manet's report

7/30/2019 Manet's Report

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Seminar Report on

A Unified Analysis of Routing Protocols used in MANETs

Submitted in partial fulfilment of the

requirement for the award of the degree

of

BACHELOR OF TECHNOLOGY

in

ELECTRONICS & COMMUNICATION ENGINEERING

Under the Guidance of: Submitted By:

Astt.Prof. Trivendra Joshi 060070102154

Department of Electronics & Communication Engineering

Dehradun Institute of Technology, Dehradun

2012-13(ODD SEMESTER)


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DEHRADUN INSTITUTE OF TECHNOLOGY DEHRADUN

CANDIDATES DECLARATION

I hereby certify that the analysis work on research paper which is being presented in the seminar

report of PEC-754 entitled A Unified analysis of routing protocols used in manets in

partial fulfilment of the requirements for the award of the Degree of Bachelor of Technology in

Electronics and Communication and submitted in the Department of Electronics and

Communication Engineering. Dehradun Institute of Technology, Dehradun, UttarakhandTechnical University Uttarakhand, is an authentic evidence of my own analysis work under the

guidance of Astt.Prof. Mr.,Department of Electronics and Communication Engineering,

Dehradun Institute of Technology, Dehradun.

TRIVENDRA JOSHI

This is to certify that the above statement made by the candidate is correct to the best of our

knowledge.

DATE:29/11/2012

Guide name with destination Seminar Coordinators Head of Department

Mr. Kuldeep Chaudhary Mr. Dhruva Chaudhary Prof. (Dr.) Sandip Vijay

(Astt. Prof) Mr. Udit Gupta


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TABLE OF CONTENTS Page No.

1. List of figures 4

2. Abbriviations used 4

3. Introduction about topic 5-6

4. Theory 7-12

5. Analysis and Methodology used 13-14

6. Result discussion 15-21

7. Area of application and present research status 22-25

8. Conclusion 25

9. References 26-28


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1.List Of Figures

Fig. no. Title Page no.

1 A multi-hop data transmission scenario in wireless sensor network 8

2 Simulation Parameter 17

3 Comparision on transmission cost when prouting = 25% 18

4 Comparision on transmission cost when prouting = 50% 18

5 Comparission on transmission cost when prouting = 75% 19

6 Comparision on end-to-end packet delay 20

7 Comparision on energy consumption per packet 20

8 Basic modle of a two way relay system 22

9 Example of a multi hop relay system 23


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2.Abbreviation used

1. AODV Access On Demand Vector Routing Protocol

2. MANET Mobile Ad-Hoc Networks

3. MAC Medium Access Control

4. ARQ Automatic Repeat Request

5. COMAC Cooperative Medium Access Control

6. CBF Cluster Based Forwarding

7. MIMO Multiple Input Multiple Output

8. PRR Packet Reception Rate

9. SNR Signal to Noise Ratio

10.CSMA Code Sense Multiple Access11.SIFS Short Inter Frame Signal

12.DIFS Distributed Inter Frame Signal

13.NAV Network Allocation Vector

14.MANET Mobile Ahoc Network

15.RDSTC Randomized Distributed Space Time Code

16.QOS Quality of Service

17.EZW Embeded Zerotee Wavelet

18.ROI Region of interest


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3. Introduction to Topic:

Mobility brings fundamental challenges to the design of routing protocols in mobile adhoc networks (MANETs). The mobility of nodes implies that the routing protocols of

MANETs have to cope with frequent topology changes while attempting to produce correct

routing tables. To accomplish this, two types of routing protocols have been proposedproactive routing and reactive (or on-demand) routing.

Proactive routing protocols provide fast response to topology changes by continuously

monitoring topology changes and disseminating the related information as needed over the

network. However, the price paid for this rapid response to topology changes is the increasein signaling overhead, and this can lead to smaller packet-delivery ratios and longer delays

when topology changes increase. In the worst case, broadcast-storms" [1] can result in

congesting the entire network. Reactive routing protocols operate on a need to have basis, andcan, in principle, reduce the signaling overhead. However, the long setup time in route

discovery and slow response to route changes can offset the benefits derived from on-demand

signaling and lead to inferior performance.

