Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems. Claudio Savaglio

Ph.D. Program in ‘‘Information and Communication Technology’’XXX Cicle
A Methodology for the Development
of Autonomic and Cognitive
Internet of Things Ecosystems
Coordinator
Prof. Felice CRUPI
Advisor
Prof. Giancarlo FORTINO
Candidate
Claudio SAVAGLIO

Outline
• Research Context
• Main Contributions
1. a Comparison Framework for supporting the state-of-the-art analysis of
Internet of Things (IoT) middleware, architectures and platforms.
2. the Agent-based COoperating Smart Objects Methodology
(ACOSO-Meth) for developing autonomic and cognitive IoT Ecosystems.
3. a full-fledged modelling approach for Opportunistic IoT Services.
• Conclusion, On-going and Future work
208/06/2018 Ph. D. Candidate: Claudio Savaglio

Research Context
Ecosystem: a set of, eventually heterogeneous,
communities, which comprise both biotic and
abiotic components interacting with each others
and with the surrounding environment.
IoT Ecosystem: a set of different systems, which
in their turn integrate notably heterogeneous but
interacting cyber-physical entities.
08/06/2018 Ph. D. Candidate: Claudio Savaglio 3
SMART CITY
(IoT Ecosystem)

Research Context
Focus on pervasive and
reliable connectivity
Focus on
data
Focus on cyber-physical
devices and their services
Within IoT Ecosystems Smart Objects (SOs) are fundamental building blocks.
SOs are everyday object augment with sensing, actuation, communication and processing units
to provide cyber-physical services.
IoT

Research Context
IoT possesses an enormous potential but it also brings challenging and multi-facet development issues.
Methodologies are gaining consensus to support the systemic development of IoT Ecosystems.

1) Comparison Framework
The proposed framework relies on a twofold comparison criteria related to the:
• Provided support to the canonical Development Phases of analysis, design and implementation.
• Fulfilment of certain Development Requirements identified through a deep analysis of the
state-of-the-art for addressing the most important features (according to the Thing-oriented IoT
vision) of IoT Ecosystems.

vision) of IoT Ecosystems.
• System-level Requirements (SLRs) related to a whole IoT system.
• _
SLRs
SLR1: Hardware Devices Virtualization
SLR2: CommunicationAbstractions
SLR3: Software Interfaces
SLR4: Self- and Context-Awareness
SLR5: Data Abstraction
SLR6: Support of development processes
SLR7: System Scale Characterization

vision) of IoT Ecosystems
• System-level Requirements (SLRs) related to a whole IoT system.
• Thing-level Requirements (TLRs) particularly related to the “things” in an IoT system.
SLRs TLRs
SLR1: Hardware Devices Virtualization TLR1: Interoperability despite Heterogeneity
SLR2: CommunicationAbstraction TLR2: Augmentation Variation
SLR3: Software Interfaces TLR3: Self Management
SLR4: Self- and Context-Awareness TLR4: Dynamic Evolution
SLR5: Data Abstraction TLR5: Things Scale Characterization
SLR6: Support of development processes
SLR7: System Scale Characterization

1) Comparison FrameworkTABLE 1 – THE COMPARISON FRAMEWORK
Related work’s fulfilment of development requirements and support to the development phases (Y=total, P=partial, blank= null)

Fortino, G., Russo, W., Savaglio, C., Shen, W., and Zhou, M. Agent-Oriented Cooperative Smart Objects: from
IoT System Design to Implementation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018
REPORTED IN

The exploitation of the proposed comparison framework:
1) enabled the systematic survey of related work
2) allows comparing future works in the field
3) highlighted common practices and trends, as well as lacks and limitations

The exploitation of the comparison framework:
a) the lack of an approach to the IoT Ecosystems development fully covering the three
phases nor addressing all the outlined requirements;
b) the suitability of the Agent-based Computing (ABC) paradigm for developing autonomic
and cognitive IoT Ecosystems.
Agent-based Computing Autonomic properties Cognitive loop

The exploitation of the comparison framework:
a) the lack of an approach to the IoT Ecosystems development fully covering the three
phases nor addressing all the outlined requirements; and
b) the suitability of the Agent-based Computer (ABC) paradigm for developing autonomic
and cognitive IoT Ecosystems.
Agent-based Computing Autonomic properties Cognitive loop
Fortino, G., Savaglio, C., Ghanza, M., Paprzycki, M., Badica C., and Ivanovic, M. Agent-based computing in the Internet of
Things: a survey. Int.l Symposium on Intelligent and Distributed Computing, Springer, Oct 2017.
Savaglio C., and Fortino, G. Autonomic and Cognitive Architectures for the Internet of Things. Int.l Conf. on Internet and
Distributed Computing Systems, Eds. Springer Int.l Publishing 2015.
Savaglio, C., Fortino, G., and Zhou, M. Towards interoperable, cognitive and autonomic IoT systems: An agent-based approach.
Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on, Dec 2016.

