Int. J. Engg. Res. & Sci. & Tech. 2014 Pradeep Kumar K G and ... - … · Int. J. Engg. Res.& Sci.&...

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Int. J. Engg. Res. & Sci. & Tech. 2014 Pradeep Kumar K G and Neelima B, 2014

ISSN 2319-5991 www.ijerst.com

Vol. 3, No. 3, August, 2014

© 2014 IJERST. All Rights Reserved

Research Paper

SELF ORGANIZATION OF NODES IN BOUNDARYLESSMOBILE ADHOC NETWORKS

Pradeep Kumar K G1* and Neelima B2

*Corresponding Author: Pradeep Kumar K G, � [email protected]

Mobile ad-hoc network is a network where the nodes are capable of moving anywhere in adefined environment. In a real time scenario such as wildlife movement surveys, militaryoperations, emergency medical situations etc where nodes tend to move out off the definedboundary, is not well handled in the current technologies. Therefore in this paper, a novel methodis proposed to organize the movement of ad-hoc nodes. Here, a network with no-boundary isconsidered for the movement of nodes. Self-organization of the nodes is achieved throughAttraction and Repulsion movements. The proposed technique is implemented using NetworkSimulator NS2. From the simulation it is observed that the proposed technique increasesconnectivity and hence throughput increases.

Keywords: MANET, Attraction, Repulsion, Self organization, Throughput

1 Department of Computer Science and Engineering, SDM Institute of Technology, Ujire, Karnataka, India.2 Department of Computer Science and Engineering, N.M.A.M. Institute of Technology, Nitte, Karnataka, India.

INTRODUCTION

A Mobile Ad Hoc Network is also called MANET.

It is a set of mobile devices as shown in Figure 1.

The mobile devices are connected by a wireless

network and they are dynamic in nature. “Ad Hoc”

is derived from the Latin word which means “for

this purpose”. The mobile devices are capable

of configuring themselves and are called “self-

configuring” devices. They form a random

topology without making use of an existing

infrastructure (Nevadita Chatterjee et al., 2006).

The mobile devices cooperate with each other in

order to achieve the certain set of task. This

allows the users to maintain connectivity to thefixed network or to exchange information with the

base station or the access point that is available.

Figure 1: Mobile Ad Hoc Network


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The multi-hop communication allows achieving

this connectivity. The intermediate nodes are

considered as relays and the nodes use these

relays to reach distant destinations. The dynamic

nature and the ability to configure themselves

allows the nodes to change location and still

maintain the connectivity. Each node acts as

router for another node. There exist many

algorithms that determine the mobility pattern, i.e.,

the node movements.

Mobility of the mobile nodes has a significant

importance as it greatly determines the

performance of the MANET (Ivan Stojmenovic

and John Wiley & Sons, 2002); (C F Garca-

Hernndez et al., 2007).

The mobility models are broadly classified as

below (Savyasachi Samal, 2003).

• Deterministic model - urban traffic model

• Semi-deterministic model - column model

• Random - Brownian motion

Figure 2 shows the deterministic mobility

model. This model is the most simplest type of

model. The type of motion in this model is

predictable and the devices follow the predefined

path. For example, if the mobile nodes are moving

in a straight-line (i.e., following a deterministic

model), then the deviation of the direction vectors

associated with any two positions would be zero,

as the mobile nodes continue to move in the same

direction. Sample scenario is cars moving in

urban traffic area.

Semi-deterministic model does not follow a

strict deterministic pattern. They do follow a

pattern and the path varies to some extent; a

general pattern still does exist. Example, a

battalion on battle tanks marching ahead. This

model is as shown in Figure 3.

Random mobility pattern has a total stateless

motion pattern. The future movement of the node

is completely independent of the past movement

and hence no bounds are imposed on the next

movement to be chosen. This randomness in

choosing the next direction vector renders this

type of motion completely unpredictable. The

Figure 4 shows this model.