Given these striking differences between proactive and on- demand routing in MANETs, a

basic question to ask is whether one routing protocol always performs better than the other.

Extensive simulations have already given the answer: both of routing algorithms exhibit

advantageous and inferior performance to the other one depending on different network

configurations, particularly with respect to different node mobility, node density and traffic

load.

Given that proactive and reactive routing schemes for MANETs have relative advantages

and disadvantages, comparing the two is important. Significant work (e.g., [2][5]) has been

conducted to evaluate and compare these protocols under network profiles of various mobility

and traffic configurations. Such performance comparisons have been mostly conducted viadiscrete-event simulations. Simulation-based studies of routing schemes are a powerful tool to

gain insight on their performance for specific choices of network parameters. How- ever, it is

difficult to draw conclusions involving multidimen- sional parameter spaces, because running

several simulation experiments for many combinations of network parameters is impractical.

Few if any analytical studies have been pursued on this topic, and prior analytical work has been

mostly restricted to the analysis or comparison of routing control on routing metric overhead

[6][8], information theoretic aspects [9], [10] or multipath routing protocols [11], [12].

Furthermore, these works do not evaluate the effects of signalling overhead on unicast capacity

at nodes, and none reveals the underlying connection between protocol performance and

network parameters. Hence, the following two questions have remained open:

Does there exist a unified analytical framework, simpli-fied but able to provide the

characterization of essential behaviors of routing protocols?

If there is one such framework, can it also provide network scalability?


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This paper provides a unified analytical framework that parameterizes and quantitatively

evaluates the performance of routing protocols from a joint characterization of the routing

logic and MAC layer. The solution emerges from a combinatorial model that synthesizes the

evaluation of both the routing logic and the operation of the MAC layer, with each of them

being parameterized individually.

The MAC layer at nodes is as well as simplified as a two-customer queuing model, where

the packet loss probability and delay at nodes can be effectively computed. When analyzing

the performance of the MAC layer, we consider the cases of a scheduled MAC (TDMA) and

a contention-based MAC (802.11 DCF MAC). In the combinatorial model, the computed

metrics are synthesized along with the routing logic to produce quantitative measures of the

routing protocols in terms of end-to-end packet loss probability and delay.

It is important to note that this work does not attempt

to model or compare between specific proactive or reactive routing protocols. Rather, the

intent is to capture the essential behaviour and scalability limits in the network size of both

classes of protocols by quantifying their performance within a unified framework. Thesimulation results in this paper are not intended to provide an exact match with our analysis;

they are provided only as a supporting evidence for the conclusions regarding to protocol

behaviors observed in literature. With the aid of simulations, we show that the analytic results

agree with the simulation findings.

We model each wireless node as a two-customer queue

without priority, where the two kinds of customers are unicast and broadcast packets. By

analyzing the service time and waiting time in queue for any unicast packet, we derive the

transmission delay time for any link; by summing up the transmission delay time for each link

of any routing path, we derive the end-to-end delivery delay.The paper is organized as follows. Section II presents

related work in the modeling of routing protocols. Section III presents the mobility model,traffic model and simplified models of routing algorithms used in the analysis in Section IV.Section V characterizes the performance of the MAC layer and provides performancemetrics in evaluating protocol performance. Section VI compares our analytical resultsagainst Qualnet simulations based on scenarios on various traffic loads, mobility and nodedensity configurations. The results illustrate the correctness of our analytical framework,which captures the essential behaviors of protocols and is capable of pinpointing the effect ofvarious parameters analytically. Section VII concludes this paper.


