Efficient Route Discovery In Mobile Ad Hoc Network

Project Report Project Report Project Report Project Report onononon

Efficient Route Discovery in Efficient Route Discovery in Efficient Route Discovery in Efficient Route Discovery in Mobile Mobile Mobile Mobile Ad HocAd HocAd HocAd Hoc NetworkNetworkNetworkNetwork

DEVELOPED BY DEVELOPED BY DEVELOPED BY DEVELOPED BY

Ankur Ghosh

Ankur Paul

Pradip Kumar Mahato

Somarka Chakravarti

Soumyojit Chakraborty

PROJECT GUIDE PROJECT GUIDE PROJECT GUIDE PROJECT GUIDE

Mr. Biplab Mondal Lecturer, Dept. of Computer Science & Engineering

December, 2009

Asansol Engineering College

Kanyapur, Asansol, Burdwan -713304

ACKNOWLEDGEMENTACKNOWLEDGEMENTACKNOWLEDGEMENTACKNOWLEDGEMENT

It is a matter of great pleasure for us to acknowledge our feelings of extreme gratitude and sincere regards to Mr. Biplab MondalMr. Biplab MondalMr. Biplab MondalMr. Biplab Mondal, Lecturer, Department of Computer Science & Engineering, Asansol Engineering College, for the regular & dedicated guidance provided by him. It was because of his steady guidance that we could bring this project to its present form.

We also take this opportunity to thank Mr. Sw. Sw. Sw. Swapan Bhattacharyaapan Bhattacharyaapan Bhattacharyaapan Bhattacharya, , , , Head of the Department, Department of Computer Science & Engineering, Asansol Engineering College, who was always there to provide us with all sort of support, be it technical or moral.

We would also like to acknowledge the help and support of Mr. AmarMr. AmarMr. AmarMr. Amar Kr.Kr.Kr.Kr. GangulyGangulyGangulyGanguly, , , , Principal, Asansol Engineering College.

The acknowledgment will remain incomplete if we do not specially give thanks to our Project Laboratory in-charge, Mr. Suman Mr. Suman Mr. Suman Mr. Suman MallickMallickMallickMallick for providing us the optimum facilities in the laboratory which was of immense help in developing the project.

Project TeamProject TeamProject TeamProject Team

Ankur Ghosh

Ankur Paul

Pradip Kumar Mahato

Somarka Chakravarti


CERTIFICATECERTIFICATECERTIFICATECERTIFICATE

This is to certify that the following project entitled

“ Efficient Route Discovery in Mobile Ad Hoc Network ”

submitted by

Ankur Ghosh Ankur Paul

Pradip Kumar Mahato Somarka Chakravarti


has been carried out in the Department of Computer Science & Engineering at Asansol Engineering College under West Bengal University Of Technology for the degree of B-TECH.

It is a complete fulfilment of their 7th semester project.

So, the performance of the group deserves my approval and acknowledgement.

Approved by

........................................ Project Guide

Biplab Mondal Lecturer, Dept. of CSE,

Asansol Engineering College

Recommendation :

Recommended/Not Recommended

.................................................. ................................................

Internal Examiner External Examiner

CREDITSCREDITSCREDITSCREDITS

Project Guide Mr. Biplab Mondal

Lecturer, Department of Computer Science & Engineering, Asansol Engineering College

Project Team 1. Ankur Ghosh Roll No. : 10801061076 Registration No : 108010111062 Stream : Computer Science & Engineering Batch : 2006 - 2010 ( 4th year ) 2. Ankur Paul Roll No. : 10801061035 Registration No : 108010141002

Stream : Computer Science & Engineering Batch : 2006 - 2010 ( 4th year )

3. Pradip Kumar Mahato

Roll No. : 10801061071 Registration No : 108010141006


4. Somarka Chakravarti Roll No. : 10801061065 Registration No : 108010111055


5. Soumyojit Chakraborty

Roll No. : 10801061044 Registration No : 108010111038


INDEXINDEXINDEXINDEX

PPPPARTICULARSARTICULARSARTICULARSARTICULARS PPPPAGEAGEAGEAGE NNNNOOOO.