The nodes are free to move randomly. Thus

the network’s wireless topology may be

unpredictable and change rapidly. They require

minimum configuration and the deployment is

quick and the central governing system does not

exist and all the above makes the ad hoc

Figure 3: Semi-deterministic Model

Figure 2: Deterministic Model


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networks more suitable for emergency situations

like natural disasters, military conflicts,

emergency medical situations, to track wildlife

animal movements etc (Sahin CS et al., 2009).

An ad-hoc network is a dynamic multi hop

wireless network that is established by a set of

mobile nodes. Such networks are, therefore,

suitable for the environments where it is a difficult

to create a fixed infrastructure. In this network,

mobile nodes randomly move and communicate

over radio channels. If two mobile nodes are in a

radio transmission range, they can communicate

with each other directly, otherwise, the source

node sends/receives the packets via some

intermediate nodes (Naouel Ben Salem and

Jean-Pierre Hubaux, 2006).

Self-organizing nodes are the major

contribution of the MANETS (Sahin CS et al.,

2008). The nodes in MANET when compared to

traditional ad hoc networks are expected to be

dynamic. The following Figure 5 depicts self

organized nodes. By dynamic, it means that the

nodes are allowed to move in any direction within

a certain geographical area. The nodes move

dynamically and there is continuous establish-

ment and breaking of the links. When a node

comes closer to another node, it breaks its link

with the previous node and establishes the link

with the new node.

In an unlimited geographical area, the

boundary is removed and random movements

of nodes in MANETs are generated (Eli De

Poorter et al. 2011); (ITU Reports, 2005); (Sahin

CS et al., 2009). The node density is maintained

during the random movement of nodes. The

nodes should keep moving and should stay in

the same area and not run away. The Attraction

& Repulsion algorithm are used to maintain the

connectivity between the nodes.

The connectivity among the nodes may differ

with time due to nodes that are randomly moving

and may join or leave the network at any time.

Hence, an efficient routing protocol is needed to

allow communication over dynamic MANET

topology. Several ad hoc routing protocols have

been proposed and implemented to determine

the most efficient path to route information

through dynamic nodes in MANET. Meanwhile,

since most of the mobile nodes have different

Quality of Service (QoS) requirements, recent

research work of MANET routing has focused on

developing protocol that is able to support various

QoS requirements. In this study, a new routing

protocol namely EMNet has been proposed to

cater for different QoS requirements in MANET.

It was designed based on an artificial intelligent

technique namely Electromagnetic-like

Figure 5: Self Organized NodesFigure 4: Random Model


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Mechanism (EM), which simulating the attraction-repulsion mechanism theory of electro-magnetism. The EM generated solutions for newpath establishment and broken links restorationby regenerating new individuals with qualityimprovement through its attraction-repulsionprocess. The new EMNet protocol has beensimulated in various MANET scenarios and itsperformance in routing was compared withconventional ad-hoc On-demand Distance Vector(AODV) routing protocol. The simulation resultsshow that EMNet is able to achieve better resultsin terms of packet delivery, normalized routingload, end-to-end delay, overhead, and throughputin comparison with AODV in the simulationscenarios (Johnson R and Jasik H P, 1984).

The stochastic model assumed to govern themobility of nodes in a mobile ad hoc network hasbeen shown to significantly affect the network’scoverage, maximum throughput, and achievablethroughput-delay. Random walk model are usefulfor assessing the achievable event detectionrates in surveillance applications where wireless-sensor-equipped vehicles are used to detectevents of interest.

Mobile ad hoc networks (MANETs) enableusers to maintain connectivity to the fixed networkor exchange information when no infrastructure,such as a base station or an access point, isavailable. This is achieved through multihopcommunications, which allow a node to reach faraway destinations by using intermediate nodesas relays.

The probabilities of path duration and pathavailability strongly depend on the mobility patternof the network nodes. Indeed, the path duration(availability) is determined by the duration(availability) of its links, which on its turn dependson the movement of a node with respect to the

other. To characterize the nodes position with res-

pect to each other, we need the spatial distribution

of a single node over time Figure 6.