2) ACOSO-Meth (Agent-based COoperating Smart Objects Methodology)
The outcomes of the state-of-the-art analysis obtained through the comparison framework
exploitation have driven the outline of ACOSO-Meth as:
• a metamodel-based, application domain-neutral and agent-oriented development
methodology to fulfill all the identified development requirements;
• the first full-fledged methodology that seamlessly supports the whole IoT Ecosystems’
development process of
• Analysis,
• Design and simulation-based design,
• Implementation.
Relationships among ACOSO-Meth metamodels at different phases

The outcomes of the state-of-the-art analysis obtained through the comparison framework
exploitation have driven the outline of ACOSO-Meth as:
• a metamodel-based, application domain-neutral and agent-oriented development
methodology to fulfill all the identified development requirements;
• the first full-fledged methodology that seamlessly supports the whole IoT Ecosystems’
development process of
• Analysis,
• Design and simulation-based design,
• Implementation.
Relationships among ACOSO-Meth metamodels at different phases
Fortino, G., Russo, W., Savaglio, C., Shen, W., and Zhou, M. Agent-Oriented
Cooperative Smart Objects: from IoT System Design to Implementation.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018
Fortino, G., Guerrieri, A., Russo, W., and Savaglio, C. Towards a development
methodology for smart object-oriented IoT systems: A metamodel approach.
Systems, Man, and Cybernetics (SMC), 2015 IEEE Int.l Conf. on, Oct 2015.

Analysis Phase: identifying the main entities of the IoT Ecosystem and abstracting their basic
features and high-level interactions.
Analysis Phase: High-Level SO Metamodel

Analysis Phase: identifying the main entities of the IoT Ecosystem and abstracting their basic
Adopting to the Thing-Oriented
IoT vision, the High-Level SO
Metamodel describes
(aggregated, e.g., a Smart Home)
SOs of every domain and
disregards any SO’s behavioral
element.
Location -> geographical SO
position
FingerPrint -> distinctive SO
information (ID, Creator, etc.)
Device -> SO sensing,
actuation and processing units
Service -> provided SO
service and its composing
operations
Status -> current SO working
status
Physical Properties -> SO
dimension, weight, etc.
User -> SO user

Analysis Phase: identifying the main entities of the IoT Ecosytem and abstracting their basic
High-Level SO Metamodel results compliant with well
known standards and initiatives.
TABLE 2 - COMPARISON OF MAIN ENTITIES OF ACOSO-METH, IEEE
P2413, AIOTI AND IOT-A SOs METAMODELS

Design Phase: technology-agnostic modelling the functional components of the IoT Ecosystem,
their specific relationships and interactions.
Design Phase: ACOSO-based SO Metamodel

SO is ‘‘agentified’’
and its operations
incapsulated in
(system/user defined)
tasks driven by
different types of
events according to
their nature.
ACOSO SO is based
on the ACOSO
middleware
acoso.dimes.unical.it

Each ACOSO SO’s subsystem
is associated to an SO’s
functional component.

Simulation-based Design Phase: the event-driven IoT Ecosystem designed by ACOSO-Meth has
been mapped on the event-based OMNeT++ network simulator to inspect physical issues related
to the networking such as wireless interferences, message congestions, coverage issues etc.
Many design choices affecting the final configurations of under development IoT Ecosystems
can be taken as a result of simulations.

Simulation-based Design Phase: the event-driven IoT Ecosystem designed by ACOSO-Meth has
been mapped on the event-based OMNeT++ network simulator to inspect physical issues related
to the networking such as wireless interferences, message congestions, coverage issues etc.
Many design choices affecting the final configurations of under development IoT Ecosystems
can be taken as a result of simulations.
Fortino, G., Gravina, R., Russo, W., and Savaglio, C. Modeling and Simulating Internet of Things
Systems: A Hybrid Agent-Oriented Approach. Computing in Science & Engineering, 2017.
Fortino, G., Russo, W., and Savaglio C. Agent-oriented modeling and simulation of IoT networks.
Federated Conf. on Computer Science and Information Systems (FedCSIS), IEEE, Sept 2016.

Implementation Phase: actually realizing the designed IoT Ecosystem by means of specific
programming paradigms, adopting well-established specifications and development tools.
Implementation phase: JACOSO SO Metamodel

JACOSO=Jade-based
ACOSO SO
Aiming at
interoperability,
JACOSO SO
relies on two
sets of software
adapters and the
standard Agent
Communication
Language
(ACL).