1. Introduction 1 1.1 Introduction to Mobile Ad Hoc Network 1 1.2 Background & Motivation 2

2. FResher Encounter SearcH 3

2.1 FRESH Idea 3 2.2 FRESH Algorithm 5

3. Implementation 7

3.1 Data Flow Diagram 7 3.2 Output 10 3.3 System Requirements 13

4. Performance Analysis 14

4.1 Performance Criterion 14 4.2 Simulation Environment 15 4.3 Age Gradients 17

5. Conclusion & Future Scope 18

6. References 19

TTTTABLE OF ABLE OF ABLE OF ABLE OF IIIILLUSTRATIONSLLUSTRATIONSLLUSTRATIONSLLUSTRATIONS

PPPPARTICULARSARTICULARSARTICULARSARTICULARS PPPPAGE AGE AGE AGE NNNNOOOO....

Figure 2.1 : An example FRESH route for N = 32000 nodes, with a random walk mobility process. 4

Figure 3.1 : Level 0 DFD 7

Figure 3.2 : Level 1 DFD 7

Figure 3.3 : Level 2 DFD (I) 8

Figure 3.4 : Level 2 DFD (II) 9

Screenshot 3.1 : Initial Look 10

Screenshot 3.2 : Initial Random Network with source & destination selected 10

Screenshot 3.3 : Network after random motion for 1 minute 11

Screenshot 3.4 : Route given by FRESH 11

Screenshot 3.5 : Selecting destination without selecting source 12

Screenshot 3.6 : Selecting a source as a destination 12

Screenshot 4.1 : Route after warm-up for 10 seconds 16

Screenshot 4.2 : Route after warm-up for 15 minutes 16

Figure 4.1 : Age gradient, random walk. (Empirical conditional mean of distance, conditional on the encounter age). 17

1.1.1.1. INTRODUCTIONINTRODUCTIONINTRODUCTIONINTRODUCTION 1.1 1.1 1.1 1.1 IIIINTRODUCTION TO NTRODUCTION TO NTRODUCTION TO NTRODUCTION TO MMMMOBILE OBILE OBILE OBILE AAAAD D D D HHHHOC OC OC OC NNNNETWORKETWORKETWORKETWORK

A "Mobile Ad Hoc Network" (MANET) is an autonomous system of mobile routers (and associated nodes) connected by wireless links, the union of which forms an arbitrary graph.

Wireless mobile hosts of Mobile Ad Hoc Network communicate with each

other, in the absence of a fixed infrastructure. The routers are free to move randomly and organize themselves arbitrarily. Thus, the network’s wireless topology may change rapidly and unpredictably. Routes between two hosts in a

Mobile Ad Hoc Network (MANET) may consist of hops through other hosts in the network. Therefore, the task of finding and maintaining routes in MANET is nontrivial. Such a network may operate in a standalone fashion, or may be connected to the larger Internet.

Routing protocols for Mobile Ad Hoc Networks generate a large amount of

control traffic when node mobility causes link states and the network topology to change frequently. On the other hand, resources such as bandwidth and battery power are usually severely constrained in such networks. Therefore, minimizing the control traffic to set up and maintain routing state is one of the main challenges in the design of scalable routing protocols for Mobile Ad Hoc Networks.

Mobility can be of two types. In the waypoint mobility model, each node

chooses a random target which is uniformly distributed in the surface and advances towards it at a constant velocity. When it reaches the target, a new target is generated and the node moves again. In the random walk model, nodes move at each step in one of the four cardinal directions, and reflect at the boundary.

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1.2 1.2 1.2 1.2 BBBBACKGROUND ACKGROUND ACKGROUND ACKGROUND &&&& MMMMOTIVATIONOTIVATIONOTIVATIONOTIVATION

Large amount of control traffic is generated by the routing protocols implemented for Mobile Ad Hoc Networks in which link states and the network topology change frequently. Resources such as bandwidth and battery power are usually severely constrained in such networks. Therefore, minimizing the control traffic to set up and maintain routing state is one of the main challenges in the design of scalable routing protocols for Mobile Ad Hoc Networks. One approach to limit control traffic is to establish routes on demand rather than proactively. On-demand routing protocols only establish a route to a destination when it is necessary to send packets to that destination, and therefore incur less overhead at the expense of higher route setup latency. Hybrid routing protocols combine both on-demand and proactive elements for more flexibility in the latency-overhead tradeoff.

