Adaptive Data Distribution for Collaboration

Luis Guillermo Torres-Ribero and Alexandra Pomares Quimbaya

Systems Engineering Department, Pontiﬁcia Universidad Javeriana, Bogot

a, Colombia

Keywords:

Mobile Collaboration, Dissemination Adaptation, Data Distribution.

Abstract:

This paper presents an adaptive data distribution model for mobile collaborative applications called ADDOCO.

Its goal is to adapt the way data is disseminated in environments where Internet is not always available to create

a collaboration network. In this kind of environments, ADDOCO allows to send and retrieve information that

otherwise would be unavailable. The dynamic information dissemination model of ADDOCO was tested in

a collaboration application using smartphones proving its utility to enhance the distribution of the required

information.

1 INTRODUCTION

The increasing number of Internet-capable devices

makes remote collaboration and data storage possible

by enhancing the interaction with constant commu-

nication and the possibility of sharing resources and

information. Storage services like iCloud, Dropbox,

Box, Ubuntu One, etc. are internet based storage ser-

vices that rely on the network to handle the informa-

tion and the resources they manage. This kind of ser-

vices allows the user the possibility of accessing his

information and resources in any place at any time.

With the increase of mobile devices, these on-line

services are an adequate solution for data storage and

processing, overcoming some of the limitations mo-

bile devices have. However, all of these services re-

quire an Internet connection to work properly. In a

scenario where no Internet connection is available, a

lot of the above mentioned services would be immedi-

ately affected by not being able to work correctly and

most of them will need to wait for a new Internet con-

nection available in order to work at all. Lack of In-

ternet can be found in developing countries where In-

ternet coverage is not as widespread as in other coun-

tries; even in developed countries in some conditions

(e.g. high mountain roads, isolated places, etc.) might

have no Internet connection whatsoever, diminishing

the possibility of remote collaboration, even when the

devices have technologies that make them capable of

making a network of their own and collaborate. For

example if there is a landslide in a mountain road

where no mobile Internet is available, a network can

be created with nearby smarthphone users in order to

send information on the landslide, e.g. photos, loca-

tion, number of injured people if any, etc. and get

adequate help from nearby entities. There are tools

that allow people to interact while being on a mobile

environment and allow users to share information be-

tween them. However, these services rely on having

an active Internet connection in order to be able to

spread notiﬁcations among the users.

The requirement of having Internet independent

collaborative networks motivates the creation of this

work. This paper presents ADDOCO: an adaptive

data distribution framework that supports collabora-

tive mobile environments making a dynamic transi-

tion on the data dissemination method they use to

communicate and allowing to change dynamically the

entity that plays a role in a collaborative task. These

dynamic properties are based on the context of the

entities, taking into account their abilities, location,

collaboration phases and other relevant contextual in-

formation. The structure of this paper is as follows:

Section 2 describes some of the basic concepts around

data dissemination; then Section 3 shows ADDOCO,

a framework that considers user context and collabo-

ration phase in order to dynamically disseminate data.

Section 4 shows related works and their contribution

to this work, afterwards Section 5 shows a prototype

of ADDOCO and its functional evaluation. Finally,

Section 6 shows the conclusions and future works.

2 PRELIMINARY CONCEPTS

In order to have an appropriate understanding of the

problem context, the key elements involved in this

157

Torres-Ribero L. and Pomares Quimbaya A..

Adaptive Data Distribution for Collaboration.

DOI: 10.5220/0004004901570162

In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 157-162

ISBN: 978-989-8565-10-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

work are introduced in this section.

Contextual information is the information that

considers environmental elements, location descrip-

tion and interaction of people, among other character-

istics, that help deﬁning a complete scenario (Bellav-

ista et al., 2013). Contextual information can be very

wide and may take into account a lot of elements in

order to correctly describe the particular scenario in

which some action is taken. In this particular case,

we restrain contextual information as a series of el-

ements that are relevant to the scenarios in which

the presented framework will be used, such as med-

ical contexts, emergency management contexts, etc.

