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Abstract—Abstract.
Keywords—Internet
ecosystem; EcoDiF
I.
of
Things;
physical
devices;
IoT
INTRODUCTION
Introduction.
II.
ECODIF: A WEB ECOSYSTEM OF PHYSICAL DEVICES
IoT environments are typically characterized by a high degree of hardware and software heterogeneity, several devices
with different capabilities/functionalities, large amount of network protocols, incompatible applications, and proprietary
solutions that are hard to manage. Furthermore, the wide dissemination of the IoT paradigm has the potential of producing
a considerable impact in the daily lives of the human beings, so
that new applications from several domains and that are based
on interacting physical devices and engaged in complex relationships arise. Given such scenario, it is necessary to provide
abstractions for physical devices and services to applications
and end-users, as well as means to manage the interoperability
between such heterogeneous devices.
In this context, our recent work [1] has introduced EcoDiF
(Web Ecosystem of Physical Devices) [2], which is a Web
platform for connecting devices with applications and/or endusers in an IoT ecosystem [3] in which new ideas and products
can be developed in an organic way. Therefore, the main goal
of EcoDiF is to integrate heterogeneous physical devices in
order to provide services for supporting real-time data control,
visualization, processing, and storage, and also to enable their
use in several application domains, such as environmental
monitoring, public urban infrastructure monitoring, health care,
prevention of environmental disasters, etc. The applications are
built as Web mashups [8] by using data (feeds) provided by
several sources and available at EcoDiF and also using other
resources from the Web.
A. Architecture and Implementation
In order to achieve its goals, EcoDiF defines several modules (middleware services) that compose its logical architecture, as illustrated in Fig. 1.
Fig. 1. EcoDiF architecture.
The Devices Connection Module aims to facilitate the connection of physical devices. In this perspective, device manufacturers can configure their devices according to the EcoDiF’s
specific API to enable their integration with the platform, as
well as users can connect their pre-configured devices to perform the operations of the provided API. Such devices are
connected to EcoDiF through customized drivers developed for
each specific device. These drivers play a very important role
in terms of the integration of the devices with EcoDiF since the
heterogeneity of such devices is abstracted away from the users
and applications that make use of the data provided by them.
Such transparency and interoperability are the main essential
requirements for middleware platforms in the context of the
IoT paradigm and consolidates one of the EcoDiF’s main
goals. Therefore, EcoDiF is able to provide a unified access to
data and services provided by the integrated devices through
high-level interfaces, besides contributing to make the development of applications easier in the highly distributed and
heterogeneous IoT environments. Furthermore, the complexity
of the devices and the underlying network environment are
hidden from applications and users, thus releasing them from
the task of explicitly manipulating infrastructure protocols and
services.
Once the respective drivers enable the connection between
EcoDiF and the integrated devices, these drivers obtain the data
collected by the devices (the so-called feeds) and structure
them by using the EEML (Extended Environments Markup
Language) protocol [4], which is a XML-based language that
describe the data obtained from devices in a specific context
(environment). Afterwards, such structured data are sent to
EcoDiF through HTTP PUT requests according to the REST
(REpresentational State Transfer) architectural style [5, 6] in
order to be registered by the Data Manipulation Module.
The Visualization and Management Module provides a
Web interface to enable users to manage the devices connected
to EcoDiF. Through this interface, users can: (i) monitor the
state and the localization of their devices; (ii) create alerts and
notifications (the so-called triggers) about the feeds, and; (iii)
visualize historical data stored in the platform. In turn, the
Collaboration Module aims to facilitate the collaboration
among EcoDiF users, thus enabling them to perform searches
for devices and applications from their respective metadata
(type, user, localization, etc.) through the EcoDiF’s Web interface. The Storage Module consists of two basic repositories: (i)
a repository for storing data by using a relational database,
and; (ii) a repository for storing application scripts in a file
system. It is important to highlight that these repositories may
use a Cloud Computing infrastructure [7] to store relational
data and files, thus providing quality attributes such as robustness, reliability, security, availability, and scalability. The
Common Services Module aggregates mechanisms inherent to
the platform, such as security (in terms of user authenticity,
confidentiality, and integrity), applications life cycle, transactions, etc.
Finally, the Applications Module is intended to provide a
model and environment for programming and executing applications that make use of the data (feeds) available at EcoDiF
and generate new information to be available at the platform. In
EcoDiF, these applications are built as Web mashups [8],
which are ad-hoc Web applications created from the composition of different types of information provided by several
sources. For instance, a sensor monitors the temperature in a
given location, but the user wishes to combine this information
with a map that informs the localization of the collected
measures. Therefore, a single mashup application can compose
such temperature and localization information. For developing
applications, EcoDiF adopts the EMML (Enterprise Mashup
Markup Language) language [9], which is an open XML-based
language for developing Web mashup applications from by
integrating data from several sources, as well as from Web
services and relational databases.
