=Paper=
{{Paper
|id=Vol-1099/paper15
|storemode=property
|title=Towards a Cloud-assisted and Agent-oriented Architecture for the Internet of Things
|pdfUrl=https://ceur-ws.org/Vol-1099/paper15.pdf
|volume=Vol-1099
|dblpUrl=https://dblp.org/rec/conf/aiia/FortinoR13
}}
==Towards a Cloud-assisted and Agent-oriented Architecture for the Internet of Things==
https://ceur-ws.org/Vol-1099/paper15.pdf
Towards a Cloud-assisted and Agent-oriented
Architecture for the Internet of Things
Giancarlo Fortino and Wilma Russo
Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES)
Università della Calabria
Via P. Bucci, cubo 41C, 87036, Rende (CS), Italy
g.fortino@unical.it, w.russo@unical.it
Abstract— In the Internet of Things (IoT), all things (e.g. sensors, performing directed actuation, cooperate with each other, and
actuators, smart devices, smart objects, RFID, embedded exchange information with other kinds of IT devices/systems
computers, robots) have their identities, physical attributes, and and human users.
interfaces. They will be seamlessly integrated into the information
network such that they will become active participants in business, The actual implementation and integration of IoT smart
information and social processes wherever and whenever needed objects, their management as well as the development of real
and proper. The technical realization of this vision is a complex applications atop them, are complex challenges that require the
challenge as distributed heterogeneous IoT components at different synergic use of suitable paradigms and technology for large-
levels of abstractions need to cooperate among themselves, with scale distributed computing. To deal with this challenge, we
conventional networked IT infrastructures, and also with human propose the synergic exploitation of two complementary
users. To cope with this issue, we propose the synergic exploitation mainstream paradigms for large-scale distributed computing:
of two complementary mainstream paradigms for large-scale (i) the agent-oriented paradigm, which fully supports the
distributed computing: the agent-oriented and the cloud computing development of decentralized, dynamic, cooperating and open
paradigms. While the former can support the development of systems, and (ii) the cloud computing paradigm, which
decentralized, dynamic, cooperating and open IoT systems in terms efficiently enables the empowering of computing and storage
of multi-agent systems, the latter can empower the IoT objects with resources of IT systems.
more computing and memory resources and effectively support
system-wide higher-level mechanisms and policies. In this paper, The Agent-oriented Computing paradigm defines
we introduce a cloud-assisted and agent-oriented vision for IoT distributed software systems in terms multi-agent systems
based on layered reference architecture. Finally, we briefly (MAS). In particular, agents are networked software programs
overview our agent-oriented middleware for cooperating smart that can perform specific tasks for a user and possess a degree
objects and a sensor-cloud infrastructure that represent the basic of intelligence that permits them to perform parts of their tasks
building blocks for technically achieving such vision. autonomously and to interact with their environment in a useful
manner. The agent features perfectly fit the CSO features [2,
Keywords - Internet of Things; Smart Objects; Agent-oriented
3]:
Computing; Cloud Computing, Middleware
• Autonomy: agents/CSOs should be able to perform the
I. INTRODUCTION majority of their problem solving tasks without the
direct involvement of humans or other agents/CSOs,
The Internet of Things (IoT) term usually refers to a world- and they should have a degree of control over their
wide network of interconnected heterogeneous objects own actions and their own internal state.
(sensors, actuators, smart devices, smart objects, RFID,
embedded computers, etc) uniquely addressable, based on • Interaction: agents/CSOs should be able to interact,
standard communication protocols [1]. In the IoT, such objects when they deem appropriate, with other software
have therefore their identities, physical attributes, and agents/SOs and humans to complete their own problem
interfaces. They are seamlessly integrated into the information solving and support others with their activities where
network such that they become active participants in business, appropriate.
information and social processes wherever and whenever • Responsiveness: agents/CSOs should perceive their
needed and proper. environment, in which they are situated and which may
be the physical world, a user, a collection of agents, the
In particular, in this paper we model the IoT as a loosely Internet, and so forth, and respond in a timely fashion
coupled, decentralized system of cooperating smart objects to changes that may occur in it.