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4. Theory:This paper presents a mathematical framework for the evaluation of the performance of

proactive and reactive routing protocols in mobile ad hoc networks (MANETs). This unified

framework provides a parametric view of protocol performance, which in turn provides adeeper insight into protocol operations and reveals the compounding and interacting effects of

protocol logic and network parameters. The parametric model comes from a combinatorialmodel, where the routing logic is synthesized along with the characterization of MAC

performance. Each wireless node is seen independently as a two-customer queue withoutpriority, where the two types of customers are unicast and broadcast packets. The model capturesthe essential behavior and scalability limits in network size of both classes of routing

protocols, and provides valuable guidance on the performance of reactive or proactive routing

protocols under various network configurations and mobility conditions. The analytical results

obtained with the proposed model are in close agreement with simulation results obtainedfrom discrete- event Qualnetsimulations.

Parameterizing and comparing the performance of routing protocols analytically is a very

complex problem. Conse- quently, the characterization and comparison of routing pro- tocolshas been limited to simulation-based approaches [2] [5], [13][16], under various

configurations. The performance evaluation metrics used in these simulations or experimental-

based approaches include packet delivery ratio, delay and throughput. Network configurations

vary on traffic pattern, mobility and network density. for proactive and reactive protocols to

evaluate the individual routing control overhead. Zhou and Abouzeid [7] presented an

analytical view of routing overhead for reactive protocols, assuming a static Manhattan-grid

network and studied the scalability of reactive protocols. These studies concentrate on the

impact of traffic patterns and they also provide [8] a mathematical and simulation-based

framework for quantifying the overhead of reactive routing protocols.

Zhou and Abouzeid extended their analytical studies to information theoretic techniques toderive analytic expressions for specific metrics, such as control message routing overhead and

memory size requirement [9] where entropy is utilized to derive bounds for these metrics.

They have made fundamental contributions [10] toward a rigorous modeling, design and

performance comparisons of protocols by deriving lower bounds on the routing protocol.

Tsirigos et al. [11], [12] studied multipath routing protocols with the aim of increasing packet

delivery ratio in inherently unreliable networks and developed an analytical framework for

evaluating them. Their work took mobility and topology changes into considerations.

Our work has been inspired by research work in [17] [20] which analyzes and

evaluates the performance of routing protocols by constructing mathematical models of the

routing operations.

Yang et al. [17] set up a mathematical model that imitates the operation of proactive routingand is utilized to optimize the operation of routing protocols in order to strike a balance

between protocol overhead and accuracy. However, this work is specific for proactive protocols

operating under certain conditions.

Lebedev [18] showed an analytical tool for both proactive and reactive protocols and

proposed a model to study the operation of two classic reactive (Ad-hoc reactive Distance

Vector Routing (AODV) [21]) and proactive (Optimized Link State Routing (OLSR) [22])

protocols in the presence offaulty links. However, this work does not cover other aspects of


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network parameters on the performance of routing protocols. Nogales [19] models routing

protocols with the consideration of the behavior of connections at the link layer and

MAC protocol. However, that model is greatly simplified when modeling the packet routing and

scheduling process and does not take into consideration traffic load and the topology of thenetwork.

Jacquet and Laouiti [20] compare proactive and reactive routing protocols performance byproposing probability-based models. However, their models focus on calculating specific

performance metric values, rather than evaluating the general operations of these routing

protocols. Their study is based on random graph models that are simplified and idealized,

such that mac and link layer failure effects are not taken into consideration.

4.1. NETWORK MODEL

4.1A. Mobility Model

Nodes are mobile and initially they are distributed equally over the network. The movement

of each node is independent and unrestricted, i.e., the trajectories of nodes can lead to

anywhere in the network. For nodei

V=

{1,

2, . . .

,N

}, let {Ti(t), t

0}be the randomprocess representing its trajectory and take values in Y, where Y denotes the domain

across which the given node moves. To simplify the model, we make the following

assumption on the trajectory processes. Assumption 1: [Stationarity] Each of the trajectory

pro-cesses (Ti(t)) is stationary, i.e., the spatial node distributionreaches its steady-state distribution irrespective of the initial location. The N trajectoryprocesses arejointly stationary, i.e.,

the whole network eventually reaches the same steady state from any initial node placements,

within which the statistical spatial nodes distribution of the network remains the same over

time.The above assumption is quite fundamental in the sense

that it lays the foundation for the modeling of node movement. Most existing models, (e.g.,random direction mobility models [23], random waypoint mobility models [24], [25] and

random trip mobility model [26]) clearly satisfy our assumption. In other words, our

assumption ensures that, on the long run, the network converges to its steady state and the

stationary spatial nodes distribution can be used in the performance analysis of the network.