There are mainly two contexts that are considered in

this work: the Collaborative Context and the Mobile

context.

The Collaborative context determines in a log-

ical way the elements involved in a scenario. It in-

cludes the elements that will determine whether a per-

son is available to collaborate and the different roles,

which that particular person could assume during a

collaborative scenario. For example, if a person is a

medical doctor and its willing to collaborate during

an accident, the collaboration context for that person

will include that he is willing to assume the role of a

doctor if it is required.

The Mobile context determines the involved ele-

ments in a physical way, this is, considering elements

that can be interpreted as physical signals or data; for

example, if there is any Internet connection available,

the speed at which the user is moving, etc. Location is

an important contextual element that is present in both

Collaboration Context and Mobile Context. How-

ever, location is expressed differently in each context.

In the Collaboration Context location will be repre-

sented as a Hierarchical Location, which describes lo-

cation as a topology or symbolic place, e.g. a room X

inside building Y (Prayogi et al., 2007)(Zhang et al.,

2006). In the Mobile Context location is represented

as a Cartesian Location which describes locations as

a set of coordinates or GPS assisted geometric calcu-

lations e.g. 04 degrees 00’ N and 63 degrees 00’ W.

Other important elements are Participants, which col-

laborate sending and receiving information and exe-

cuting actions according to their knowledge and role.

It is important to notice that Participants are the

main elements in the model because they are mobile

entities that share and store information. Additionally,

a Participant is able to act as a bridge between two

participants. Each participant will have a set of roles

that he is willing to assume.

For example, in the landslide scenario, if there

were injured people and a doctor is quickly required,

Participant A could look for the Doctor role in par-

ticipants nearby. If a nearby Participant B has in his

set of roles the Doctor role, then he will be asked to

assume that role in healing the injured.

Each participant has relevant data that must be dis-

tributed; to achieve this different variables must be

taken into account, such as, the availability of Internet

connection and the location, both physical and log-

ical. For example, in our landslide scenario, infor-

mation about the time when the landslide occurred,

the location where it happened and the number of

injured people must be distributed to Participants to

whom that information is relevant (e.g Firemen, Doc-

tors, Road Safety Department, etc.)

Since these two variables are dynamic, this is, are

prone to change rapidly, the data distribution method

must also by dynamic. In order to address this is-

sue, different data dissemination methods have been

selected and are the basis of the dynamic transition of

data distribution according to the context of the par-

ticipants. For example, if in the landslide scenario,

the accident occurred in a road and the people who

are driving through that road do not know each other,

the ﬁrst step in the dissemination of data would be to

ask if a required role is nearby. If, on the contrary,

people already know each other along with role in-

formation, a communication protocol can be estab-

lished and a dynamic communication protocol with

a basic structure will be generated according to the

scenario needs. There are different data dissemina-

tion models (Bellavista et al., 2013) that are used dif-

ferently according to the requirements of the environ-

ment that uses them. Since a highly-dynamic environ-

ment is being evaluated, then dissemination methods

must also be highly-dynamic.

Some of the data dissemination models are: i)

Sensor Direct Access dissemination: Distributing the

data directly to a speciﬁc place, ii) Flooding-Based

dissemination: Distributing the data to a speciﬁc

group, iii) Gossip-Based dissemination: Distributing

data randomly among available nodes and iv) Selec-

tion Based dissemination: Distributing data creating

a backbone of nodes.

The next section will present ADDOCO, a frame-

work that dynamically changes data dissemination

methods in mobile environments according to contex-

tual information and available participants.

3 ADDOCO

Due to the requirements of data sharing in highly- dy-

namic environments, this is, mobile and distributed

environments; ADDOCO is created as a framework

that aids in the distribution of data between partici-

ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems

158

Figure 1: Smartphones creating a smartphone-network.

pants among a collaborative process. To achieve this,

ADDOCO adapts, according to the context, the model

used for data distribution between the participants in-

volved in a collaboration.