On-demand routing overhead can be broken down into two components : route discovery and route maintenance. Their relative costs vary depending on the protocol and scenario, but in general route discovery tends to be costly. In this project, we propose a new approach to reduce the cost of route discovery, which can benefit both pure on-demand and hybrid routing protocols.

When a source node first wishes to establish a route to a destination, it must search the network until it finds either the destination or another node which has a route to the destination. Many of the proposed protocols for Ad Hoc Networks perform a flood based route discovery, whereby a Route REQuest (RREQ) packet is flooded across the network, possibly using an expanding ring search to "grow" the flood until the destination is found. This search is omnidirectional as the source node does not know where the destination lies – the flood cannot favour any one particular direction.

In this project, we propose an algorithm called FRESH that improves the performance of route discovery over omnidirectional approaches. FRESH achieves this performance improvement by exploiting the history of last encounters between nodes (two nodes encounter each other when they are directly connected neighbours). Our work is motivated by a simple observation – ‘the history of last encounters between nodes contains valuable, but noisy information about the current network topology’.

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2.2.2.2. FRFRFRFRESHER ESHER ESHER ESHER EEEENCOUNTER NCOUNTER NCOUNTER NCOUNTER SSSSEARCEARCEARCEARCHHHH

2.12.12.12.1 FRESHFRESHFRESHFRESH IIIIDEADEADEADEA

The FResher Encounter SearcH (FRESH) requires that nodes keep a table of their most recent encounter times with all other nodes. An encounter between two nodes happens when those nodes are one-hop neighbours. The “one-hop” may vary according to the protocol we are using.

We now informally introduce the FRESH algorithm, before giving some insight on how and why it works. Consider a node ‘s’ that establishes a route to a destination ‘d’. We note TLE(i,d), the age of the most recent encounter between nodes i and d, with the convention that TLE(i,d) = ∞ if nodes i and d have never encountered and TLE(i, d) = 0 if i = d.

Source node s searches for the nearest anchor node ‘a1’ such that TLE(a1, d) < TLE(s,d) (this is the nearest node which has encountered the destination more recently than s). Node a1 then searches around itself for the nearest anchor node ‘a2’ such that TLE(a2,d) < TLE(a1,d). Anchor node a2 in turn repeats this FResher Encounter SearcH and the procedure iterates until we reach the destination d (for which TLE(d, d) = 0).

This algorithm is advantageous in the sense that it requires no global

knowledge, and lends itself to a distributed implementation, because each search is defined only in terms of the nodes' local encounter tables. Also, the algorithm only makes use of relative times (encounter ages), and so clocks need not be synchronized.

The figure ahead shows a route computed with FRESH, where the nodes

have been moving according to a random walk. The anchor nodes are represented as crosses. For clarity the path between anchor nodes is approximated by a straight line, though it will in reality follow a multi-hop route through intermediate nodes.

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\

Figure 2.1 : An example FRESH route for N = 32000 nodes, with a random walk mobility process.

What enables FResher Encounter SearcH to compute good routes at a lower cost is a single-step route discovery. The basic principle is simple: For most mobility processes, the distance travelled during a time interval of duration ‘t’ is positively correlated with t. We refer to this as time-distance correlation.

Now consider three nodes i, j, and d. At the present time t = 0, node ‘i’ is separated from node d by a distance ‘Di’, similarly node ‘j’ is separated from node ‘d’ by a distance ‘Dj’. The intuition behind FRESH is that if TLE (i,d) < TLE(j,d), then with high probability Di < Dj. Simply put, “a node that was my neighbour 5 minutes ago is probably closer to me than a node that was my neighbour 5 hours ago”. If time-distance correlation holds then successive FResh Encounter SearcHes will advance towards the destination. This will result in a directional route discovery. In common mobility processes time-distance correlation holds well enough for the algorithm to work very effectively.

Though successive iterations of the FResher Encounter SearcH on average bring us closer to the destination, they may not always advance along a straight line, and so we may not obtain the shortest-path route. Since FRESH establishes routes at lower cost than single-step methods, one may consider that we trade off some route quality for a reduction in search cost and so we must be sure that routes remain good enough so that this is worthwhile.