4.1.B. Traffic Model

We consider a new traffic flow or simply a new session as one that is associated by the

arrival of a new application-levelsession request at a node i with some destinationj, j = i,in the network. Traffic flows are randomly generated with uniformly distributed sources and

destinations. Long-lived traffic flows are assumed in order to investigate protocol performance

under the steady state of nodes mobility and traffic distributions. Short-lived traffic flows,

reflecting transient behaviors, are beyond the scope of this paper. Furthermore, well-

connected networks are assumed, i.e., if an existing path for any traffic session is broken, there

is always an alternative path (with high probability) available to support continuing operations

of the traffic flow. 1


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4.1.C. Neighbor Sensing

Neighbor sensing protocols, such as periodic broadcasts of HELLO messages, are effective

approaches used in routing protocols (both proactive and reactive) to detect the availability of

links between neighbor nodes. New links are detected when HELLO messages are received

from nodes not included in the neighbor list. Existing links are declared as failed if none of

HELLO messages from the neighbor node is received during a certain amount of timewindow.

4.1.D. Routing Protocols Model

We provide descriptions of generic proactive and reactive routing protocols, which we

believe capture the essential behavior of many designs and implementations of existing routing

protocols. However, this analysis, and hence the generic protocols below, does not consider any

protocol- specific techniques, such as multi-point relay, local repairs and route caching

mechanisms.

Note that the alternative path is not necessarily disjoint with the former broken path.

1) Proactive Routing Protocol: In proactive routing protocols, every node maintains a list ofdestinations and updates its routes to them by analyzing periodic topology broadcasts from

other nodes. When a packet arrives, the node checks its routing table and forwards the packet

accordingly.

Every node monitors its neighboring links and every change in its neighbors results in a

topology broadcast packet. That is flooded over the entire network. Other nodes update their

routing tables accordingly upon receiving the update packet. In a well-connected network, the

same topology broadcast packet could reach nodes multiple times and therefore enjoy a good

packet reception probability. In the paper, we assume that every node reliably receives

topology packets from other nodes.

2) Reactive Routing Protocol: In reactive routing protocols, nodes maintain their routingtables on a needed basis. This implies that when a new traffic session arrives, nodes have to

set up the paths between sources and destinations before starting to deliver data packets.

The process of path setup is called route discovery. Complementarily, another process called

route maintenance is necessary to find an alternative path if a former path was broken. More

specifically:

a) Route Discovery: a mechanism initiated by a node iupon the arrival of a new traffic session in order to discover a new path to a nodej. Nodei floods the whole networkwith route request (RREQ) packets. Upon receiving the RREQ packet, nodej sends out a routereply packet (RREP) along the reverse path to i. As a result, node i usually gets a shortest pathto nodej.

b) Route Maintenance: a mechanism by which a node iis notified that a link along an active path has broken, such that it can no longer reach thedestination nodej through thatroute. Upon reception of a notification of route failure, node i can initiate a route discoveryagain to find a new route for the remaining packets destined toj.

In reactive routing protocols, each node does not maintain routing tables before a routing task

is triggered. They only find a route on demand by flooding the network with RREQs, i.e.,

before sending data packets sender broadcasts router request and initiates a route discovery


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process. If a link breakage is detected during packets delivery, a new RREQ is generated.

The main disadvantages of such algorithms are high latency time in finding routes and

excessive flooding when traffic load is high.Assume that a successful delivery between source node Sand destination node Utakes Khops, that at the first step a route discovery is initiated at Sand that after time 0 source nodeSreceives a RREP and starts sending data packets. Assume that a link breakage occurs at arelay node Fwith probability pi and then a new RREQ is generated for Fand

that it takes time i to restart delivery. Let us suppose that the transmission delay for any

linki is Ti. Then the end-to- end delivery delay Dp between S and Ucan be formulatedasDp = 0 +

K(pii+ Ti).

