AN EXTENDED ANALYSIS OF AN INTEREST-BASED SERVICE

DISTRIBUTION PROTOCOL FOR MOBILE AD HOC NETWORKS

Mohamed Hamdy and Birgitta K¨onig-Ries

Institute of Computer Science, Friedrich-Schiller University, Jena, Germany

Keywords:

MANET, SOA, replication.

Abstract:

In principle, service orientation is a suitable paradigm to allow for effective resource sharing in wireless ad

hoc networks. However, special attention needs to be paid to ensure a high service availability since this is

the basis for reliable service execution. Unfortunately, typical characteristics of ad hoc networks like ever-

changing topology and limited resources have a dramatic negative effect on service availability. Therefore,

special measures are needed to cope with this problem. In general, replication for services as well as data

represents an efﬁcient solution when the availability of some resource needs to be ensured. In this paper,

we extend the service replication and distribution protocol presented in our prior work which is based on the

interest of clients and providers of a speciﬁed service by taking into account not just one but all partitions of

the network. By elaborating an extensive detailed simulation, the efﬁciency of replication and the allocation

correctness are being examined. The results show that relying on our protocol is feasible. Since our protocol

utilizes high level (application level) information about the available services in the ad hoc network only and

does not rely on lower level information like network or protocol speciﬁcs, it is applicable in a wide range of

settings.

1 INTRODUCTION

In many applications, wireless ad hoc networks rep-

resent the only feasible solution to achieve commu-

nication for each of the network participants. The

mobile hosts are working in a collaborative way to

achieve the core network functions such as routing.

In addition, collaboration is needed to provide indi-

vidual devices with information and/or functionality

they do not possess themselves. Ad hoc characteris-

tics like resource-restricted, wireless-enabled partici-

pants’ devices, limitations of battery lifetimes, and the

ever-changing network topology pose very tight con-

straints on all of the applications to be deployed on

this type of network. In order to enable mobile hosts

to share functionality as is needed to guarantee the

functioning of the network, service orientation can be

used: Provided functionality is offered as a service,

required functionality is searched for via a service re-

quest.

However, provider mobile hosts can become tem-

porarily or permanently unavailable at any point in

time for some or all of the clients, the availability of

the services offered by them cannot be guaranteed.

Unavailability can be caused by service providers

leaving the network, but also by the development of

network partitions. Thus, mechanisms are needed to

ensure service availability in such a setting.

The idea to replicate some resource in order to

increase its availability is one of the classical con-

cepts of computer science, applied in many applica-

tions and systems (e.g., DDBMS, RAIDs, DNS, ...).

In fact, in many cases replication is the only feasible

solution to ensure availability. Unfortunately, direct

mapping of replication concepts into ad hoc networks

is not possible as will be explained below.

The ad hoc network topology consists of a vary-

ing number of separate network partitions. Most of

the related work is based on the prediction of the net-

work partitioning behavior, host disjoins, etc. It as-

sumes that all services offeredare vital to the network,

and thus aims at trying to deploy a copy (replica) of

each of these vital services into developing new net-

work partitions, and then ﬁnding solutions to manage

the service and its replica concurrently in the differ-

ent network partitions (Derhab et al., 2005; Derhab

and Badache, 2005; Dustdar and Juszczyk, 2007). As

mentioned in (Hamdy and k¨onig Ries, 2008) this type

203

Hamdy M. and König-Ries B. (2008).

AN EXTENDED ANALYSIS OF AN INTEREST-BASED SERVICE DISTRIBUTION PROTOCOL FOR MOBILE AD HOC NETWORKS.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 203-210

DOI: 10.5220/0002024202030210

 SciTePress

of scheme couples the replication decision tightly to

a speciﬁed lower layer component like the routing

component as in (Derhab and Badache, 2005; Dust-

dar and Juszczyk, 2007). This is a tremendous dis-

advantage. Instead , our work in (Hamdy and k¨onig

Ries, 2008) proposes a protocol for service distribu-

tion for ad hoc networks based on the providers’ and

clients’ interest in a speciﬁed service. This protocol

avoids dealing with the lower layer component and

can thus be ﬂexibly used in wide range of settings.