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2.22.22.22.2 FRESHFRESHFRESHFRESH AAAALGORITHMLGORITHMLGORITHMLGORITHM

Before we actually go into the FRESH algorithm, we would like to put forward to important properties based on which the protagonized algorithm is supposed to work.

PROPERTY 1: The search primitive is omnidirectional, that is to say it does not favour any specific direction for finding the required node. PROPERTY 2: The search proceeds in concentric rings of expanding radius until a node is found which satisfies a given condition.

Ø FRESH Algorithm :

In this algorithm the nodes keep a table of their most recent encounters times with all the other nodes they have encountered. This table is called the TLE table. The pseudo-code given ahead invokes the search primitive through an

abstract interface which allows a querying node N to find the nearest anchor node A having seen the destination node D more recently than a time T. This search is invoked by calling nextAnchor(D,T), which triggers a network search and returns A. The search process creates routing state in the network which will allow N to subsequently send packets to A. This state will be used by the notifyNextAnchor call to instruct A to pursue the route discovery. More precisely, notifyNextAnchor(A,D) will send a packet to A, which triggers invocation of the call FRESH(D) on node A. We note that the packet sent by the notifyNextAnchor(A, D) call does not need to carry the time T representing the current node's encounter age with D since node A only needs its own encounter age with D in order to iterate the search.

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The algorithm, which is run at every node in the network, is as follows:

procedure FRESH (D) begin

if (thisnode.ID = D) then replyToSource()

else begin

T := prevEncounterAge(D); A := findNextAnchor (D, T); if (A != D) then

notifyNextAnchor(A, D); end

end replyToSource() is a invoked when the route is found, i.e., the last node encountered in the search is the destination itself, and notifies the source.

prevEncounterAge(D) returns the time since the last encounter of the particular node with the destination.

procedure findNextAnchor(D, T) begin

repeat currentDist:=0;

repeat currentDist:=currentDist + unitDist; presentNode:=ID of the node at currentDist; if(T>prevEncounterAge(D) of presentNode; return presentNode;

for all nodes at currentDist forever end currentDist : Distance at which Anchor nodes are checked unitDist : Lowest distance for incrementing search area for anchor nodes presentNode : Node under concern to be checked for next Anchor Node notifyNextAnchor(A, D) transfers control to the Anchor Node ‘A’ & executes the procedure FRESH(D) at ‘A’.

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3.3.3.3. IMPLEMENTATIONIMPLEMENTATIONIMPLEMENTATIONIMPLEMENTATION

3333.1.1.1.1 DDDDATA ATA ATA ATA FFFFLOWLOWLOWLOW DDDDIAGRAMIAGRAMIAGRAMIAGRAM

Figure 3.1 : Level 0 DFD

Figure 3.2 : Level 1 DFD

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Simulation Environment

& Route Discovery

0

Simulation Environment

0.1

Route Discovery

0.2

No. of Nodes, Source id, Destination id

Efficient Route


Efficient Route

Source id, Destination id

Drawing the Background

0.1.1

Generate Random

Locations 0.1.2

Draw the Nodes

0.1.3

Generating New

Locations 0.1.4

Node Records



Node Locations

Node Location

Node’s new Location

Start/Stop Motion

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Figure 3.3 : Level 2 DFD (I)

DDDDATA ATA ATA ATA DDDDICTIONARYICTIONARYICTIONARYICTIONARY No. of Nodes : integer

Source id : Node id

Destination id : Node id

Start/Stop Motion : User Command

Node Location : {integer} 2

Node’s New Location : {integer} 2

Next Anchor : Node id

Efficient Route : Source id + { Node id }* + Destination id

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FRESH

0.2.1

nextAnchor

0.2.2

Current Anchor id, Destination id


Efficient Route

Next Anchor id

Figure 3.4 : Level 2 DFD (II)

3.23.23.23.2 OOOOUTPUTUTPUTUTPUTUTPUT 1. An Initial Screenshot of the system asking the user for the no. of nodes in the network.