(CSOs). A CSO is an autonomous, physical digital object
augmented with sensing and/or actuating, processing, storing, • Proactiveness: agents/CSOs should not simply act in
and networking capabilities. CSOs are able to sense, store, and response to their environment, they should be able to
interpret information created within themselves and in the exhibit opportunistic, goal-directed behavior and take
environment where they are situated, act on their own by also the initiative where and when appropriate.
The Cloud Computing paradigm provides flexible, robust sensing/actuating, processing, and networking capabilities.
and powerful storage and computing resources, which enables Although, in such vision, not all "things" can be directly
dynamic data integration and fusion from multiple data sources exploited as the object granularity is coarser, communication
[4]. In addition a Cloud-based approach can offer flexibility among smart objects is homogenized by the adoption of the
and adaptability in the management and deployment of data Internet protocols and "intelligence" is mainly embedded inside
analysis workflows. The dynamic deployment of software the objects themselves.
components as Cloud services removes the need for new client
applications to be developed and deployed when the user Figure 1 shows a high-level layered architecture for the
smart object-oriented IoT vision, where the main layers are:
requirements change. This also introduces an intrinsic
competitive environment for the development of better Application, Middleware, Internet and Smart Object. In
services. Cloud computing layers (Infrastructure as a Service - particular:
IaaS, Platform as a Service - PaaS, Software as a Service - - The Application layer encompasses applications based
SaaS) and software components (e.g., databases, data mining, not only on SOs but also on other IT
workflow tools) can be customized to support a distributed infrastructures.
real-time system for the management and analysis of IoT - The Middleware layer provides as set of mechanisms
objects and data streams generated by IoT objects. for the naming, discovery, high-level interaction
This paper proposes a cloud-assisted and agent-oriented and state management of SOs.
vision of IoT based on a layered reference architecture. - The Internet layer includes application, transport, and
Furthermore, we briefly overview our agent-oriented network protocols for supporting the
middleware for CSOs and BodyCloud, a sensor-cloud communication with SOs and among SOs.
infrastructure, that represent the basic building blocks, which - The Smart Object layer offers programming
will be purposely integrated, for technically achieving such frameworks and tools enabling the design and
vision. implementation of SOs.
The rest of this paper is organized as follows. Section II
provides a look at glance of the mainstream IoT visions. In
Section III our reference architecture for cloud-assisted and
agent-oriented vision of IoT is proposed. Section IV overviews
our agent-oriented middleware for cooperating smart objects
and a sensor-cloud infrastructure, and discusses their
integration requirements to realize the proposed reference
architecture. Finally conclusions are drawn.
II. SMART OBJECT-ORIENTED IOT
IoT semantically means a world-wide network of
interconnected things (an ecosystem of things), which are
Figure 1. SO-oriented IoT architecture
uniquely addressable and based on standard communication
protocols [1]. Things include sensors, actuators, sensor Even though the standardization process of the SO
networks, embedded systems, RFID tags and readers, and other communications based on IP is supported by the IP for Smart
soft sensors in different forms. These things can be deployed in Objects (IPSO) alliance [6], the availability of middleware and
different physical environments to support diversified frameworks that support development and management of SOs
applications domains. They are communication-oriented is still limited. Nowadays, apart from several available
objects and provide identification and information storage (e.g. middlewares for smart environments (e.g. Smart-Its, 2WEAR,
RFID tags), information collection (e.g. sensor networks), Ambient Agoras, Aura, Gaia, iRoom) [7, 8] that are not
information processing (e.g. embedded devices and sensor centered on the very concept of SO, a few SO-specific
networks), and control and actuation (e.g. embedded systems middlewares have been so far proposed: UbiComp [9], FeDNet
including smart actuators). The interesting advantage is that
[10], Smart Products [11], and ACOSO [12, 13].
everything is "reachable" and can be "exploited". The main
disadvantages are that such enormous heterogeneity makes UbiComp [9] defines a paradigm providing conceptual
distributed communication and management very complex; abstractions, the plug/synapse model and a middleware named
moreover, "intelligence" is not embedded and should be GAS (Gadgetware Architectural Style)-OS, which is installed
provided at a higher level by means of smart services and/or on each SO, to manage SO as components of distributed
applications. applications composed of ubiquitous computing services.
