=Paper=
{{Paper
|id=Vol-1156/paper7
|storemode=property
|title=On the Classification of Cyberphysical Smart Objects in the Internet of Things
|pdfUrl=https://ceur-ws.org/Vol-1156/paper7.pdf
|volume=Vol-1156
|dblpUrl=https://dblp.org/rec/conf/ipsn/FortinoRRS14
}}
==On the Classification of Cyberphysical Smart Objects in the Internet of Things ==
https://ceur-ws.org/Vol-1156/paper7.pdf
On the Classification of Cyberphysical Smart
Objects in the Internet of Things
Giancarlo Fortino, Anna Rovella, Wilma Russo, Claudio Savaglio
University of Calabria
Via P. Bucci 41c, 87036 Rende, Italy
{g.fortino,a.rovella,w.russo}@unical.it
csavaglio@si.dimes.unical.it
Abstract. The vision of the Internet of Things (IoT) based on Smart
Objects (SOs) promotes an high-level architectural organization of the
future IoT designed around the basic concept of SO. An SO is an au-
tonomous, cyberphysical object augmented with sensing/actuation, pro-
cessing, storing, and networking capabilities. An important issue in sup-
porting future SO-based IoT systems is how to classify SOs. Classifica-
tion of SOs is an important activity directly influencing the definition
of effective SO discovery services and management systems. In particu-
lar, the discovery service is a fundamental middleware component of the
IoT as it allows SOs and their users to dynamically discover distributed
SOs and, specifically, the services, operations, and data that they pro-
vide. This paper aims at proposing a reference taxonomy for SOs that is
highly functional for an SO discovery service, and, more generally, for an
SO management system. The taxonomy is based on a metadata model
that is able to describe all the cyberphysical characteristics (geophysical,
functional, and non-functional) of an SO.
Keywords: Internet of Things, Smart Objects, Classification, Discovery, Man-
agement
1 Introduction
According to the “Thing-oriented” vision, the Internet of Things (IoT) refers to
a world-wide network of interconnected heterogeneous objects (sensors, actua-
tors, smart devices, smart objects, RFID, embedded computers, etc.) uniquely
addressable, based on standard communication protocols [3]. In such an IoT, all
things have their identities, physical attributes, and interfaces. They are seam-
lessly integrated into the information network such that they become active par-
ticipants in business, information and social processes wherever and whenever
needed and proper.
In this paper, we refer to the IoT as a loosely coupled, decentralized system of
smart objects (SOs) [12][6]. In particular, an SO is an autonomous, cyberphysical
object augmented with sensing/actuating, processing, storing, and networking
capabilities.
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The establishment of an SO-oriented IoT raises many technical issues involv-
ing low-level communication protocols, programming languages, system architec-
ture, middleware (notably including discovery and matchmaking), management
system, and development methodologies for SO-based (large-scale) applications.
Classification of SOs is an important building block enabling both discovery
and management of SOs. In fact, SO classification allows to characterize an SO
from several perspectives or aspects that are needed to identify and exploit it for
different purposes. A few research efforts can be found in the literature about
SO classification [17],[12],[11],[13], which is indeed still in its inception phase.
This paper proposes a taxonomy for SOs that is based on a metadata
model able to describe all the characteristics (geophysical, functional, and non-
functional) of cyberphysical SOs. This model is technology-neutral and can be
implemented by using any data modeling language (e.g. XML, JSON, etc) and
embedded into SO discovery middleware components or SO management sys-
tems.
The remainder of the paper is organized as follows. Section 2 overviews the
background concepts related to SO-oriented IoT systems. Section 3 describes
the currently available classification models for SOs. In Section 4, we describe
our metadata model for SO classification. Finally, Section 5 concludes the paper,
discusses on-going work and delineates some future research challenges.
2 Background
The transition from the current “Human-Oriented” Internet to the “Things-
Oriented” Internet is already in place, and the dividing line between the real
world and the virtual one is bound to weaken. The development of new en-
abling technologies, increasingly pervasive (like RFID, sensor networks, short
range wireless communications, etc.), combined with the use of concepts and
methodologies already well-established (inherent to distributed computing and
the Artificial Intelligence) make the IoT as the most potentially disruptive tech-
nological revolution of the last 50 years [1]. Moreover, the IoT is considered to
be the enabling element which will definitely integrate and worldwide connect
Smart City, Smart Grid, Building Automation Systems, Body Sensors Networks,
currently developed as “poor” intranet of smart things [18]. The long-term re-
sults of such a revolution are not entirely predictable, as happened to the Internet
in the ’60s: in fact, political, social, technological impacts cannot be precisely
assessed. However, even in the short-term, forecasts say that in 2020 the number
of personal smart devices will be 7 units pro capita and the linked industries are
from multiple sources estimated around $1.9 trillion dollars 1,2 . Consequently, a
wide range of researchers from industry and academia, as well as businesses and
government agencies are proving to be interested in the IoT and hence in the SO
technology. In the SO-based IoT (see Figure 1), SOs cooperate to dynamically
compose and deliver evolved services to humans or to other objects. Thinking
1
https://www.gartner.com/newsroom/id/2602817
2
http://share.cisco.com/internet-of-things.html
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about how to make objects smart and applications to exploit them is quite in-
tuitive, while it is much more difficult to design an architecture that supports
such a complex ecosystem. First, it is necessary to ensure ubiquitous connec-
tivity to all kinds of devices, even the cheapest ones and those with smaller
energy and computational requirements. For that purpose, a new Internet layer,
which embodies application, transport, and network protocols for effectively sup-
porting communication among SOs, should be introduced. Then, effective and
autonomous management for both SOs and application services need to be de-
fined, so that all IoT system components are uniquely identifiable and easy, but
at the same time safely and in privacy, to be composed.