Screenshot 3.1 : Initial Look

2. The initial random network. The user can select a source by clicking on ‘Select Source’ & then clicking on the desired node. Destination can be selected or deselected in a similar manner. The following screenshot shows the initial network with source & destination selected.

Screenshot 3.2 : Initial Random Network with source & destination selected

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3. The user can start/stop random motion of the nodes by using the ‘Start/Stop Random Motion’ button. The following screenshot shows the same network after random motion for 1 minute.

Screenshot 3.3 : Network after random motion for 1 minute

4. The FRESH algorithm is invoked on pressing the ‘Show/Hide route’ button. The route discovered is shown in the following screenshot.

Screenshot 3.4 : Route given by FRESH

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HANDLING ERRORSHANDLING ERRORSHANDLING ERRORSHANDLING ERRORS 1.The following message box is displayed if the user tries to select a destination before selecting the source.

Screenshot 3.5 : Selecting destination without selecting source

2.The following message box is displayed if the user tries to select the source node as a destination.

Screenshot 3.6 : Selecting a source as a destination

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3.33.33.33.3 SSSSYSTEM YSTEM YSTEM YSTEM RRRREQUIREQUIREQUIREQUIREMENTSEMENTSEMENTSEMENTS

Hardware Requirements

A PC (or Laptop) with mouse

65MB RAM

256 MB of Hard Disk Space

Software Requirements

Operating System : Windows (XP, NT, Vista) or Linux (Red Hat, ubuntu, etc)

Development tool : jdk1.2.4 (or later)

N.B : The system can support a maximum input of 3500 nodes. An input of more than 3500

nodes can result in unexpected results.

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4.4.4.4. PERFORMANCE PERFORMANCE PERFORMANCE PERFORMANCE ANALYSISANALYSISANALYSISANALYSIS

4444.1.1.1.1 PPPPERFORMANCE ERFORMANCE ERFORMANCE ERFORMANCE CCCCRITERIONRITERIONRITERIONRITERION

The first performance criterion will be the cost of the n searches (n is the no. of anchors) in a route discovery. The baseline to which we will compare FRESH search cost is the search cost of a single-step route discovery as employed by existing protocols. Our simulation shows that FRESH allows for a substantial reduction in this cost.

The second performance criterion is the quality of routes. Though successive iterations of the FResher Encounter SearcH on average bring us closer to the destination, they may not always advance along a straight line, and so we may not obtain the shortest-path route. Since FRESH establishes routes at lower cost than single-step methods, one may consider that we trade off some route quality for a reduction in search cost and so we must be sure that routes remain good enough so that this is worthwhile. We have performed simulations to verify the scaling performance of FRESH at large network sizes with a random walk.

The simulations used two metrics to evaluate the performance of the protocol: search cost and route quality. In this section we report the results and further discuss two other important aspects of routing performance: proactive overhead and latency.

ü The search cost of a route discovery is the overhead necessary to build the route from a source to a destination. In the case of the on-demand protocols we are considering here, this will be the cost of the search(es) associated with the route discovery.

ü Route quality measures the difference between the route obtained by the

algorithm and the shortest-hop path.

Our purpose is to evaluate the performance of FRESH in relation only to the mobility process and the size of the network. Nodes are one-hop neighbours when they come within unit distance of each other, and interferences and collisions are not modelled. We note that this simplification is neutral to the evaluation since we have no cross-traffic.

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4.24.24.24.2 SSSSIMULATION IMULATION IMULATION IMULATION EEEENVIRONMENTNVIRONMENTNVIRONMENTNVIRONMENT The topology is a continuous square surface. The mobility model employed

here is random walk in which nodes move at each step in one of the four cardinal directions, and reflect at the boundary.

The simulations run in two phases: warm-up and route computation. In the warm-up phase, nodes move according to the chosen mobility process, populating their tables with the most recent encounter times of each peer node that they encounter. Ideally the warm-up phase runs until an encounter ratio of 40% is attained, where the encounter ratio is the proportion of node pairs that have encountered at least once since the beginning of the warm-up.

Once the warm-up is complete, we apply FRESH to sequentially compute a number of routes between randomly chosen source-destination pairs and record the statistics of interest to us. We note that a single route discovery happens on a timescale of tens or hundreds of milliseconds whereas node mobility occurs on a timescale of several seconds or minutes. This allows us to use the approximation that nodes' positions are static for the duration of a route discovery.