Also, it takes into account that not all deployed ser-

vices will be equally important to all participants at

all times. Therefore, we introduce a time-varying im-

portance degree, the vitality of a service for a client.

Moreover, how interested a provider is in hosting a

service should be proportional to the overall interest

in it by the client group of this server. This means

that a service that clients and providers are inter-

ested in should be replicated, until interest decreases

in which case it should be hibernated. Our proto-

col achieves this behavior by using two measurements

(client-service-interest and provider-service-careless)

and employing two opposite mechanisms, namely (a)

replication mechanism: allows the service to be repli-

cated to a speciﬁed client based on a certain client-

service-interest level of it and (b) hibernation mech-

anism: allows a provider to hibernate a service after

gaining some certain provider-service-careless or the

gross client’s interest of that service becomes low.

By maintaining an appropriate number of replicas

for the current service interest, the following main ad-

vantages are achieved:(a) increasing the availability,

(b) avoiding the time and computation intensive op-

eration of network partition prediction and detection

and resolving the couplingto the lowernetwork layers

, (c) introducing the service interest as a realistic mea-

surement to be used in the replication process besides

the hibernation mechanism which represents a realis-

tic behavior of service providers in many cases, (d)

introducing the ability of tuning the degree of repli-

cation by utilizing the information of the application

layer.

The work of (Hamdy and k¨onig Ries, 2008) esti-

mated the performance of the service distribution pro-

tocol on just one partition of the network. In this

paper we are extending the protocol to be used on

the whole set of ad hoc network partitions. The con-

cepts to be evaluated by the current work are: Is the

generated number of replicas based on the interest

enough to satisfy the whole number of formed parti-

tions? How is the efﬁciency of the replica placement

based on the service interest? In order to evaluate our

concepts, we developed our own network model and

its related performance measurement. A detailed sim-

ulation and further analysis of the results show a good

and promising performance.

The rest of this paper is organized as follows: In

Section 3, the network model that we use to evaluate

our concepts and the proposed protocol is presented.

The proposed protocol is presented in Section 4 with

its two mechanisms of replication and hibernation. In

Section 5, a primary and extended results of an elab-

orated simulation for our proposed protocol are ana-

lyzed. Finally, the related work and conclusions are

discussed in Sections 2, and 6 respectively.

2 RELATED WORK

J. B¨ose et al. introduce in (B¨ose et al., 2005) an adap-

tive pull protocol for data dissemination over ad hoc

networks. That work estimates the data freshness con-

sidering the data load, by comparing their proposed

optimistic protocol to others techniques like ﬂood-

ing and combinations of the proposed protocol with

ﬂooding feature, they can nearly save 13% of the net-

work load and achieve high freshness rates. S. Mous-

saoui et al proposed in (Moussaoui et al., 2006) a

method for data replication in ad hoc networks with

after building the required replicas of available data,

starts a recovery stage to overcome the effects of

the mobility and ever-changing topology. The work

is based on the frequency of accessing based on a

moving averages equation (like in (Fu and Cheung,

1994)). Also, Work of T. Hara in (Hara, 2003) is

considering the data accessibility frequency to intro-

duce data replicas in many approaches of replicating

a speciﬁed data item on the whole mobile hosts.

In (H¨ahner, 2007) J. H¨ahner introduces a survey

over many data consistency models, then introducse

full and partial replication algorithms taking into con-

sideration the data consistency based on ordering the

observer’s graphs.

In (Derhab et al., 2005; Derhab and Badache,

2005) A. Derhab et al., by estimating the link qual-

ity and employing a partition prediction mechanisms

based on TORA (Park and Corson, 1997) supplies

two mechanisms for pull based replication; (a) repli-

cation (pre-partition formation) and (b) merging (af-

ter two partitions merged) mechanisms. In (Dustdar

and Juszczyk, 2007; Juszczyk, 2005) S.Dustdar et

al. introduce algorithms that take care of replication

and synchronization of services in ad hoc mobile net-

works. Based on a global view of all network nodes,

a replication mechanism (component) by the origi-

nal service node moderates the replication process

per predicted partition, replicas in the new formed

partitions are supposed to be hosted by a powerful

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204

elected node. The used service model assumes pres-

ence of master nodes in order to keep services syn-

chronized. (Hauspie et al., 2001) M.Hauspie et al.