Fig. 1. A view of an SO-based IoT system and related smart applications.
The design of a set of fundamental mechanisms for SO naming, interoperabil-
ity, discovery, interaction and orchestration, converging in a middleware layer,
is probably the most urgent and even more challenging task. In fact, despite the
Internet of today, the problem of scale in IoT will have much more stringent
and critical dimension [15], and entirely new issues would result from the cyber-
physical nature of SO [14]. On this basis, the traditional models of networks,
in which the management functionality resides outside the network in dedicated
management stations and servers, need to be abandoned, pushing cognitive and
autonomic management abilities directly into SOs at design time. It is worth
noting that, apart from future IoT management architectures, classification of
SOs is another important task that involves the definition of a suitable SO meta-
data model on the basis of which SOs can be discovered and managed according
to their cyberphysical characteristics.
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3 Related Work
In [17], an SO classification accoding to the concepts of creator and purpose is
defined. In particular, the creator can be either an individual creating SOs for a
personal purpose (e.g. personal use) or an industrial company that creates SOs
for business. The former SOs are called self-made whereas the latter ones are
named ready-made. The purpose of an SO may be to play a role in a specific
application/system or to be reused in a wide range of different applications. The
former is defined specific, while the latter open-ended. However, such a classifi-
cation only considers two dimensions (creator and purpose) that are not related
to the cyberphysical characteristics of the SOs. Thus, such classification cannot
be used in an operational way within an IoT system. In [12] authors classify SOs
in activity-aware objects, policy-aware objects, and process-aware objects. Each
SO type is characterized by the following design dimensions: (i) awareness, which
is the ability of SOs to understand (environmental or human) events of the SO
surrounding context; (ii) representation, which refers to the programming model
of the SO; and (iii) interaction, which defines the communication with users.
Such classification is oriented to the design of SOs within an application domain
and can be usefully exploited during IoT systems development. However, such
contribution is not operational as it can only be used to classify SOs according
to design dimensions.
We are indeed interested in operational classifications that are the base to
build up SO discovery services and management systems.
[11] presented an operational SO classification based on two documents:
smart object description document (SODD) and profile description document
(PDD). SODD contains the meta information of the SO: name, vendor, and list
of profiles. PDD specifies a profile which can be either a detector or an actuator.
A detector contains information about a specific sensing device according to the
Sensor Modeling Language (SML), whereas an actuator is modelled through the
Actuator Modeling Language (AML). The proposed classification is specific to
the SO implementation and management supported by the FedNet middleware
[11]. In [13] and [9], authors proposed a metadata model to represent functional
and non functional characteristics of SOs in a structured way. The metadata
model is divided into four main categories: type, device, services, and location.
The type is the SO type (e.g. smart pen, smart table, etc). The device defines
the hw/sw characteristics of the SO device. Services contains the list of services
provided by the SO; in particular, a service can have one or more operations
implementing it. The location represents the position of the SO. This metadata
model is more general than the previous one and its implementation is currently
available in a discovery framework (named SmartSearch) for SO indexing, dis-
covery and dynamic selection [13],[9].
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4 A Metadata Model for Classification of Cyberphysical
Smart Objects
The proposed metadata model is an extension of the one proposed in [13],[9] and
also borrows some concepts from the other models discussed in Section 3. The
metadata model is portrayed in Figure 2 according to the UML class diagram
formalism. In particular, the proposed model defines a set of metadata categories
that can characterize an SO in any application domain of interest (e.g. Smart
Cities, Smart Factories, Smart Home, Smart Grid, Smart Emergency, etc). The
metadata represent the SOs static parameters, while the related dynamic param-
eters can be retrieved through operations associated to the available services.
Our metadata model is organized in the following eight main categories:
– Identifier: represents the identifier (or ID) of the SO, which allows its unique
identification within the IoT or a IoT subsystem.
– Creator: represents the SO creator, which can be either an individual cre-
ating the SO for personal use, an industrial company that creates it for
business, or an academic research lab implementing it for research purposes.
– Physical Property: represents all physical properties of the original object
without any augmentation and smartness.