3.1 Distribution Principle

When mobile devices are connected to the internet

they can access on-line services that allow them to

communicate, obtain and even process information.

Some mobile devices nowadays are smartphones, de-

ﬁned as “a mobile phone that can be used as a small

computer” by the Cambridge dictionary and are ca-

pable of doing a lot of data processing. Smartphones

take more advantage of on-line services, by having

synchronized contacts, conﬁguration ﬁles, etc. Most

of them have the ability to create networks between

them in order to share information and communi-

cate. It is possible to think in a smartphone network

that would act like a small scale Internet, with smart-

phones providing services to other smartphones while

communicating and sharing data. This capacity of

making a network between them can be very useful

when collaboration is required, but there is no Inter-

net connection that allows smartphones to do so. In

a scenario where no Internet is available the smart-

phones could create a network between them, with no

guarantee that all of the smartphones connected to the

internet will be inside the smartphone network due to

technical restrictions like location, i.e. too far to be in-

cluded in the network, or permissions, i.e. the device

has not allowed smartphone networking, as seen in

Figure 1. Within the smartphone network there may

be smartphones that act as a bridge connecting two

smartphones that are within its range, but not within

the range of each other. For example, if three de-

vices A,B and C are making a network and the de-

vice A needs to send some data to C, but C is out

of range, then B, who is in range of both A and C

will act as a bridge transmitting information between

both A and C. This scenario is illustrated in Figure

2. Taking advantage of this possibility, different dis-

semination methods can be dynamically selected ac-

cording to the context. For example, if a landslide

occurs in a road where no internet access or mobile

Figure 2: Smartphone Network Bridge.

network is available, someone affected by the land-

slide could start making a network with nearby drivers

in order to send information on the landslide like pic-

tures, location, if there were accidents, etc. In this

scenario the device of the user will look for other

devices and send information, either randomly to a

group of those nearby users using gossip-based dis-

semination, or selecting a range to send to all users

within that range using ﬂooding-based dissemination.

When a pattern has been established, the dissemina-

tion method changes to a selection based method, cre-

ating a backbone based on the interactions of each

participant.

3.2 Dynamic Dissemination Process

In order to describe the dynamic dissemination pro-

cess some basic concepts must be formally deﬁned.

A Participant has a physical Location. Note that the

location here is represented as a Cartesian coordi-

nate in order to be interpreted appropriately by the

device. The Participant also has a set of Permis-

sion that allows him to request, obtain and manipu-

late Data within the device. It is important to remem-

ber that Data will be stored as closely as possible to

the node that generated it. Therefore the Data mod-

elled in this context is not a replicated copy of Data

available someplace else, but represents the Data that

a mobile device is responsible for creating, updating

and disseminating. Note that the Mobile context also

has a Connection Status component. This component

has information regarding its environment, for exam-

ple if the Participant is connected to a network, it

must search within near devices in order to perform

a collaborative task, and will aid in selecting the best

dissemination method that a requested Data needs to

follow in order to arrive at its destination.This contex-

tual information is a key element used to dynamically

adapt the dissemination method. The “dissemination

service” algorithm is described in Algorithm 1. In this

algorithm a directory is ﬁrst used in order to obtain the

dataHolder, this is, the entity responsible for hand-

AdaptiveDataDistributionforCollaboration

159

Algorithm 1: ADDOCO Dissemination Process.

Require: Data requirements, Access Permissions speci-

ﬁed, Max number of jumps.