Compared to the local recovery time in proactive routing protocols, the route discovery time in

reactive routing protocols is much larger.

5. Analysis and Methodology used

5.A. Models

Previous studies [2] [9] have pointed out that the simple radio models (e.g., the ideal binary

model assumes that there are perfect links within a given communication range, beyond which

there is no link) is unlikely to be valid in any realistic environment. Actually, the reliability of a

link is influenced by a lot of factors, such as the transmitter-receiver distance, the emitting power

and the interference noise. To model the reliability, we choose to use the one derived in [2] and

[19], which is based on the log-normal path loss model. The packet reception rate (PRR) p for a

transmitter-receiver distance dis given by

p(d)={1-exp[-(d)BN/2R]/2}8f (6)

whereBNis the noise bandwidth,R is data rate in bits,is the encoding ratio, andfis the frame

length in byte. The signal to noise ratio (SNR) at the receiver can be given as

(d)= Pt-PL(d0)- 10log10(d/d0)+N(0,)-Pn (7)

where Ptis the output power, PL(d0) is the power decay for the reference distance d0, is the

path loss exponent (rate at which signal decays with respect to distance), N(0, ) is a

Gaussian random variable with mean 0 and variance 2 (due to multi-path effects), and Pnis the

noise floor. In this model, low-power wireless links are classified based on transmitter-receiver

distance in distinct reception regions: connected (p1), transitional (0 < p < 1), and

disconnected (p 0). Then, the communication rangeRccan be defined as


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Rc=max{d|P(d)>p threspthres>0 ptres0} (8)

We model a sensor network by an undirected graph G = (V, E), where V is the set of sensor

nodes and the set of wireless communication links,E, is defined as

E={(u,v)V2|uvdu,vRc} (9)

The neighborhood setH(u) of a node u is defined as

H(u)={vV|vu(u,v)E} (10)

5.B. Assumptions

A wireless sensor network composed of a large number of resource-constrained sensor nodes and

several more powerful sinks in an area is considered in this paper. Sensor nodes are

randomly distributed in the area and remain stationary after deployment, and all of them have

similar capabilities and equal significance. Each sensor node sends its data packets to sink nodes

through multi-hop wireless links. All nodes in the network are supposed to implement a CSMA-

like MAC (with ARQ) to share the physical medium. Due to link unreliability, the network is

assumed to employ a more optimal metric [19] [20] instead of the conventional shortest-path-

first policy for setting up an optimum routing path (referred to as intended path) between

source and sink. Each node knows and keeps record of the PRR between itself and any

neighboring node.

6. Result Discussion

6.1 PERFORMANCE EVALUATION: ANALYTICAL RESULTS

Based on reasonable assumptions, we compare the energy efficiency of our EECC and the

traditional methods, i.e., pure retransmission-based and FEC-based. According to previous

researches [23], wireless communication is responsible for the greatest weight in the energy

budget when compared to sensing and data processing. Assume Msensor nodes are randomly


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deployed in an area of size S. LetEtandErbe the energy consumed by a single transmission and

reception respectively.

Theorem 1. Let the per hop energy consumption for FEC, pure retransmission and node

cooperation are denoted byEFEC, Eretrans, andEEECCrespectively, we haveEFECEretrans>

EEECC.

Proof: The energy consumed for each transmission attempted

by a sensor node is

Esignal=Et+(MR2c/S-1)Er (11)

Ideally, we assume that a node retransmits for infinite times. Thus, in the retransmission-based

mechanism, the expected energy consumption for a successful delivery from s to ris

Eretrans

= ps,r

Esingle

+......+nps,r

(1-ps,r

)n-1

Esingle

=nk=1kps,r(1-ps,r)

k-1Esingle (12)

then (12) can be rewritten as

In the FEC mechanism [4], it is required that

Now we analyze the energy consumption of EECC in three different cases of cooperation

scenario. From (13), it is more energy efficient to relay packet on link ( t, u) than (r, u). Thus,

to simplify the analysis, we only consider the energy consumed for data packet transmission in

one hop range, i.e., from s to its next hop relay. Let the cardinality ofTand Q is

denoted by |T| and |Q| respectively.

where

Therefore,we have


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where

and lQ is thenode that relays the packet as the cooperator. Becausepl,r>ps,r, we have

Summarizing the above analytical results in (11) ~ (19), the following inequality holds: EFEC

Eretrans>EEECC.