– Type: represents the primary type of SO (e.g. a smart pen, a smart chair,
a smart office). Moreover, a secondary type can also be given that contains
information about the SO design classification as proposed in [12].
– Device: defines the hardware and software characteristics of the device that
allow to augment and make smart the object. Device can be specialized into
one of the following three categories:
• Computer: represents the features of the main processing unit of the
SO (e.g. PC, embedded computer, plug computer, smartphone).
• Sensor: models the characteristics of a sensor node belonging to the SO.
• Actuator: models the characteristics of an actuator node of the SO.
– Service: represents a service provided by the SO. A service has a name, a
description, the type (sensing, actuation, object state), the return (primitive
or complex) type. It can also be associated with QoS indicators. Each
service may contain a list of one or more different operations that implement
the service.
• Operation: defines the individual operation that may be invoked on a
service. An operation is equipped with a set of parameters necessary for
its invocation, and a description.
– Location: represents the geophysical position of the SO. It can be set in
absolute terms, specifying the coordinates (latitude and longitude), and/or
in relative terms through the use of location tags.
– QoS Param: defines a QoS parameter associated to the SO. Different QoS
parameters may be defined such as trust, reliability, availability, etc.
The generation of a metadata description document for a simple SO can be
done by the SO creator/manager who, knowing the SO in details, can describe
90
its characteristics following the required formalism. Moreover, generation could
be automatically accomplished by a module installed on the SO, usually called
information provider, which can dynamically generate the metadata [13].
Fig. 2. Smart Object Metadata Model.
4.1 An Example of Metadata Representation
In Figure 3, the description of an SO (Smart Desk), based on the proposed
metadata model implemented in the JSON format, is reported. The smart desk
is able to detect the presence of its user and is equipped with a display that
provides information to its user. The manually-generated JSON document has
eight members associated with each of the eight categories of metadata previ-
ously described (Identifier, Creator, Physical Property, Type, Device, Service,
Location, and QoS Param). In particular, the smart desk provides a sensing ser-
vice to check whether or not a user is at the desk and an actuation service to
send messages, targeting the desk user, onto the desk display. There is only one
QoS Param defined which is the level of trust (in the range 0..1) of the smart
desk.
5 Conclusions
In this paper we have proposed a novel metadata model for SO classification.
The model is operational and can be embedded into discovery services for in-
dexing, searching and selecting SOs and into SO management systems for SO
querying. The model extends and enhances different SO classification metadata
models currently available in the literature. On-going work is being devoted to
implement the model into the ACOSO agent-oriented middleware for SO devel-
opment [8],[10],[7]. Future research challenges will involve the definition of al-
gorithms/methods for automatic multi-layer classification of SOs [5][2]. In fact,
91
even though the proposed model is currently thought for operational purposes
(strongly related to discovery services or management systems), text-based rep-
resentations of SOs (e.g. JSON or XML-based) could be processed for obtaining
higher-level classifications so as to create a cyberphysical digital library. This
library could be used not only to access the SOs according to catalogs like it is
commonly done with digital documents/objects of digital libraries [16][4], but
also to support (i) the development process of SOs, specifically the design phase,
and (ii) the analysis of SOs, i.e. all live and historical information produced
and/or recorded by SOs, through ad-hoc defined GUIs.
6 Acknowledgements
Authors wish to thank Paolo Trunfio and Marco Lackovic for the definition
and implementation of the previous version of the smart objects metadata
model. This work has been partially supported by DICET INMOTO Organi-
zation of Cultural Heritage for Smart Tourism and REal Time Accessibility
(OR.C.HE.S.T.R.A.) project funded by the Italian Government (PON04a2 D).
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{
"identifier":{ "id": "desk1"},
"creator":{ "name": "Sensyscal Lab"},
"physical_properties": [ {"dimension": "120x80x90"} ],
"type": {
"primaryType": "desk",
"secondaryType": "activity-aware"
},
"devices": [
{ "computer" :{ "type": "PC"},
{ "sensor" :{ "type": "presence"},
{ "actuator" :{ "type": "monitor"}
],
"services": [{
"id": "isUserAtDesk",
"name": "isUserAtDesk",
"type": "sensing",
"return-type": "boolean",
"description": "TRUE: user is at desk; FALSE: user is not at desk",
"operations": [{
"id": "isUserAtDesk1",
"description": "one shot request to retrieve the user’s presence at desk"
}]},
{
"id": "setDisplay",
"name": "setDisplay",
"type": "actuation",
"param": "message",
"return-type": "none",
"description": "The message param is output on the Display",
"operations":[ {
"id": "setDisplay1",
"param": "information message",
"description": "visualize information message on the display"
}]}
],
"location": {
"latitude": "3921’47.16’’N",
"longitude": "1613’32.58’’E",
"place": "University of Calabria",
"building": "Cube 41C",
"floor": "3",
"room": "Sensyscal Lab",
},
"QoS_params": [ {"trust": "0.95"}]
}
Fig. 3. JSON representation of a smart desk according to the SO metadata model.
94