Hot-spot components (e.g.,
UserDefinedTask) need to
be customized according to
the specific application,
while frozen-spots (e.g., all
the Managers) can be
directly re-used.
*Hot-spot
°Frozen-spot
°
° °
°
° **
*
* *

Interesting case studies, related to different application scenarios, have been realized following the
ACOSO-Meth development approach.
• Cyber-physical Digital Libraries (DLs): the High-Level SO Metamodel of Analysis Phase
supported the SOs inclusion into DLs as novel first-class objects to be collected, managed, and
preserved (beside conventional multimedia contents).
• Smart Unical:
Fortino, G., Rovella, A., Russo, W., and Savaglio, C.
Towards Cyberphysical Digital Libraries: Integrating
IoT Smart Objects into Digital Libraries. Management
of Cyber Physical Objects in the Future Internet of
Things, Springer Int.l Publishing. 2015.

preserved (beside conventional multimedia contents)..
• Smart Unical:
a complex IoT
Ecosystem (providing
cyber-physical
services related to
structural, indoor
space and wellness
monitoring)
effectively supported
by ACOSO-Meth
from the analysis to
the implementation
phase.

preserved.
• Smart Unical:
a complex IoT
Ecosystem (providing
cyber-physical
services related to
structural, indoor
space and wellness
monitoring)
effectively supported
by ACOSO-Meth
from the analysis to
the implementation
phase.

The Smart Unical has been evaluated through preliminary simulation tests to define the best
scenario and SOs configuration.
a
Simulation results provided important insights for driving the Smart Unical final deployment.
Packet Delivery Ratio (PDR) and Round Trip Time (RTT) when the number of SOs changes

TABLE 3 - OPERATION MODALITIES OF THE SMART UNICAL SOs TABLE 4 - SMART UNICAL PERFORMANCE EVALUATION
Simulation results drove and facilitated the deployment of Smart Unical, which has been then
experimentally evaluated with respect to responsiveness, reliability and required resources.

3) Opportunistic IoT Services
Current ‘‘Intra-net of Things’’ provide conventional computing services mainly designed for static
environments with a-priori interactions.
Future IoT will be dense and fully realised, in which the key drivers will be IoT services featured
by the following opportunistic features:
Ph. D. Candidate: Claudio Savaglio 3308/06/2018

Opportunistic IoT Service: an interface that allows an IoT Entity to be engaged, under specific constraints and
pre/postconditions, in a temporary, contextualised and localised usage relationship. The service provision impacts the
involved IoT Entities/the service provider(s), service consumer(s), and, in some case, third parties indirectly related to
the service provisioning and the IoT Environment, by modifying their properties and/or their status.
1. Dynamicity, IoT services can be dynamically, and not a-priori, created/activated;
2. Context-awareness, any implicit/explicit information about the current location, identity, activity, and physical
condition of the involved IoT entities should be considered;
3. Co-location, IoT services are created for being simultaneously exploited by different IoT entities sharing the same
(cyber-physical) resources in the same location;
4. Transience, IoT services can last for a temporary time or till certain conditions are met.

Opportunistic IoT Service: an interface that allows an IoT Entity to be engaged, under specific constraints and
pre/postconditions, in a temporary, contextualised and localised usage relationship. The service provision impacts the
involved IoT Entities/the service provider(s), service consumer(s), and, in some case, third parties indirectly related to
the service provisioning and the IoT Environment, by modifying their properties and/or their status.
1. Dynamicity, IoT services can be dynamically, and not a-priori, created/activated;
2. Context-awareness, any implicit/explicit information about the current location, identity, activity, and physical
condition of the involved IoT entities should be considered;
3. Co-location, IoT services are created for being simultaneously exploited by different IoT entities sharing the same
(cyber-physical) resources in the same location;
4. Transience, IoT services can last for a temporary time or till certain conditions are met.
Fortino, G., Savaglio, C., Zhou, M. Opportunistic Cyberphysical Services: A Novel Paradigm for the Future Internet of Things.
The 4th IEEE World Forum on the Internet of Things (WF-IoT 2018), Feb 2018.

Descriptive
IoT Service
metamodel
Operational
IoT Service
model
Opportunistic
IoT Service
Service analysis
Service verification
Service programming
Service simulation
Goal:
Goals:
Two different but complementary models fully support the modelling of Opportunistic IoT
Services, a novel paradigm of context-aware, co-located, dynamic and transient services.