Now we give the performance bound of EECC

Lemma 1. In idealistic cases with perfect links (ps,r = 1), the data forwarding in one hop range

succeeds at the first time. Thus, the lower bound of per hop energy consumption is Esingle.

Lemma 2. In realistic cases with imperfect links, the packet can be received by the receiver and

overheard by the cooperator candidates. The overall reception rate provided by these nodes is

Hence,transmission cooperation is more likely to be realized when the node density is relatively

high. In sparsely deployed scenarios, we can know that the per hop energy consumption is upper

bounded byEretranswhen there are few cooperation opportunities.

6.2 PERFORMANCE EVALUATION: SIMULATION RESULTS

The performance of EECC was evaluated by conducting simulations via the ns-2 simulator.

Here, we adopted a model which is widely used in previous researches to calculate energy


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consumption for transmission. To receive l bits at the receiver with a distance of d from the

transmitter, energy consumption for a single transmission and reception is given by

where Eelec is the electronics energy, fsd2and mpd

4 are the amplifier energy in the free space

and in the multi-path fading environment respectively, and d0 is a distance threshold that

depends on the environment. In our simulation experiments, the wireless link model is the same

with that in [2], and other parameters are set as shown in Table I.

Fig: 2

The first set of simulation experiments is performed to verify our analysis above in analytical

result. Thus, we manually deployed two sensor nodes and a single sink to create a simple2-hop

data transmission topology as that has been introduced in analytical result. Different amount of

sensor nodes are randomly deployed as the neighbours of the intended data source node to create

different cooperation scenarios, i.e., (|T| > 0, |Q| = 0), (|Q| > 0, |T| = 0), and (|T| > 0, |Q| > 0). To

make the results more pronounced, for each specific transmission scenario the experiment is

carried out independently for multiple times by network redeployment. The performance metricselected is called transmission cost, which is defined as the ratio of the total number of data

transmissions by the total number of non redundant packets sent out by the data source. This

metric reflects both communication overhead and energy efficiency. To fully investigate on the

performance effect by EECC, experiments are performed under different routing reliability

conditions (That is, the PRR metric for becoming an intended receiver, prouting, is set as low


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(25%), middle (50%), and high (75%) respectively). The simulation results for this experiment

set are shown in Fig. 3 to Fig. 5.


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We have several observations concerning these results. First, EECC can actually help to reduce

transmission cost to some extent, as compared to the pure retransmission mechanism (i.e., (|T| =

0, |Q| = 0)). Such cost reduction is even more obvious and stable when proutingof the intended

receiver is relatively low (e.g., prouting = 25%) and when the total amount of cooperator

candidates is relatively high (e.g., |T| = 3 or |Q|= 3). On the other hand, node cooperation

contributes little to the cost reduction when |T| = 1 and ps,t is extremely low (e.g., Experiment

Index = 12, 16, 20 in Fig. 5(a)) or when |Q| = 1 and pq,r is slightly greater than ps,r (e.g.,

Experiment Index = 9, 10 in Fig. 3(b)). This observation helps to validate our analysis on EECCperformance in analytical result.

Second, relaying by the receivers cooperator (tT) is more efficient than by the senders

cooperator (qQ) in general. The reason is that the receivers cooperator is able to reach the next

hop of the receiver directly, while the senders cooperator just substitutes for the original sender

to retransmit the packet to the receiver with a relatively better link. We could even notice that it

becomes less effective to invoke the cooperation by the senders cooperator when prouting

becomes relatively higher, as shown in Fig. 3(b), Fig. 4(b) and Fig. 5(b). Therefore, EECC can

be an optional choice if the sender has a good transmission link to the receiver while there are no

receivers cooperators.