Ensure: Data obtained or stored

1: nJump ⇐ Max number of jumps

2: if Directory is active then

3: dataHolder ⇐ call retrieveDataHolder with

Task,Location,Role

4: else

5: for all Entity in nearEntities do

6: if Entity 6= dataHolder and nJump > 0 then

7: dataHolder ⇐ call Dissemination Process

with Entity,Task,Location,Role, (nJumps-1)

8: end if

9: end for

10: end if

11: return dataHolder

ling the data requested. A number of jumps are spec-

iﬁed (1) in order to know how many neighbours must

be queried to try to obtain the requested data. If there

is an active Directory with the information of the data

handler (2) the dataHolder is then acquired. If the

Directory is not available then the query is made to

near entities. If a near entity does not have the data, it

will recursively ask its neighbours for the dataHolder,

having one jump less than the original caller (7) un-

til it reaches zero (6). The element nearEntities are

dynamically located according to both contextual and

collaborative information according to the messaging

protocol if: a) the collaboration model relies on a

centralized role and someone who plays that role is

nearby, most likely a sensor direct access will be used.

b) the collaboration model relies mainly on message

or event interchange in a publisher-subscriber way,

the selected dissemination method would be selection

based. c) the collaborative model relies on roles, but

there are not many entities that play the role in range a

ﬂooding based dissemination may be adequate, and d)

There are some of the required roles nearby a gossip

based dissemination could obtain the data.

3.3 ADDOCO Architecture

The main components of the architecture are de-

scribed in Figure 3. The BPMN parser component

is in charge of obtaining a BPMN model described in

XPDL (XML Process Deﬁnition Language) and in-

terpreting them so that both; the mobile context and

the collaboration context obtain the data in their own

terms in order to correctly execute the tasks described.

The Mobile Context Manager component handles

the data, location and connectivity capabilities and

status of the mobile device. The Collaboration Con-

text Manager manages all the elements related to the

collaborative process such as the sequence of the tasks

Figure 3: ADDOCO Architecture.

to be executed, the role that will perform each task,

the abilities of each user, etc. This manager handles

the dynamic role assignment in ADDOCO. The Dis-

semination Manager will take into account the in-

formation provided by both (the Mobile and Collab-

oration context managers) in order to determine the

right dissemination model. This manager handles the

dynamic dissemination in ADDOCO. A collaborative

process using ADDOCO will start by deﬁning the col-

laborative services involved in collaboration. This in-

volves the deﬁnition of roles, their required abilities

and the contextual elements that are going to be con-

sidered. After these basic components have been de-

ﬁned, a BPMN model is made in order to link those

services and determine which role will execute them.

The translation of a BPMN model into a collaborative

service is not detailed due to the scope of this paper.

4 RELATED WORKS

This section presents an analysis of the main lim-

itations of the architectures and frameworks related

to the intention of ADDOCOM. MoCA (Sacramento

et al., 2004) is an architecture oriented towards mo-

bile collaboration, by communicating each node with

another directly and obtaining information through a

proxy. This architecture models effective collabora-

tion architecture, but it does not deﬁne how data is

managed and highly depends on the availability of the

proxy for new interactions. Other solutions are dis-

tribution applications as Solar, (Chen et al., 2008) a

middleware that aids in the creation of data-centred

applications, by letting the program request data to

the middleware and handles the data distribution for

the application, storing and retrieving it using differ-

ent distribution models. However, once a distribution

model has been selected it distributes data in a spe-

ciﬁc way and it does not adapt according to the chang-

ing context of the application. There are also frame-

works and middlewares for building collaborative ap-

plications that take into account the above mentioned

concepts and act as a guideline for new or exist-

ing programs and determine how they distribute data.