(2001), other research like (Wang and Li, 2002) goes

also on the same fashion and concepts of link evalu-

ation and availability of global view about the ad hoc

network, (Derhab et al., 2005) presented a compari-

son between these approaches.

3 NETWORK MODEL

The goal of this research is to evaluate the per-

formance of the proposed replication/hibernation

mechanism across all network partitions. We use the

network model described in (Hamdy and k¨onig Ries,

2008), which models the network at a certain time

as an undirected, unweighted graph G(N,E) where

N represents the set of uniquely identiﬁed nodes,

and E is the set of edges representing network con-

nections between nodes. G

, E

) represents one

of the network partitions, where: G(N, E) =

, E

)· ··

, E

)· ··

, E

N = N

···

, and E = E

···

Each of the mobile hosts can cover a ﬁxed range with

radius R, a connection is established between two

nodes if the distance between them is less than or

equal to R. All mobile hosts are placed in a square

area. The other components of the network model,

namely mobility , service, and calling models are

described in the following paragraphs.

Mobility Model. We use the random waypoint

mobility model (Lin et al., 2004), in which each

mobile node picks a random constant speed uni-

formly between some preset interval (in our model

[1..12]m/s), then generates a random destination

location to visit after waiting for a pause time

uniformly selected (in our model) between [0..30]

minutes. A slight modiﬁcation was introduced.

By introducing the ”mobility index”, which is a

percentage [0..100]%, we can change the mobility

status of the network. The higher the mobility index,

the higher speed value selection is allowed, and lower

pause times are generated, and vice versa.

Service Model. To simplify the analysis, the

network is maintaining just one service. This service

is placed on the ﬁrst created node in the network.

Three assumptions are made in the service model;

(a) all mobile nodes can participate in the replication

mechanism; (b) the original service is replicable;

in case of service hibernation. Each replica is

described by a requirement index which quantiﬁes

the requirements needed to run this service. These

values are generated as a normal distribution of

about 20% of a general requirement index. The

requirement index is a mimic of the reality; normally

and even if two providers provide the same service,

requirements by each of them to use its service (or get

a replica) will differ. Clients are supposed to ﬁnd the

minimum requirement index from the neighboring

services to communicate with. This varying of the

offered requirement index is one of the responsible

components of distributing the interest of the clients

among the offered services/replicas in the network.

By the work in this paper, since we are relying on

a optimistic replication model, the synchronization

management of the service/replicas is not considered.

Another current ongoing work is addressing the

concurrent service synchronization and states.

Calling Model. Initially, all of the created nodes

seek for the initial (original) service provider node;

only those nodes with at least one feasible path to

the provider node are supposed to start evaluating

the service calling and be involved in the related

replication/hibernation processes. After a while,

service/replicas prevalence through the network is

supposed to cover as much as possible of the ad

hoc formed partitions. Variant calling rates are

maintained by each node; the calling rate is generated

between [0..4] calls per minute, the calling rate

is supposed to be constant during a calling period

of {5,10,15} minutes, and after a pause time of

{0,5,10,15} minutes, the node is supposed to select

another calling rate and so on. Calling rate, calling

period, and pause period are uniformly randomly

generated.

4 THE APPLIED PROTOCOL

The main players in the protocol components are

the two measurements of the client-service-interest

and the provider-service-carelessness.For simplicity,

currently, both of these measurements are based

just on the client calling frequency and the service

requirement index. For a client, it will be considered

to be ”a replica-interested client” if it achieves a

certain number of calls within a speciﬁed time

interval; then the replication mechanism should start.

