Systems, Man & Cybernetics - April 2016 - 44
actuate, and embed processing,
◆ Social ability: Smart objects as
provide highly pervasive cyberagents should be able to interThe objective of the
physical services to humans and
act, when they deem appropriother machines.
ate, with other smart objects
design is to define
The development of IoT systems
and even humans to complete
the functional
(or entities, e.g., smart home, smart
their own problem-solving
components of
car/vehicle, smart building, smart
tasks and to help other smart
factory, smart port, smart city),
objects in their activities where
the system, their
their management, and their inteappropriate.
specific behaviors
gration in real applications, are
◆ Responsiveness: Smart objects
evolving complex challenges that
as agents should perceive (be
and interactions.
require appropriate methods/techaware of) the environment, in
niques and technology. Such syswhich they are situated and
tems, in fact, consist of many
which may include the physical
distributed and interacting components that are usually
world, a user, and/or a set of other smart objects and
heterogeneous in terms of hardware, communication prorespond in a timely manner to changes that occur in it.
tocols, software interfaces, and output data. To effectively
◆ Proactiveness: Smart objects as agents should not simsupport their development, general and specific requireply act in response to their environment, but they
ments need to be established. While general requirements
should also be able to exhibit opportunistic, goalallow engineering effective and flexible frameworks and
directed behavior and take the initiative where and
middlewares for facilitating IoT systems' programming,
when appropriate.
specific requirements are purposely defined for the target
◆ Mobility: To fulfill physically distributed tasks, mobile
IoT system within its specific application domain. In particsmart objects should be able to physically move from
ular, we group the general requirements that are common
one location where they act to another (as mobile softto all IoT systems into two sets [3]: system-level (hardware
ware agents canmigrate from one environment
devices, physicality, communications, software interfaces,
[machine] to another in a logical environment).
data, development process), which includes requirements
Currently, not much research work has been done to
related to the whole distributed system and its developdefine methodologies and middlewares for developing and
ment, and things-level (heterogeneity and interoperability,
managing agent-oriented IoT systems; the most interesting
augmentation variation, decentralized management,
ones are described in Table 1.
dynamic evolution), which encompasses requirements speResearch conducted at our laboratory is focused on a
cifically referring to the "things" (such as radio-frequency
novel agent-oriented approach to build and manage IoT
identification, sensors, actuators, smart objects, mobile
systems, named the agent-based cooperating smart
devices, machines, and robots) of the IoT system.
objects (ACOSO) approach. It is based on metamodels
To deal with the IoT system development challenges,
exploitable at system analysis, design, and implementawe propose exploiting the agent-based computing (ABC)
tion levels [13]. At the analysis level, we defined the highparadigm [4], which is focused on the concept of "agent"
level smart object metamodel that specifically supports
as a well-defined software engineering and distributed
modeling of ecosystems of smart objects, i.e., it captures
computing paradigm for programming, deploying, and
the relevant high-level aspects and features of smart
managing IoT systems. The ABC paradigm models distribobjects (status, fingerprint, physical property, device,
uted software systems in terms of multiagent systems
service, location) and their relationships (peer-to-peer
(MAS), where agents are networked software entities that
and/or hierarchical) with users, other smart objects,
can perform specific tasks for a user and have a degree of
and digital systems. The objective of the design is to
intelligence that permits them to perform parts of their
define the functional components of the system, their
tasks autonomously by interacting with other agents and
specific behaviors and interactions. The design phase is
with their environment in a useful manner. To date,
currently supported by the event-driven lightweight
agents have been effectively used in many application
distilled statecharts agents (ELDA)-based smart object
domains to analyze and build robust and dynamic distribmetamodel and/or by the ACOSO-based smart object
uted systems and applications. We therefore claim that
metamodel. The former supports a smart object system
their characteristics also fit perfectly those of IoT systems
design based on the ELDA model [14] that can be semiauand their components [5], [6]:
tomatically translated into a simulation object executable a nd a na lyzable by the ELDA Meth tool [15],
◆ Autonomy: Smart objects as agents should be able to
specifically by the ELDASim simulation framework, that
perform the majority of their problem-solving tasks
enables both functional and performance evaluation of
without direct intervention of humans or other agents,
networked MAS. The latter is based on the ACOSO midand they should have a degree of control over their
dleware model [16] that provides specific agent-oriented
own actions and their own internal state.
44
IEEE SyStEmS, man, & CybErnEtICS magazInE A pri l 2016
Table of Contents for the Digital Edition of Systems, Man & Cybernetics - April 2016
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