Beyond such low-level and network-oriented vision of IoT, FeDNet [10] is based on a data-centric approach.
the smart object-oriented vision is at a higher level of Specifically, FeDNet uses XML-based documents to describe
abstraction and promotes an ecosystem of smart objects based the requirements of an SO application, without considering the
on the Internet [5]. In particular, in such vision IoT is viewed as management of the SOs. Therefore, the services offered by SOs
a loosely coupled, decentralized system of smart objects (SOs), are described through structured documents. On the basis of
which are autonomous physical/digital objects augmented with such documents, the run-time FeDNet infrastructure provides a
semantic association between the applications and the SOs. In
FeDNet, SOs are objects of the daily life with computing and
communication capabilities. However, as SOs are not
proactive, task-oriented FeDNet applications are able to
provide proactivity by SO orchestration.
Smart Products [11] aim at the development of SO
equipped with proactive knowledge. Such knowledge is
exploited to communicate and cooperate with human users,
other smart objects and the external environment. SOs rely on
MundoCore, a communication middleware, and a set of well-
defined ontologies to enable effective cooperation among SOs.
ACOSO (Agent-based COoperating Smart Objects) [12,
13] is a middleware providing an agent-oriented programming
model for CSOs and tools for their effective development. Figure 2. High-level architecture for Cloud-assisted and Agent-based IoT
CSOs are based on an event-driven proactive architecture and
on two different communication models (message passing and
publish/subscribe). ACOSO is currently available atop JADE IV. INTEGRATING SMART OBJECT MIDDLEWARE AND
[14] and JADEX [15]. A more detailed description of ACOSO CLOUD PLATFORM FOR LARGE-SCALE IOT SYSTEMS
is reported in Sect. IV.A. MANAGEMENT
III. A CLOUD-ASSISTED AND AGENT-ORIENTED A. An Agent-oriented Middleware for the Development and
ARCHITECTURE FOR IOT Management of CSOs
To deal with the complexity of the SO-based IoT, we The ACOSO middleware allows for the development and
propose a high-level architecture based on cloud and agent- management of CSOs, which are modeled as agents that can
based computing. The architecture named CA-IoT (Cloud- cooperate with each other and with non-agent cyber-physical
assisted and Agent-based IoT) is shown in Figure 2. The entities to fulfill specific goals. An ecosystem of CSOs
architecture components are: therefore forms a multi-agent system (MAS). ACOSO
currently relies on JADE that provides an effective agent
- The Smart User Agent, which models human users in management and communication support and on an external
the context of specific smart systems. They smart object discovery service that is purposely integrated into
therefore provide GUI-based functionalities ACOSO [17]. Specifically, CSOs can be implemented as either
through which users can request services and/or JADE or JADEX agents, atop both Java-based and Android-
formalize specific service requests. based devices, and can cooperate by a direct coordination
model based on ACL message passing and/or by a spatio-
- The Smart Interface Agent, which defines an temporal decoupled coordination model relying on a topic-
interfacing agent such as brokers, mediators, based publish/subscribe mechanism. Figure 3 shows the JADE-
wrappers. Specifically, they are able to interact based ACOSO platform for CSO development and
with and/or wrap components of the external IT deployment. The platform is composed of three layers:
systems.
- The high-level CSO architecture, which is the
- The Smart Object Agent, which models a CSO. reference architectural agent-oriented model for
CSOs.