Given the features of its modules, EcoDiF envisions four
profiles of stakeholders (as depicted in Fig. 1), namely:
(i) device manufacturers, which develop drivers to their
devices in order to make them compatible with the EcoDiF’s
API;
(ii) data providers, which are device owners that make
the data produced by their devices available at EcoDiF
through the specific drivers for each device;
(iii) application
developers,
which
build
Web
applications or services that take as inputs data (raw data
provided by the connected devices or more refined data
produced as output of other applications) available at EcoDiF
or produced by any other Web accessible resource, for their
business purposes, and;
(iv) data consumers, which are users that interact with
EcoDiF in order to consult information (about devices and
provided data, applications, etc.) that is available at the
platform.
III.
VALIDATION CASE STUDIES
In order to validate our platform and verify its potential for
integrating heterogeneous devices and enabling data control,
visualization and processing, we have developed some proofof-concept applications in different scenarios. The next subsections describe such applications.
A. Participatory Sensing
The broad proliferation of mobile devices with Web capabilities have enabled to collect information about the environment, weather, traffic, people, etc. over large areas for little
incremental cost [10]. In this perspective, the Participatory
Sensing concept [11] emerged as means to retrieve such information by emphasizing the involvement of users as active
players for providing these data, which may range from private
personal observations to the combination of data from hundreds, or even thousands, of individuals about where they live,
work, and play [12]. Through wireless and/or mobile networks,
participants scattered across a given place, city, or even the
world, can easily send data to servers that process them and
integrate them with other data, e.g. GIS map layers and weather reports. Participatory Sensing has shown to be very useful in
several scenarios, such as transportation planning, public health
maintenance, environmental sustainability, etc.
In the context of private planning, which can be viewed as a
Participatory Sensing scenario, there is the need of estimating
the presence of public (people) in given areas in order to better
provide resources to them. In a conference, for example, it may
be interesting to address how many participants have attended
to workshops, tutorials, etc. or issues related to the appropriate
levels for environmental parameters (e.g. temperature, lightness, noise). Under this scenario, we have exploited geolocation capabilities of mobile devices (such as smartphones and
tablets) in order to provide the localization of the respective
users in given areas within a scientific conference in a building.
For experimental purposes and given the devices integration
capabilities provided by EcoDiF, we have developed a driver
for the Android operating system installed on the mobile devices. Such driver was responsible for: (i) collecting the localization information; (ii) structuring it in EMML, and; (iii) sending it to EcoDiF through a HTTP PUT request. As a result, a
presence map application retrieves the localization information
registered at EcoDiF and displays the number of attendees that
have checked in the sessions of the conference (rooms A and
B), as shown in Fig. 2.
B. Data Processing Centers Monitoring
In data processing centers (DPCs), it is necessary to monitor assets and then notify the stakeholders (managers) when
possible abnormalities in the operations arise. Besides the assets and the logical infrastructure of the DPC, it is also necessary to monitor variables related to the physical infrastructure,
such as temperature, air humidity, electric power consumption,
etc. However, sometimes the employed monitoring solutions in
this scenario are not systematized and are unintegrated, so that
managers (the main stakeholders in this scenario) typically
need to use generic-purpose software or specific proprietary
software for each device by explicitly accessing them. Moreover, due to the lack of integration among the existing monitoring solutions, they are not able to have a systemic view about
the real situation of the DPC and its physical structure and
constituent assets.
As illustrated in Error! Reference source not found.,
EcoDiF can enable the integration of such devices and the data
provided by them and make them available to the users through
the Web. Therefore, the DPC managers can control what is
happening in the ambient and can be notified (through e-mail
or messages sent to their mobile devices, for example) about
meaningful events. In this scenario, the assets are usually managed by using the SNMP (Simple Network Management Protocol) protocol, which is an application-layer protocol used to
manage devices in a network that enable to collect several
information about such devices through OIDs (object identifiers) defined in their MIB (Management Information Base).
In order to integrate such devices to EcoDiF and make the
collected data available, we have developed a driver to the
SNMP protocol to: (i) obtain the information through the
SNMP protocol with the respective OID and the devices’ IP
address; (ii) structure such data in EEML, and finally; (iii) send
the information to EcoDiF through a HTTP PUT request. Furthermore, we have developed a mashup application by using
the EMML language in order to aggregate information (as
feeds) from switches at the UFRN’s DPC (Natal, Brazil). This
application performs requests to EcoDiF to obtain such data
and displays them to the users through the EcoDiF’s Web interface, thus enabling the users to follow the state of several variables regarding the devices at the UFRN’s DPC at real time.
Fig. 3 shows a screenshot of the application that monitors a
H3C switch at the UFRN’s DPC. The EcoDiF’s Web interface
shows several real-time data regarding this switch, such as
temperature, CPU usage, and measures collected from network
interfaces.
C. Gas Pipeline Monitoring
The third application aimed to demonstrate the use of EcoDiF
in a real-world scenario, in which the gas flow in a pipeline at
Rio de Janeiro (Brazil) is monitored at real-time through EcoDiF. Similarly to the applications presented at the previous subsections, we have developed a driver for Dialog 3GTM Universal sensors, which were used for collecting data regarding the
gas flow at the pipeline. After collecting such data, the drivers
structure them in EEML and send them to EcoDiF at real-time,
so that users can visualize the last measures collected by such
sensors at the EcoDiF’s Web interface and also be notified
when abnormal conditions are detected, e.g. in cases when the
measured values indicate a possible gas leak in the pipeline.