On the other hand, a provider will be considered as

a ”service-careless provider” if it receives no more

than one call in a speciﬁed time interval; then the

hibernation mechanism should start. Our motivation

for this research was more to investigate the concepts

AN EXTENDED ANALYSIS OF AN INTEREST-BASED SERVICE DISTRIBUTION PROTOCOL FOR MOBILE AD

HOC NETWORKS

205

of replicating the services based on the interest and

watching the service prevalence than on establishing

a sophisticated computation of the interest itself.

Finding more expressive deﬁnitions of interest is,

however, part of our ongoing work. As in (Hamdy

and k¨onig Ries, 2008), the core component actions

of the replication and hibernation mechanisms are

described below.

Replication Mechanism. The core actions of the

replication mechanism are as follows:

• Restore from Cache. If a client is interested

enough to host a replica, it should search ﬁrst if

it had a replica before, if yes it restores it.

• Find Least Requirement Service. If a client is in-

terested enough to host a replica, then, it should

discover the replica with the least requirement in-

dex.

• Pass a Replica. If a client is interested enough

to host a replica, it receives a replica from its

provider.

• Switching to the Local Service. When a replica is

received by a client node (new provider), then the

node switches its calling to the local replica.

• Publish. Allows publishing the new ser-

vice/replica status.

• Check the Correctness. Enforces the interested

client to check if it can achieve a certain correct-

ness of replica placement if it receives its own

replica.

Hibernation Mechanism. The core actions of the hi-

bernation mechanism are as follows:

• Shutdown. Hibernates a local replica.

• Publish. Allows publishing the new replica status.

• Find another Service. Finds another replica of the

called service, if that service is not found.

5 SIMULATION AND

DISCUSSIONS

A detailed simulation for the extended application

of the proposed protocol of (Hamdy and k¨onig

Ries, 2008) has been elaborated. The results are

divided mainly into two groups, the ﬁrst group comes

from applying just the replication mechanism (R

group), and the other one comes from applying both

replication and hibernation mechanisms(R-H group).

In our performance analysis, four main performance

measurements have been introduced:

• Service Availability is the ratio between the time

during which at least one replica was available in

any of the network partitions to the total running

time of the network.

• Success Ratiois the ratio between the number of

successful service calls to the overall number of

calls in the entire network.

• Service Prevalenceis the ratio between the num-

ber of mobile hosts that hosted a replica to the

whole number of network participants.

• Residence Timeis the average time that the replica

remained running (not hibernated) on some mo-

bile host.

5.1 Conﬁgurations

The mobile hosts are placed in a 500 meter

area. The

transmission range of each node is ﬁxed to 120 me-

ters. The network operation time has been set to be

2 hours per sample run; results are obtained from the

average of 20 runs. The replication threshold is set to

be 4 calls per minute; the hibernation threshold is set

to be 1 call in 5 minutes.

The network size is varying from 10 to 140 nodes. In

case of varying the network size, the mobility index is

ﬁxed to be 50% and the maximum allowed prevalence

is set to be 100%. In case of observing the effects of

varying the mobility index, the network size is set to

be 50 nodes, and the maximum allowed prevalence is

set to be 100%. Finally, in case of varying the maxi-

mum allowed service prevalence, the network size is

ﬁxed to be 50 nodes, and the mobility index is 50%.

5.2 Basic Performance Analysis

In both of the proposed groups of experiments (R and

R-H) the network size, mobility index, and maximum

allowed prevalence are varying and the service avail-

ability, success ratio, and residence time are observed.

Service Availability. In the (R) group of experi-

ments, by deﬁnition, the service is always available

in some partition, it might, however, be inaccessible

by nodes from other partitions as depicted in Figure

1(a,b,c). For the (R-H) group of experiments, allow-

ing the hibernationprocess enables the providernodes

to evaluate their carelessness about their offered ser-

vice, this leads to shutting down replicas by some

providers. Figure 1(a) shows, that the higher network

sizes the higher service availability. Starting from a

moderate network size of 30 nodes, about 71% of ser-

vice availability is observed.