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The above two observations imply that a proper network density can provide EECC with more

and better cooperation opportunities to minimize excessive transmission costs caused by

unreliable links and packet losses. We argue that it is not so difficult to satisfy this condition


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since many applications have already required high density deployment of sensor nodes. In the

second set of experiments, EECC is evaluated in the multi-hop transmission scenarios, by

measuring end to- end packet delay and energy consumption per packet. The network is densely

deployed so that for each intended node, |T| 3 and |Q| 3. The routing metric is the same as

that introduced in [19]. The results are averaged from multiple simulation runs. As shown in Fig.

6, EECC significantly reduces end-to-end packet delay as compared to the pure retransmission

method. An examination of ns-2 simulation traces reveals that this is attributed to a considerable

decrease in the number of packet retransmissions: without EECC, the intended sender will time-

out and resend the lost packet. The delay caused by such time-outs is far longer than that

introduced by the new NAV setting in EECC. On the other hand, EECC is able to decrease such

time-outs by node cooperation on data transmission, and thus effectively reducing the end-to-end

delay. Meanwhile, the decrease in retransmissions also results in the energy saving for packet

transmission, as shown in Fig. 7. These results justify the application of EECC in large-scale

sensor networks with energy constrained sensor nodes and unreliable wireless links.

7. Area of applications and present research status

The research in the area of cooperative communications dates back to the pioneering work in the

1970s, where the capacity of relay channels was studied for the problem of information

transmission over three terminals. Then the channel capacity of relay networks over non-faded

channel was examined. Because of its advantage over conventional one-way communications,

the relaying concept has gained great attention in recent research for an extension to fading

channels .Many important aspects of relay networks have been extensively studied. For example,

the capacity of relay networks over Rayleigh fading channels has been investigated .The

diversity-multiplexing tradeoff of DF and AF relays has been investigated . User cooperation

which is the generalization of relay networks to multiple sources has been investigated

.Relaying/cooperation has been shown to offer a performance enhancement in terms of the

capacity .Although the problems of relaying and cooperations have been studied for years in

many aspects such as communications, signal processing, networking, and information theory,

they still attract the research community as a new paradigm for wireless and mobile networks.

Recently, the relaying method has been introduced in the WiMAX standard and is expected to

spread into many other commercial standards .


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Multi-hop Relay NetworksThe multi-hop communication in relay networks is a very promising approach to improve the

transmission coverage of cellular and ad hoc networks

Fig.8 Basic modle of a two way relay system

In multi-hop relay networks, a transmission between a source node and destination node is

composed of multiple hops as illustrated in Fig. 8. Multihop transmission can significantly

reduce the transmit power compared to direct communications. In addition, because of transmit

power constraints, multi-hop transmission also leads to remarkable coverage extensions by

dividing a total end-to-end transmission into a group of shorter paths. On the other hand, due to

the rigorous effect of fading and shadowing, the lack of line-of-sight can severely degrade the

system performance. Using multi-hop transmission can overcome this dead-end spots problem

and thus another advantage of multi-hop relaying has been pointed out, especially for rural areas

with low traffic density and sparse population. In these areas, there is no economic benefit in

building the whole cellular networks but multi-hop relay networks. Hence, the multi-hop

technique is a promising candidate to meet the desirable goal of contemporary wireless systems

with respect to providing seamless communications.


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Fig.9 Example of a multi hop relay system

Mobile Multimedia over Cooperative Communications

Future mobile communication systems are expected to satisfy the increasing demand for services

with stringent constraints on quality of service (QoS) such as mobile multimedia, mobile video

applications, and mobile streaming on-demand. Due to the source encoding that is usually

imposed to multimedia applications, e.g., speech, audio, image, and video, some parts of the

produced multimedia format have a higher priority for the signal reconstruction than others.