Figure 6: Flowchart

RELATED WORK

The geographic area covered by a conventional

structure based network (e.g., cellular network,

WiFi network, etc.) is populated with base stations

(also called access points) that are connected to

each other via a backbone. A mobile node can

use the network when it has a direct (single-hop)

connection to a base station, but as soon as it is

beyond the reach of the base stations' coverage,

the mobile node is disconnected from the struc-

ture-based network. For the operator, the usual

solution to this problem consists in increasing the

coverage by adding antennas and for the user to

move until he reaches a covered region.

An alternative solution would be to allow multi-

hop communications in the structure-based

network, which would make it possible for the


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isolated node to ask other nodes to relay its traffic

to or from a base station (Ivan Stojmenovic and

John Wiley & Sons, 2002).

In Delay Tolerant networks, Node cooperation

is fundamental to ensure acceptable perfor-

mance: in fact, differently to more traditional (fully

connected) types of wireless multi-hop networks,

nodes are typically requested not only to act as

message forwarders, but also to store in their own

buffer other nodes’ messages for a very long time

interval (store-and-forward communication).

Thus, both energy and memory resources,

which are very limited in a typical mobile node,

has to be sacrificed for the other nodes’ good.So

the performance of common DTN routing proto-

cols under different levels of node cooperation

are characterized.

Nodes within a network transmit their own

information, and serve as relays to help other

nodes transmissions (Rohit Negi and Arjunan

Rajeswaran, 2003). Several energy-efficient

cooperative transmission schemes have been

investigated in (Philo Juang et al., 2002) charac-

terizing their outage behavior. Most existing

cooperative protocols operate in a time or

frequency-sharing manner, such that each node

sends its own messages and relays its partners'

messages in separate time/frequency slots.

ZebraNet is a wireless sensor network aimed

at wildlife tracking. The ZebraNet system includes

custom tracking collars (nodes) carried by

animals under study across a large, wild area;

the collars operate as a peer-to-peer network to

deliver logged data back to researchers. The

collars include global positioning system, flash

memory, wireless transceivers and a small CPU;

essentially each node is a small, wireless

computing device. Since there is no cellular

service or broadcast communication covering the

region where animals are studies, ad hoc peer-

to-peer routing is needed(Philo Juang et al.,

2002); (Ajay Koul et al., 2010); (Macker J P and

Corson M S, 2001); (C F Garca-Hernndez et al.,

2007).

The Terminodes project [4] is a long term

research project aimed at studying and

prototyping large scale self-organized mobile ad

hoc networks. Here the terminodes refer to

terminal and node. Self-organized networks

distinguish themselves from traditional mobile ad-

hoc networks, based on the traditional Internet

two level hierarchy routing architecture (S K Tiong

et al., 2012), by emphasizing their self-

organization peculiarities (JanuszKusyk et al.,

2011). Self-organized networks are non-authority

based networks, i.e., they can act in an indepen-dent way from any provider or common denomi-nator, such as the Internet, even if they still requireregulation (self-organization).

Self-organized networks are potentially verylarge and not regularly distributed. In principle,one single network can cover the entire world.Density is supposed to be very high in small areas(e.g. towns), and low in large areas (Sahin C S,2008); (Macker J P and Corson M S, 2001).

Self-organized networks are highly co-operative. The tasks at any layer are distributedover the nodes and any operation is the result ofthe cooperation of a group of them.

THE ATTRACTION &

REPULSION ALGORITHM

The node movement considered here in this israndom and the boundary restriction is removed.The node movement in general is guided by thealgorithms which consider the node mobilitywithin the network boundary. The attraction and


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repulsion algorithm works on the principle that,the nodes which move apart from each other bya predefined distance are repulsed and therepulsed nodes, establish a link with those nodesthat are nearby.