The effect of the mobility is presented in Figure 1(b):

the higher the mobility index, the higher service avail-

ability, the reason is that higher speeds and lower

pause times result in the service host nodes to traverse

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206

Figure 1: Service Availability.

more of the network partitions in a shorter time; this

traversing enables more pervasive replication in the

whole network. The proposed protocol shows achiev-

ing high service availability for the lower mobility in-

dices as well, about 80% as a minimum service avail-

ability for the minimum mobility index 10%.

In Figure 1(c), The smaller values of the maximum

allowed service prevalence lead to achieve higher

and steady service availability, these small values

of the maximum allowed prevalence concentrate the

overall client interest to a few number of provider

nodes on the whole network, this interest concen-

tration pushes those provider to keep their services

one and of course minimize their provider-service-

careless measurements. The higher values of maxi-

mum allowed prevalence increase the number of the

service providers and of course distribute the gross in-

terest of the client among them, this leads to varying

achieved service availability, Despite of that, the min-

imum achieved service availability lies above 75%.

Success Ratio. The success ratio is much important

than the service availability because it indicates the

service accessibility form all of the network partitions

Figure 2: Success Ratio.

over the operation time. In Figure 2(a), even applying

just the replication mechanism -in (R) group- does not

insure 100% success ratio for moderate and low net-

work sizes, it achieves 68% for a very low network

density (10 nodes). The difference between success

ratios of (R) and (R-H) groups of results is due to the

reduced service availability in the (R-H) group. Start-

ing from a network size of 30 nodes, the success ratio

is above 70%, and the average difference between the

two groups is less than 15%. The effect of varying the

mobility index in Figure 2(b) is on the same fashion

of the service availability, for the (R) group, the suc-

cess ratio is constant about 100% because the network

size is sufﬁcient to achieve enough interest by the dif-

ferent partitions to maintain at least one replica inside

each of them for all values of the mobility index. For

the (R-H) group, because the higher speeds and lower

pause times enable wider service dissemination on

more network partitions, the success ratio increases

as the mobility index increases. Figure 2(c) shows

the effects of varying the maximum allowed preva-

lence ratio, both curves are dramatically increasing

by slight increments of the maximum allowed preva-

lence, starting from 6% allowed prevalence the (R)

AN EXTENDED ANALYSIS OF AN INTEREST-BASED SERVICE DISTRIBUTION PROTOCOL FOR MOBILE AD

HOC NETWORKS

207

Figure 3: Service Prevalence.

curve is increasing very slowly to close to 100%, on

the other hand, the (R-H) curve is hesitating about av-

erage value about 83%. The valuable notice here is

that there is no need to have high prevalence ratios to

achieve high success ratios.

Service Prevalence. Figures 2(a,b) show the effects

of both the varying network size and mobility index

on the service prevalence. In the (R) group, in Fig-

ure 2(a) we can easily deduce that the prevalence ra-

tio is to be about half (55%) of the number of par-

ticipants which represents a high number of simulta-

neously running replicas over all network partitions.

Also, varying the mobility index (Figure 2(b)) has the

same effect, it produces about (55%) service preva-

lence on the network. On the other hand, in the (R-H)

group, by applying the hibernation mechanism, a sig-

niﬁcant reduction of the service prevalenceratio could

be achieved. The reduction value is about(48%), 2(a),

in case of varying the network size and about(44%),

2(b), in the case of varying the mobility index. The

notable result here, is the effect of the hibernation

mechanism in enhancing the many criteria related

to the number of the running replicas like minimize

the required effort of service/replicas synchronization

and of course link’s utilization.

Residence Time. Figures 4(a,b) are showing that

applying just the replication mechanism of the (R)

curves makes the average residence time of the ser-

vice by the hosting node seem to be constant, the rea-

son is that, the gross interest of the network partici-

pants is divided on the same set of providers, since

Figure 4: Service Residence Time.

that gross interest comes from the uniform distribu-

tion of the calling rates, it is always supposed to have

a constant average. On the other hand, in case of ap-

plying replication/hibernation mechanisms together,

by the (R-H) curves, the sets of the hosting nodes are

supposed to be increased, not all of that participants

can receive the same client interest portion, so many

providers trigger the hibernation mechanism and shut

down their service, this behavior makes the average

residence time decrease affected by both increasing of

the mobility index and the maximum allowed preva-

lence.