Specifically, errors to headers and markers of such formats that may be induced during

transmission over the radio channel can severely degrade the multimedia quality regardless ofthe reception of the remaining parts. A coded image sequence based on the wavelet transform,

so-called embedded zerotree wavelet (EZW), is divided into two priorities. The higher priority

contains the most significant information of image pixels consisting of the wavelet coefficients

of dominant pass and subordinate pass extracted from several early scanning steps. The lower

priority conveys less important information. Using cooperative communications, two pre-

assigned relays cooperate to form a distributed-Alamouti code which help the source to transmit

the low priority sequence of the image over error-prone wireless channels. Among the whole

relay nodes in the networks, two relay nodes are selected which provide the maximum signal-to-

noise ratio to convey the higher priority. With this strategy, unequal error protection is implicitly

achieved for image transmission without controlling power or varying modulation and coding at

the transmitter at the expense of increasing networks overhead (i.e. feedback information). The


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selection of relays needs limited feedback, hence, making the proposed scheme feasible for

practical applications.

Apart from prioritized data structures caused by source encoding techniques, the actual content

of a multimedia application may result in some parts being more important to the end-user than

others. In particular, as far as context or content of images is concerned, the region of interest

(ROI) prioritizes those areas that attract more attention over the background (BG) content.

Viewers will commonly pay attention to ROI rather than BG. Accordingly, it appears to be more

annoying to observe distortions in the ROI of an image although the BG has high quality. Hence,

it is beneficial from a perceptual quality point of view to rather improve the quality of an ROI

than spending too much resources on the BG. Clearly, this is particularly helpful for wireless

imaging and mobile video services where bandwidth is scarce and expensive. For image

transmission, for example, a more efficient use of available

radio resources such as bandwidth and power may be unequally allocated between ROI and BG

in the way that the former receives a higher portion. In other words, resources could be

controlled to increase the quality of the portion of an image that would be of most interest to the

viewer. For instance, unequal error protection for ROI coded images over fading channels

has been proposed .A novel ROI-based image transmission scheme for wireless fading channels

to improve the quality of ROI using cooperative diversity is proposed. Specifically, image

communications with three terminals, i.e., source, relay, and destination, is considered. In the

first-hop transmission, the source broadcasts the ROI frame to both the relay and the destination.

In the second-hop transmission, the source sends the BG frame to the destination while the relay

assists to enhance the quality of the ROI at the destination by forwarding the ROI received from

the first-hop transmission. With this transmission scheme, the relay does not consume the power

used to transmit BG as in the conventional communications. Therefore, to satisfy the total power

constraint, the relay can allocate more power for the ROI-based image transmission by using the

remaining power reserved for the BG. At the destination, the ROI frames from the first-hop and

second-hop transmission are combined using the MRC technique to enhance the quality of ROI.

It has been shown that the proposed scheme significantly improves the ROI and the quality of the

image compared to conventional approaches.

Using relays to transmit videos has recently also gained an increased interest in the research

community . Specifically, a video multicast strategy for a two-hop cooperative transmission


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scheme has been proposed in .A H.264/SVC encoded video with two layers, namely, base and

enhancement layers is considered so that receptions of more layers lead to better video quality.

The randomized distributed space-time codes (RDSTC) are used to provide users different video

quality based on their channel conditions. The performance of the proposed relaying scheme has

been shown to outperform a conventional multicast systems.

8. Conclusion:

We presented an analytical framework to evaluate the behavior of generic reactive and proactive

protocols. In the model, the operation of the routing protocol is synthesized with the analysis ofthe MAC protocol to produce a parametric characterization of protocol performance. The

effectiveness and correctness of the model are corroborated with extensive simulations. The

model enables in-depth understanding of routing protocol performance, and points out the needto design routing protocols that are capable of confining signaling overhead to those portions of

the network where the routing information is needed, in order to operate efficiently under

different types of mobility and traffic patterns. A similar conclusion can be derived by looking atthe capacity of ad hoc-networks under different types of information dissemination [39]. In our

future work, we plan to incorporate realistic physical layer effect into our modeling framework

to make it more practical and comprehensive.

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