Here the minimum distance that is allowedbetween two nodes as “MinD” and the maximumdistance that the two nodes can be at as “MaxD”.The distance between the nodes is calculatedcontinuously; the node positions and thedistances are updated periodically in therespective routing table. During the randommovement, if two nodes happen to come neareach other and the distance between them is lessthan “MinD”, then the nodes are repulsed. Ifnodes drift apart from each other and the distanceexceeds “MaxD”, then the nodes are attracted.The distance is calculated using Euclidiandistance formula. In real time this algorithm wouldbe running infinitely. This is required to maintainconsistent connectivity.

The algorithms have been utilized to maintaincontinuous data transfer between nodes in thenetwork.

Algorithm: [The attraction & repulsion method]

1. Initialize the nodes with random movement inthe provided geographical area except node0 and node 1.

2. Calculate position of each node except node0 and node 1.

a. For every node except node 0 and 1, findout position of i’th node as (Xi, Yi)

b. For every node except node 0 and 1, findout position of j’th node as (Xj, Yj)

3. Display the node positions, used forcalculating the distance.

4. Calculate distance between two nodes usingeuclidian distance formula

d = (x2 – x

1)2 + (y

2 – y

1)2 (1)

5. If distance is less than MinD, it means thenodes are close to each other.

a. Select a random number for repulsion in Xcoordinates.

b. Select a random number for repulsion in Ycoordinates.

c. If j’th current node is greater than previousposition, moving towards (maxX, ---) thenrepulse back to (minX, ---) coordinates.

d. If j’th current node is greater than previousposition, moving towards (---, maxY) thenrepulse back to (---, minY) coordinates.

e. If j’th current node is less than previousposition, moving towards (minX, ---) thenrepulse back to (maxX, ---) coordinates.

f. If j’th current node is less than previousposition, moving towards (---, minY) thenrepulse back to (---, maxY) coordinates.

6. Make the position within the boundary ofsimulation area.

7. If nodes are far away for attraction, attract toinitial position by setting the position of node(Xi, Yi) coordinates.

8. Repeat the above steps to maintain coordi-nates, where nodes i and j are close enoughand not far from each other.

EXPERIMENTAL SETUP

The proposed Attraction & Repulsion algorithmis simulated using Network Simulator 2(NS2)(HaninAlmutairi et al., 2013).The simulationenvironment is summarized in the Table 1.

RESULT ANALYSIS

The Simulation was carried out by increasing thenumber of nodes in the sequence 10, 20, 30, 40,


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50, 60, 70, 80, 90, 100 for better understanding

and comparison. The simulation also considers

the bounded network and thus a comparison is

being made. The simulation and the analysis

clearly show the advantages and benefits of

moving from bounded network to boundary-less

network.

The Figure 7 shows the ratio of the number of

packets delivered to the destination. The number

of nodes delivered with bounded simulation is

less and the overall performance shows the

improved packet received ratio against the total

number of nodes. As the number of nodes

increases, there is better delivery of packets and

in turn reducing the number of packets lost with

boundary less simulation. During emergency

situations the boundary less network provides

better connectivity.

Figure 8 depicts the total number of packets

dropped during the simulation. Packet drop

occurs when one or more packet fails to reach

their destination. Packet drop is closely linked with

the quality of service. The performance of the

protocol is better as long as the packet drop value

is less. In our experiment it is observed that,

packet loss is reduced with more number of

nodes in case of boundary-less network. In case

of bounded network when the nodes move out

of the boundary, the connectivity is lost and hence

the packets get dropped to a larger extent.

However this is not the case in boundary-less

network since the boundary is removed and the

connectivity is maintained. Hence data is more

secure in this when more number of nodes are

present in the large geographical area, hence

better data delivery.