SALES (Corradi et al., 2010) is a middleware that

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160

aims to distribute information in a context-oriented

mobile based application. The SALES model takes

into account the individual context in each node and

notiﬁes others of the contextual information it han-

dles. SALES aids in data distribution by reducing

the weight of data transferred and the amount of data

transmitted while at the same time increasing the rel-

evance of the disseminated information. However,

an algorithm for intelligently distributing data is yet

to be done. Another work establishes a publisher -

subscriber dissemination, which is a selection based

dissemination method (Wu et al., 2010), between Ve-

hicular Ad-Hoc Networks (VANETs). This project

proposes the transmission of real time information on

the trafﬁc to surrounding vehicles while taking into

account their behaviour order to predict their future

location and whether the information is relevant or

not. This work aids in disseminating trafﬁc data but

only as information to other vehicles and it cannot be

applied to a complex collaboration scenario. Zim-

bra (Zimbra, 2011) is another collaborative applica-

tion that provides real time information and aids in the

management of schedules, tasks and calendars. Mo-

bile collaboration in these applications is well man-

aged, however, it depends on a server-side to obtain

and manage the data required in order to function

properly, being highly dependent on an Internet con-

nection. The ADDOCOM framework considers dy-

namic roles of the users, their location and context

information, takes into account collaboration models

and has a Dynamic Data Dissemination method that

adapts to the environment, addressing the data man-

agement requirements of a distributed mobile collab-

oration environment.

5 PROTOTYPE

In order to make tests of the ADDOCO model, a mo-

bile prototype was made. This prototype aims to val-

idate the usefulness of the collaboration model and

how dynamic data dissemination may aid in obtain-

ing the information required to collaborate in different

environments and contexts. The selected devices for

the test were Android powered devices running 2.3.3

version of the OS (Gingerbread) with no cellular data

internet plan, with an initially active Wi-Fi connec-

tion to connect to an initial directory, and Bluetooth

in order to make a collaboration network that tests

the data dissemination model. The prototype ﬁrst at-

tempts a connection with a pre-established server in

order to obtain the data and begins downloading it. If

that server is unreachable, whether at the beginning

of the process or the connection fails in the middle of

the process, an alternate dissemination method will be

selected using Bluetooth networks. If the connection

with the directory fails the user will be notiﬁed that

the connection has been lost and that nearby users are

being queried for the information. A list of nearby

users who have the requested information is displayed

and the user selects one of the available devices.

Once a user selects a data source who will provide

the information, the provider user’s device will dis-

play a message with the request of information and

additional data on the user who wants to obtain it.

The provider user may accept or reject the request.

If the request is accepted then the information will be

provided to the user that requested it, if the request

is rejected a message will show up in the requester’s

device informing that the connection could not be es-

tablished.

6 CONCLUSIONS AND FUTURE

WORK

The implementation of the ADDOCO model aims to

take full advantage of Ad-Hoc networks in collab-

orative environments. Functional tests proved that

collaboration is enhanced with ADDOCO by having

dynamic methods for obtaining the data while being

aware of the context, thus reducing the time of a po-

tentially non-executable task. Bluetooth technology

was used in order to make closed-range networks to

adequately test the dissemination model; however, we

found that this technology is not the best choice for

making effective data dissemination and collabora-

tive networks due to its restrictions and characteris-

tics. However, it shed light in how the functionality of

a smartphone network could work and the effective-

ness of the collaboration and data distribution model

of ADDOCO. As a future work the prototype is going

to be enhanced in order to work in more complex col-

laborative tasks, taking into account not only the con-

nection status but also the speciﬁc location of the user

and making possible the execution of parallel tasks.

Additionally, ADDOCO will make use of an emerging

technology called Wi-Fi Direct

(WiFi, 2011) in or-

der to overcome technical drawbacks seen during the

use of Bluetooth technology in this context. The Wi-

Fi Direct technology will be supported by Android 4.0

(Ice Cream Sandwich) and due to the implementation

of the prototype using the Android SDK ADDOCO’s

prototype will most likely adopt this technology in a

near future for further tests and improved functional-

ity.

AdaptiveDataDistributionforCollaboration

161

ACKNOWLEDGEMENTS

This work was supported by the project “Iden-

tiﬁcaci

on semiautom

atica de pacientes con enfer-

medades cr

onicas a partir de la exploraci

on retro-

spectiva de las historias cl

ınicas electr

onicas reg-

istradas en el sistema SAHI del Hospital San Igna-

cio” made by the Pontiﬁcia Universidad Javeriana in

conjunction with the Hospital Universitario San Igna-

cio.

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