- The CyberPhysical Environment, which refers to the - The JADE-based agent middleware, which provides
non-agent-oriented logical and physical context an implementation of the high-level CSO
(made up of logical and physical components) in architecture through JADE and JADEX atop
which agents are embedded. It can be modeled in different computing devices (PCs and mobile
terms of a reactive/proactive environment devices).
abstraction [16] that is able to interact with agents - The WSAN programming and management, which is
according to a specific coordination model. based on the Building Management Framework
- The Cloud Computing Platform, which supports all (BMF1) [18], for the management of the wireless
smart agents, empowering their specific resources, sensor and actuator network (WSAN) of smart
and allows for the definition of new (virtual) smart space-oriented CSOs, and on the Signal In-Node
object agents as meta-aggregation of existing Processing Environment (SPINE2) [19, 20], for the
smart object agents. management of the body area network (BAN) of
body/personal oriented CSOs.
1
http://bmf.deis.unical.it
2
http://spine.deis.unical.it
manage communication with external networked
entities based on TCP and UDP, respectively.
- The Device Management Subsystem, which manages
the sensing/actuation devices that belong to the
CSO. It is organized in a DeviceManager handling
several DeviceAdapters. Currently, two
DeviceAdapters are available: the BMFAdapter,
which allows to manage WSANs based on BMF,
and the SPINEAdapter, which allows to manage
BANs based on SPINE. BMF and SPINE are
based on IoT standards protocols such as IEEE
802.15.4, ZigBee, and 6LowPan.
- The KB Management Subsystem, which supports
CSOs through a knowledge base (KB). It consists
of a KBManager, which manages and coordinates
different KBAdapters, and a KBAdapter, which
manages a KB containing the knowledge of the
Figure 3. Agent-based Platform for Smart Object Development
CSO. KB can be local and/or remote and archives
Figure 4 shows the JADE-based CSO architecture. CSOs information that can be shared among tasks.
are agents of the JADE platform (either simple JADE agents or
encapsulated JADEX agents). They are therefore managed by
the AMS (Agent Management System), communicate through
the ACL-based message transport system, and use the
Directory Facilitator (DF) to look up CSOs and other agents.
The DF, which supports dynamic agent service discovery, was
purposely modified/extended in ACOSO to allow searching
CSOs on the basis of their specific metadata: type, offered
services, location, static hw properties, dynamic system
properties. Currently, the extended JADE DF receives requests
from the JADE-based CSOs and fulfills them by using the
remote interface of an external CSO discovery service [17].
The main CSO architecture components are:
- The Task Management Subsystem, which manages the
reactive and proactive tasks of CSOs. In particular,
tasks are event-driven and state-based software
components encapsulating specific objectives to
fulfill through computation, communication,
sensing/actuation, and storage management
operations. Tasks can be defined as JADE
Behaviours or JADEX Plans so their execution is
based on the mechanisms provided by the basic
JADE behavioral execution model or plan-
oriented Jadex execution model, respectively.
Being tasks driven by events, external CSO
Figure 4. JADE-based CSO Architecture
communication, signals to/from the CSO devices,
data to/from the knowledge base (KB) are Moreover, to support small-size CSOs running on a single
internally formalized and managed as events. sensor node, the Mobile Agent Platform for SunSPOT
- The Communication Management Subsystem, which (MAPS3) framework [21] can be adopted to implement the
provides a common interface for CSO CSO architecture. It is worth noting that MAPS agents can be
communications. In particular, message-based wrapped by JADE agents through the JADE-MAPS gateway
communication is based on the ACL-based [22] so as to interact with JADE-based CSO agents.
MessagingService whereas publish/subscribe
coordination is the TopicManagementService. The B. BodyCloud: an architecture for Cloud-assisted body area
subsystem is internally organized in handlers. The networks
CommunicationManagerMessageHandler, which
is implemented as Behaviour in JADE and as Plan BodyCloud [23, 24, 25] is a SaaS-oriented architecture for
in Jadex, captures the ACL messages targeting the integration of BANs and a Cloud PaaS infrastructure. Its
CSOs and translates them into internal events.