ü Proactive Overheads: During and after the route computation FRESH

requires that nodes keep track of their one-hop neighbourhood in order to maintain their encounter tables with up-to-date information. One solution is for nodes to broadcast periodic hello messages in order to inform one-hop neighbours of their presence.

ü Latency: It is an important aspect of routing protocol performance. In this paragraph we explain why latency of FRESH is similar to the latency of single-step methods. We consider two types of latency: route establishment latency and round-trip time (RTT) latency. Route establishment latency is the time elapsed between the moment when a source requests a route to the destination and the moment when it has a route and may start sending packets. However the main source of latency in route establishment will be the time spent doing expanding ring searches.

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Screenshot 4.1 : Route after warm-up for 10 seconds

Screenshot 4.2 : Route after warm-up for 15 minutes

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4.4.4.4.3333 AAAAGE GE GE GE GGGGRADIENTSRADIENTSRADIENTSRADIENTS In order to see how distance is related to encounter age, we have plotted the

empirical conditional mean of the distance between node pairs, conditional upon their encounter age. The figure shows this empirical mean for the random walk, over a convenient node density. Each point in this graph was computed by considering all the node pairs whose last encounter time is within a certain age interval, and averaging over the distance between these node pairs.

Figure 4.1 : Age gradient, random walk. (Empirical conditional mean of distance, conditional on the encounter age).

We observe that as the encounter age increases, the expected distance

converges to a constant which is on the order of a half side of the square surface. Therefore, once a node moves toward its second target, its position is already independent of its starting point. We see that once the stationary regime is reached, the empirical mean of the distance between two nodes is constant, and therefore does not vary with the encounter age. After the first two hops we reach a node whose encounter age lies within the descending area of the age gradient, and we see that the route makes good progress from there onward.

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5555.... CONCLUSIONCONCLUSIONCONCLUSIONCONCLUSION & & & & FFFFUTURE UTURE UTURE UTURE SSSSCOPECOPECOPECOPE

We have introduced an algorithm for efficient route discovery in Mobile Ad Hoc Networks that uses iterated Fresher Encounter SearcHes. A novel aspect of this algorithm is that it takes advantage of the fact that nodes are moving. Compared to geographic algorithms, an advantage of our proposal is that it does not assume any hardware add-ons such as GPS receivers. In developing this project we have implemented the FRESH algorithm which counts over a variety of flooding and routing techniques.

Though this project has focused on the application of routing between peer nodes, we believe that FRESH will have other applications in Ad Hoc networks. For example, assuming an Ad Hoc network which has one or more gateways to the wired internet, FRESH could be used by a mobile node to establish a route to the nearest gateway.

Under a conservative search cost metric, where we assume a naive search strategy, our simulations indicate that the algorithm reduces the flood overhead by an order of magnitude in large networks. This is significant since route discovery is a major source of overhead in Ad Hoc routing protocols. We believe that this route discovery algorithm may therefore be a useful component in designing routing protocols that scale to larger number of nodes. The search cost will be further reduced with an enhanced search strategy which could for example exploit the directionality of sequential searches.

Future Scope : As part of future work, a full routing protocol incorporating the ideas described in this project can be developed. One topic that will deserve further attention is the possibility to trade off better routes in exchange for a higher search cost, (alternatively to trade off a sub-optimal route for a lower search cost) by recursively applying FRESH to interior portions of the route. This trade-off deserves to be adjustable dynamically, since the optimal point will vary widely depending on the duration of a connection.

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6666. . . . REFERENCESREFERENCESREFERENCESREFERENCES

1. Age Matters : Efficient Route Discovery in Mobile Ad Hoc Networks By Henri Dubois Ferriere, Matthias Grossglauser, Martin Vetterli School of Computer & Communication Sciences

EPFL 1015 Lausanne, Switzerland

2. Data Communications & Networking

By Behrouz A. Forouzan

3. Computer Networks

By William Stallings

4. Java : The Complete Reference

By Herbert Schildt

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Efficient Route Discovery In Mobile Ad Hoc Network