5.3 Extended Performance Analysis and

Discussion

In this extended analysis both of the replica-

tion/hibernation mechanisms are applied together.

With these experiments we measure the correctness

of replica placement, and the suitable degree of repli-

cation.

Replication Allocation. Obviously, if we can

compute the optimum number of ad hoc partitions or

clusters for a certain point in time, placing a replica

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208

inside each partition will be the minimum required

number of replicas to keep service/data available

for all participants. Our proposed measure of the

correctness of replica placement is linearly based on

the partition size and the number of already available

replicas in it.

Correctness Ratio CR. In ﬁgure 5, the correct-

ness ratio of the placement process is bounded

between 0% and 100%, if there is no replicas in the

partition the ratio should be 0%, else if there are

one or two replicas in the partition the ratio will be

100%, otherwise ,for simplicity, the ratio is linearly

inversely proportional to the number of replicas, the

ratio becomes zero at a number of replicas equals to

the partition size (Pz). Normally, at least one replica

per partition is an optimal case. Finding two replicas

in the same partition is very healthy from replication

point of view.The following equation describes the

value of the correctness ratio CR

) in an ad hoc

formed partition P

at a certain moment of time t:

) =







0 no(replicas) = 0

1 no(replicas)in



1, 2



Pz−no(replicas)

Pz−2

no(replicas) > 2

where no(replicas): the available number of repli-

cas in the partition.

Figure 5: A Linear Ratio of the Allocation Correctness.

In Figure 6(a,b,c), both of the correctness ratio of

the replica placement process and the replication de-

gree are investigated against the network size, mobil-

ity index, and the maximum allowed service preva-

lence. Allocation correctness is directly proportional

to bnetwork size as in Figure 6(a). By our approach

of replica placement (based on the client interest), we

can achieve a moderate correctness ratio for moderate

network sizes (about 50 nodes). The higher mobility

increases the correctness ratio, starting from the value

of 50%, for 50 nodes, the correctness ratio is about

35%, as in Figure 6(b). Starting from maximum al-

lowed prevalence ratio equals to 12%, (Figure 6(c)),

the achieved allocation correctness is about 35%. So,

the allocation correctness is increased as the network

Figure 6: Allocation Correctness and Replication Degree.

size increases, the same holds for both higher mobil-

ity and maximum allowed prevalence ratio until cer-

tain values are reached, then it becomes steady vary-

ing about a certain average.

Degree of Replication. The meaning of the degree

of replication here is the average number of replicas

in each network partition per that partition size over

the network operation time. Generally, the optimum

number of replicas is depending on the network

connectivity probability. The global connectivity

probability is based on the network density and

could not be precisely computed (Gianuzzi, 2004).

Rather than creating probabilistic models for estimat-

ing the connectivity probability (Madsen et al., 2004).

Figures 6(a,b,c) show that the proposed protocol

achieves a very low degree of replication, this degree

seems to be varying about some average about 15%

against each of the varying network size, mobility in-

dex, and maximum allowed prevalence.

AN EXTENDED ANALYSIS OF AN INTEREST-BASED SERVICE DISTRIBUTION PROTOCOL FOR MOBILE AD

HOC NETWORKS

209

6 CONCLUSIONS

In this paper we have shown that the proposed pro-

tocol of mobile service distribution in (Hamdy and

k¨onig Ries, 2008) is applicable on a real ad hoc mo-

bile network simulation considering the formation of

several network partitions. Simulation showed that by

using interest measurements of a categorized group of

clients, a certain number of running replicas could be

generated which traversed through the network par-

titions achieving a high service availability and suc-

cess ratio, while showing a low service prevalence

on the network participants. Moreover, we propose

deﬁnitions for both correctness of replica placement

regarding the ad hoc formed network partitions and

the replication degree. The proposed protocol shows

promising results for both of these criteria.

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