Figure 8: Number of Nodes vs. Total Packet Dropped

Figure 7: Number of Nodes vs. Total Packet Received

Simulation area (m x m) 1000x1000

Simulation time (s) 900

Min. Number of Nodes 10

MAC layer protocol 802.11

Maximum velocity (m/s) 50

Topology used Flat grid topology

Application layer protocol Constant bit rate

Table 1: Simulation Setup

Packet forwarding is the basic method of

transmitting the packet from source node to

destination node. The graph in Figure 9 shows

the total number of packets forwarded from the

source to destination. Routing is the process

where in the router/system decides destination

node to transmit the packet. Packet forwarding

is extensively used to keep unwanted traffic off

networks. In bounded network, if the nodes move


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Hop count refers to the number of hops to the

destination. It is a rough measure of distance

between two hosts. Hop counts are often useful

to find faults in a network, or to discover if routing

is indeed correct. Network utilities like Ping can

be used to determine the hop count to a specific

destination. This prevents packets from endlessly

bouncing around the network due to routing

errors. Both routers and bridges are capable of

managing hop counts, but other types of

intermediate devices (like hubs) are not. The

Figure 11 shows consistent hop count when

increase in number of nodes.

The graph in Figure 12 describes average hop

count. It is the average number of hops among

the shortest paths of all node pairs.

out of the boundary the link is lost and this

requires more packets to be forwarded in all

possible directions. This may lead to duplication

of packets and hence leading to more traffic. In

case of boundary-less network this does not

happen.

Figure 9: Number of Nodes vs.Total Packet Forwarded

Figure 10: Number of Nodes vs.Packet Delivery Ratio (%)

Packet delivery ratio (PDR) is defined as the

ratio of data packets received by the destinations

to those generated by the sources. The greater

value of packet delivery ratio means the better

performance of the protocol.

The Graph in Figure 10 shows the ratio of data

packets that are successfully delivered during

simulations time versus the number of nodes.

PDR =Total packets received

Total packets sent (2)

Figure 11: Number of Nodes vs.Total Hop Counts

Figure 12: Number of Nodes vs.Average Hop Count


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The throughput of network is the amount of

data transferred from source to destination in a

specified amount of time. Data transfer rates for

disk drives and networks are measured in terms

of throughput. Typically, throughputs are

measured in kbps, Mbps and Gbps. The average

hop count is reduced in the proposed technique.

The diagram in Figure 13 describes through

put is the rate of successful packet delivery over

a communication channel. This data may be

delivered over a certain node. The system

throughput or aggregate throughput is the sum

of the data rates that are delivered to all terminals

in a network.

BENEFITS & APPLICATIONS

The major advantages are as follows. Therandom movement proposed, provides betterconnectivity. The nodes will always be connectedand due to this, better throughput is achieved.The connectivity also ensures continuoustransmission which provides more appropriatedata for the analysis purpose. The count of nodesthat are required to cover larger geographicalarea is less. The lesser count of nodes in turnreduces the cost and leads to increasedefficiency.

The following are the major area ofapplications of proposed technique.

1. This would prove valuable in militaryapplications, especially during war whereimmediate action and data is required.

2. Understanding, analyzing and trackingwildlife movement by more appropriatedata.

3. Establishing better communication duringnatural calamities, since the movement israndom.

4. Unmanned vehicles have been recentlylaunched. The approach mentioned in thispaper can be used for better trafficmanagement in the coming future.

Figure 13: Number of Nodes vs.Throughput of Network (KBps)

The Figure 14 explains end-to-end delay. The

end-to-end delay of a packet is defined as the

time it takes to reach the destination after it is

locally generated at the source. The expected

end-to-end packet delay is obtained by averaging

over all packets of the n traffic flow in the long

term, and without incurring any ambiguity, it is

called the packet delay. The delay is more in

bounded network and thus the performance is

reduced.

Figure 14: Number of Nodes vs.Average End-to-End Delay


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CONCLUSION

The proposed attraction and repulsion algorithm

takes into consideration both the position based

technique and map based technique. The

previous position information is considered from

the routing table for the distance calculation. The

algorithm proposed has the advantages of better

efficiency and throughput. The packet drop is

considerably less and the cost involved is less. It

is also noted that packet delivery ratio increases

with the increase in nodes.

The only disadvantage that can be assumed

is the high traffic; however due to the long lasting

connectivity, this high traffic constraint can be

ignored.

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