulatorWe needed a tool that can visualize distributed algorithms not necessarily a Network Simulator like NS-2. Thus, we

just need a middleware vision which means that we need to simulate the logical network.

To do so, there are lots of tools that can fit, but we chose Visidia mostly because it has many properties that arent

found in other applications, and for its good performance with distributed algorithmic.

VISIDIA is a software tool for the visualization and the simulation of distributed algorithms based on a solid

theoretical base. It is kitted up by a graphical user interface which will be used to draw the sensor network

architecture of our scenario.


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After we had learnt how the VISIDIA graphical interface works and how we can create a graph, we had to learn how

we can execute the algorithm on it. To do so, we needed first to implement in java the algorithm on our favorite

editor (we used eclipse), and then we needed to generate the .class file. And this is that .class file that is used on

VISIDIA to launch the algorithm on each node. But as we have said before, we first have had to adapt andimplement the algorithm to our scenario. This part of our work is explained in the next section.

4.3 The implementation of the algorithmVISIDIA is implemented in Java, so we worked on the VISIDIA package, and we used the VISIDIA API.

We encountered some difficulties to establish relationships between classes, in order to use the methods that we

needed to implement the algorithm. We defined a class EnergyAlgo that inherit from the class Algorithm,the main

function of our class is the method init()that is executed by a thread launched by the class simulator which takes

the algorithm as a parameter.

As we have said before, we used the model of fire propagation. To do so, we defined four ellipses in order to get thelevel lines we needed for the model. These ellipses are defined according to the hearth, that is to say the first sensor

that detects the fire. When the sensor detects the fire it sends a message to its neighborhood. Then, its neighbors

decide if the information is pertinent or not, and if so they send it to their neighbors (except the node they received

the information from). To check if the information is pertinent or note we compare the temperature that the local

node detects to the temperature of the sensor that detects the fire. If the difference between the two temperatures is

inferior to 20 degrees, the information is considered as pertinent and is transmitted.

Each ellipsis defines a space that has a specific temperature, in other word, to attribute a temperature to a node, we

have to check if it is inside an ellipsis, and if so we need to find to which one it belongs.

Thus, the four ellipses define 5 temperatures areas, four are inside an ellipsis and a last one is outside and represents

the rest of the network that is not concerned by the fire.

With the method isInEllipse()defined below, it is easy to check if the node is inside an ellipsis or not. But some

nodes can be inside two or more ellipses, that is why we defined for each ellipsis a specific table that contains the

IDs of the nodes that are inside of it.

By this way we can attributes a temperature for all the nodes according to its position in the network and according

to the model fire propagation.

Hashtable zone3 = new Hashtable();for(int i = 0;i < netSize; i++){

if( i != this.getId() &&

isInEllipse(vueGraphe.rechercherSommet(Integer.toString(i)).centreX(),

vueGraphe.rechercherSommet(Integer.toString(i)).centreY(), 600, 600)){

if(!zone1.containsKey(i) && !zone2.containsKey(i))

zone3.put(i, i);

}

}

//check if the point is inside an ellipse

public boolean isInEllipse(int x,int y,int a, int b){

System.out.println((Math.pow((float)((float)(Math.PI/4)*(x-y))/a,2)+

Math.pow((float)(float)(Math.PI/4)*(x+y)/b, 2)));

return ((Math.pow((float)((float)(Math.PI/4)*(x-y))/a, 2)+

Math.pow((float)(float)(Math.PI/4)*(x+y)/b, 2)) < 1);

}


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5 Experimentations and Results

(a) (b)

Figure 4. Knowledge Sharing Algorithm (a) and Spread all knowledge Algorithm (b) simulation

The figures above show the simulation of the knowledge sharing and the spread all algorithms. The second algorithm

does not attribute spread rule to nodes. Thus, the knowledge shared by the starter node has been transmitted to all

nodes on the graph.

The main difference between those two algorithms is that in the first one, 117 messages have been exchanged

whereas in the second one, we count 184 messages. Therefore, as the energy consumption is proportionate to the

number of message send, we can conclude that the knowledge Sharing Algorithm is more efficient toward the

problem energy dissipation.

In fact, especially in large sensor networks, this algorithm limits the area in which a specific knowledge should be

shared by establishing criteria of neighborhood. Those neighbors are the only node that can take profits from the

content of the knowledge. If we refer to figure4, the neighbors of the starter node are distributed in for zone havingrespectively red, yellow, orange and black nodes color. The blue nodes represent the neighborhood area bounds.

However, in the figure5, the neighborhood area of the starter node is not bounded due to the fact that all nodes are

neighbors.


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6 Conclusion

The implementation of a simple adaptation of the knowledge share algorithm was sufficient to show the efficacy ofthis algorithm toward the problem of energy consumption in a WSN. In fact, by introducing the neighborhood

concept, this algorithm decrease the number of message exchanged to spread a knowledge and preserve, as a result,

the nodes energy in an auto-management process.

The implementation of such algorithm was not much easy. Indeed, we have faced some difficulties to take profits

from the code of VISIDIA in order to implement our algorithm notably, to establish the relationship between the

simulation software classes and the algorithm class.

Although, it was a very interesting project that necessitated an important personal devotion to come over all the new

notions and specificity of WSN and it constitutes a real value-added.

References

[1] Mbaye Maissa, Krief Francine : Distributed Knowledge Sharing Algorithm for Autonomic Networking.

[2] The ViSiDiA Project: Visualization and Simulation of Distributed Algorithms. Please see

http://www.labri.fr/projet/visidia/

[3] K. Akkaya and M. Younis, "A Survey of Routing Protocols in Wireless Sensor Networks, in the Elsevier Ad Hoc

Network, Vol. 3(3), 2005, pp. 325-349.

[4] J. Kulik, W. R. Heinzelman, H. Balakrishnan, "Negotiation-based protocols for disseminat-ing information in

wireless sensor networks",

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[10] IBM Corporation, An architectural blueprint for autonomic computing, White Paper 2003.[11] Clark, D. D., Partridge, C., Ramming, J. C., and Wroclawski, J. T. 2003. A knowledge plane for the internet. In

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