Moreover, the TCPAdapter and UDPAdapter 3
http://maps.deis.unical.it
architecture, shown in Figure 5, consists of four main
subsystems (or sides):
- The Body-side manages the BAN and sends the
collected sensor data to the Cloud-side through an
Android-enabled mobile device. In particular, data
acquisition is currently based on Android-SPINE,
the Android version of SPINE. It allows Android-
enabled smartphones and tablets to be used as
coordinator of the BAN. In particular, the
coordinator communicates with the wearable
sensors by means of the SPINE application-level
protocol atop Bluetooth and supports sensor
discovery, sensor configuration, in-node
processing, BSN activation/deactivation, data
collection, and logging.
- The Cloud-side provides data collection and storage,
processing/analysis and visualization. Each
specific application can be defined through four
programming abstractions: Group, Modality,
Workflow/Node, and View. Group formalizes a
specific application manipulating a well-defined
BAN data source. Modality captures a specific
interaction Body-Cloud sides and Viewer-Cloud
sides. In particular, it models a specific service,
such as data feeds to the Cloud-side, data analysis Figure 5. The architecture of BodyCloud
tasks, single-user or community applications.
Workflow formalizes a data-flow process
C. Integration requirements
analyzing input data to generate output data. It
consists of one or more nodes organized in a To support the CA-IoT architecture introduced in Sect. III
directed acyclic graph. Node is a specific and thus develop large-scale IoT systems, ACOSO and
algorithm that can be developed according to the BodyCloud are being jointly extended and integrated according
Workflow Engine library (see Analyst-side in Fig. to the following main requirements:
5). A Node is uploaded to the Cloud-side where it - Smart agent enhancement. While the basic smart
can be used in different workflows. Finally, View agent layer is fully supported by ACOSO, the
defines the visualization layout of the output data Cloud platform needs to provide a new
for Viewers. Such abstractions are supported by functionality to define Cloud-based smart agents
the developed RESTLet-based SaaS Framework, to dynamically create new virtual smart interface
which makes the interaction with the Cloud-side and object agents than run on the Cloud-side and
fully based on HTTP get, put, post, delete seamlessly interact with the basic ACOSO agents.
methods. The Cloud-side is supported by the
- Smart object data stream collection and management.
Google App Engine PaaS 4 that enables data
Data streams coming from highly decentralized
persistence and task execution.
smart objects needs to be efficiently uploaded onto
- The Analyst-side supports the development of new the Cloud-side and here effectively managed.
application services. In particular, developers can
- Workflow-oriented analysis of smart object data.
create new BodyCloud services by defining the
Decision making applications should be
aforementioned abstractions. To program
dynamically developed through distributed
workflows, the Analyst-side is based on an
workflows defined at the Cloud-side involving
appropriate development environment (XML
smart agents and cloud services.
Editor and Workflow Engine API).
- Effective multi-level security architecture for smart
- The Viewer-side visualizes the output produced by the
object data collection (from smart objects to the
data analysis through advanced graphical reports.
Cloud-side) and data analysis services (at Cloud-
By applying a View abstraction (see above) to the
data, the graphical view is automatically side).
generated. The current prototype is based on a
Java library named jxReport that has been V. CONCLUSIONS AND FUTURE WORK
purposely implemented and integrated into the In this paper, we have proposed a novel high-level
client application. architecture for smart object-oriented IoT based on Cloud and
Agents. Through the exploitation of agent-based computing,
4
the proactiveness and cooperation capabilities of smart objects
https://cloud.google.com/products/
could be effectively defined, whereas by means of cloud [9] Christos Goumopoulos and Achilles Kameas, “Smart Objects as
computing, smart objects could be empowered in terms of Components of UbiComp Applications,” International Journal of Multimedia
and Ubiquitous Engineering, Vol. 4, No. 3, July, 2009.