Fig. 4 shows historical and current data of gas flow in a pipeline at Rio de Janeiro collected by Dialog 3G Universal sensors
and sent to EcoDiF.
IV.
DISCUSSION
From the developed proof-of-concept applications, it was
possible to observe the clear potential of EcoDiF for managing
any sort of device, thus enabling the easy visualization and
control of the data provided by them at real time and making
EcoDiF a very useful tool for monitoring activities. Moreover,
due to the device integration capabilities provided by EcoDiF,
it is possible to develop applications that are able to use data
provided by several heterogeneous devices anywhere. In this
perspective, we can conclude that the range of applications and
real context that can benefit from the functionalities provided
by EcoDiF is very broad, with contributions with respect to
applied research, industry, and society. For instance, the combination of remote sensing for monitoring with alert and action
mechanisms can be enabled by EcoDiF in order to notify authorities and citizens about imminent disasters or take the appropriate actions for avoiding such event, as in the case of the
gas pipeline monitoring application presented in Section III.C.
Furthermore, as we have already discussed, another important
issue is related to the production of large amounts of data from
several heterogeneous devices, so that EcoDiF can work as a
platform that is able to integrate such devices and easily make
the data provided by them available for users and applications.
V.
DISCUSSION
Recent work in the literature has pointed out that middleware platforms are able to provide transparency and interoperability capabilities, as well as multiple services for users and
applications in highly distributed and heterogeneous IoT environments [13]. There are several research works that address
the conception and implementation of IoT middleware platforms in order to address: (i) the interoperability between heterogeneous devices in several application domains; (ii) adaptation and context-awareness of applications; (iii) dynamic discovery of devices, and; (iv) issues related to the management of
massive data, privacy, security, and scalability in the IoT context [14]. In this section, we briefly present some infrastructures for IoT in terms of the integration of heterogeneous devices and composition of applications that take advantage of
such integration. Most of them is has not been achieved a mature state and the proposed solutions are still in development.
The OpenIoT Project [15] aims to develop an open-source
middleware for IoT applications in a Sensing as a Service
model, which is available at a cloud environment and can be
transparently accessed and configured by the users. OpenIoT
connects sensors with the cloud environment, so that the cloud
resources can be used for data processing and management,
which are useful functionalities that usually cannot be performed at the IoT infrastructure because the resources are limited. In turn, the LinkSmart Project (former Hydra) [16] proposes a service-oriented middleware for embedded systems
that supports the development of applications composed of
heterogeneous physical devices. Such middleware provides
interfaces based on Web services for controlling any kind of
physical device and enables developers to incorporate physical
devices into their applications.
Xively [17] (former Pachube and Cosm) is a cloud-based
IoT platform for managing data provided by devices. It provides a RESTful API for directly sending data from the sensors, enables to visualize historical data, and provides mechanisms to trigger events based on the acquired sensing data. In
Xively, data are hierarchically organized in terms of feeds,
datastreams, and datapoints. As in EcoDiF, feeds represent the
environment data with its respective datastreams, which represent the data provided by the sensors, whereas datapoints represent a specific value of a datastream at a given time. In addition, the data available at Xively can be retrieved in the JSON,
XML (more specifically, EEML), and CSV formats.
Finally, RestThing [18] is a REST-based Web service infrastructure intended to abstract away the heterogeneity of
physical de-vices and provide a way to integrate devices embedded in Web applications. RestThing platform enables developers to create REST-based applications by combining
physical and Web re-sources so that the devices and information are both represented as resources and manipulated
through a uniform RESTful interface, thus enabling accessing
them by using the conventional operations defined in the HTTP
protocol. In addition, the accessed information can be retrieved
in JSON, XML and CSV formats.
Table I summarizes the main features of the IoT middleware platforms described in this section and a comparison with
the features available at EcoDiF. As can be observed in Table I,
all of these proposals have several features in common with
EcoDiF mainly in terms of the integration and interoperability
among heterogeneous physical devices. However, only EcoDiF
enables to build applications by using the mashups technology,
which is appropriated for the IoT paradigm [8]. In this sense,
EcoDiF is the first initiative that tackles the complexity associated for programming loosely couples, opportunistic applications in the IoT context whose components interact and depend
upon each other in an unpredictable way.
TABLE I. COMPARATIVE TABLE BETWEEN ECODIF AND EXISTING IOT
MIDDLEWARE PLATFORMS.
Features
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OpenIoT LinkSmart
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Xively
RestThing
EcoDiF
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
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
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VI.
FINAL REMARKS
Final remarks.
ACKNOWLEDGEMENTS
This work is supported by the Brazilian Education and Research Network (RNP) through the GT-EcoDiF Working
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Fig. 2. Conference presence map application at the EcoDiF’s Web interface.
Fig. 3. Management of a H3C switch at the UFRN’s DPC through the EcoDiF’s Web interface.
Fig. 4. Monitoring the gas flow in pipeline at Rio de Janeiro (Brazil) through the EcoDiF’s Web interface.