processing power and storage resources. Thus, cloud-assisted
[10] F. Kawsar, T. Nakajima, J. H. Park, and S. S. Yeo, "Design and
and agent-oriented smart objects could be used as basis for the implementation of a framework for building distributed smart object systems,"
development of large-scale IoT applications and systems. The Journal of Supercomputing, vol. 54, no. 1, pp. 4-28, Oct. 2010.
proposed architecture can be actually implemented by adopting [11] M. Miche, D. Schreiber, D., and M. Hartmann, “Core Services for Smart
and integrating an effective smart object middleware and an Products,” In: AmI-Blocks'09, at AmI'09, 2009.
efficient cloud platform. To this purpose, we briefly introduced [12] G. Fortino, A. Guerrieri, and W. Russo. Agent-oriented smart objects
ACOSO, an agent-oriented middleware for the programming development.In Proc. of IEEE 16th Int. Conference on Computer Supported
and management of cooperative smart objects, and BodyCloud, Cooperative Work in Design (CSCWD), pp. 907--912, 2012.
an architecture for the integration of sensors on the Cloud. [13] G. Fortino, A. Guerrieri, M. Lacopo, M. Lucia, and W. Russo, "An
They are currently being integrated according to specific Agent-based Middleware for Cooperating Smart Objects", in Highlights on
Practical Applications of Agents and Multi-Agent Systems, Communications
defined requirements to achieve a large-scale distributed in Comp. and Inform. Science (CCIS), Vol. 365, pp. 387-398, Springer, 2013.
platform for smart object-based IoT development. Finally, [14] F. Bellifemine, A. Poggi, and G. Rimassa, “Developing multi agent
future work will be aimed at defining a novel methodology for systems with a FIPA-compliant agent framework,”. Software Practice And
the development of IoT applications. Such methodology will be Experience 31, 103-128, 2001.
obtained as extension of ELDAMeth [26, 27], a methodology [15] A. Pokahr, L. Braubach, W. Lamersdorf, “Jadex: A BDI Reasoning
for simulation-based prototyping of distributed agent systems. Engine,” In Multi-Agent Programming: Languages, Platforms and
Applications. Multiagent Systems, Artificial Societies, and Simulated
Organizations, Vol. 15, Springer, pp. 149-174, 2005.
ACKNOWLEDGMENT [16] D. Weyns, A. Omicini, and J. Odell, Environment as a first-class
Authors wish to thank Antonio Guerrieri and Michele abstraction in multiagent systems, International Journal on Autonomous
Agents and Multi-Agent Systems 14 (1), pp. 5-30, 2007.
Lacopo for their valuable efforts and contribution in the
[17] G. Fortino, M. Lakovic, W. Russo, P. Trunfio, “A discovery service for
development of the smart object agent-oriented middleware smart objects over an agent-based middleware”, IDCS 2013, Lecture Notes in
and Giuseppe Di Fatta (University of Reading) for his valuable Computer Science (LNCS), Vol. 8823, pp. 1-14, 2013.
insights for the BodyCloud approach definition. This work has [18] G. Fortino, A. Guerrieri, G. O’Hare, A. Ruzzelli, “A Flexible Building
been partially supported by TETRis – TETRA Innovative Open Management Framework based on Wireless Sensor and Actuator Networks”,
Source Services, funded by the Italian Government (PON 01- Journal of Network and Computer Applications, 35(6), pp. 1934-1952, 2012.
00451). Giancarlo Fortino is also partially funded by DICET – [19] F. Bellifemine, G. Fortino, R. Giannantonio, R. Gravina, A. Guerrieri, M.
INMOTO – Organization of Cultural Heritage for Smart Sgroi, “SPINE: A domain-specific framework for rapid prototyping of WBSN
Tourism and REal – Time Accessibility (OR.C.HE.S.T.R.A.) applications” Software Practice and Experience, 41(3), 2011, pp. 237-265.
funded by the Italian Government (PON04a2_D). [20] F. Bellifemine, F. Aiello, G. Fortino, S. Galzarano, R. Gravina, “An
agent-based signal processing in-node environment for real-time human
activity monitoring based on wireless body sensor networks”. Journal of
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