Descriptive
IoT Service
metamodel
Operational
IoT Service
model
Opportunistic
IoT Service
Service analysis
Service verification
Service programming
Service simulation
Goal:
Goals:
Fortino, G., Savaglio, C., Zhou, M. Opportunistic Cyberphysical Services: A Novel Paradigm for the Future Internet of
Things. The 4th IEEE World Forum on the Internet of Things (WF-IoT 2018), Feb 2018.
Two different but complementary models fully support the modelling of Opportunistic IoT
Services, a novel paradigm of context-aware, co-located, dynamic and transient services.

The SO High-level Metamodel of the Analysis phase has been purposely extended (in red) to
describe what an Opportunistic IoT Service does (Service Profile) and how it works (Service Model).
Both Service Profile and Service Model are compliant with the web service-oriented OWL-S: Semantic Markup for
Web Services - W3C standard, but also accomodate the cyber-physical and opportunistic features of IoT services.
Descriptive IoT Service metamodel

Information contained in both Service Profile/Service Model can be exploited to formally describe
an Opportunisic IoT Service through a Finite State Machine (FSM), so enabling its verification.
IoT service S, IoT Entitiy E, IoT Environment Env
Since interactions enabling IoT Services are typically asynchronous, event-driven and time-dependent, IoT
Ecosystems may be formally modeled as Discrete Event Systems (DESs).
Operational FSM-based IoT Service model

Some contributions related to Opportunisti IoT Services have been provided within the H2020 INTER-IoT
European Project (e.g., Opportunistic multi-technology and multi-standard IoT gateway)
The effectiveness and flexibility of the approach is illustrated by means of two case studies, related
to Opportunistic IoT services in the Smart City and Industrial IoT scenarios.
http://www.inter-iot-project.eu/
(a) Crowd Service (b) Connectivity Service (c) Smartphone-based IoT Gateway

Some contributions related to Opportunisti IoT Services have been provided within the H2020 INTER-IoT
European Project (e.g., Opportunistic multi-technology and multi-standard IoT gateway)
http://www.inter-iot-project.eu/
(a) Crowd Service (b) Connectivity Service (c) Smartphone-based IoT Gateway
Casadei R., Fortino G., Pianini D., Russo W., Savaglio C., Viroli M. Modelling and Simulation of Opportunistic IoT Services with
Aggregate Computing. Future Generation Computer Systems (accepted with minor revisions).
Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., and Savaglio, C. Enabling IoT interoperability through opportunistic
smartphone-based mobile gateways. Journal of Network and Computer Applications, 2017.
Fortino, G., Savaglio, C., Palau, C. E., de Puga, J. S., Ghanza, M., Paprzycki, M., Montesinos, M., Liotta, A., and Llop, M. Towards Multi-
layer Interoperability of Heterogeneous IoT Platforms: The INTER-IoTApproach. In Integration, Interconnection, and Interoperability
of IoT Systems, Springer, Cham. 2018.
The effectiveness and flexibility of the approach is illustrated by means of two case studies, related
to Opportunistic IoT services in the Smart City and Industrial IoT scenarios.

Conclusions, on-going and future work
Conclusions:
• The complexity featuring IoT Ecosystems claims for proper and full-fledged development
methodologies.
• ACOSO-Meth is the first metamodel-based, application-neutral, agent-based methodology
able to support the main engineering phases of IoT Ecosystems.
• ACOSO-Meth has been (i) inspired by a framework-based analysis of the state-of-the-art
of IoT platforms/architectures/middlewares, (ii) exploited to support the development of
heterogeneous case studies, and (iii) extended to support novel Opportunistic IoT services.
On-going work:
• Support Opportunistic IoT Services’ programming and simulation through the Aggregate
Computing paradigm.
Future work:
• Support Opportunistic IoT Services’ formal verification through proper formalisms.

Publications related with this Thesis
4 Journal articles (with ISI impact factor);
2 Book chapters (Springer);
10 Conference papers (mostly IEEE and Springer).

• Journal articles:
1. Fortino, G., Gravina, R., Russo, W., and Savaglio, C. Modeling and Simulating Internet of
Things Systems: A Hybrid Agent-Oriented Approach. In Computing in Science &
Engineering, 19(5):68-76. 2017.
2. Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., and Savaglio, C.
Enabling IoT interoperability through opportunistic smartphone-based mobile gateways.
In Journal of Network and Computer Applications, 81:74-84. 2017.
3. Fortino, G., Russo, W., Savaglio, C., Shen, W., and Zhou, M. Agent-Oriented Cooperative
Smart Objects: from IoT System Design to Implementation. In IEEE Transactions on
Systems, Man, and Cybernetics: Systems, 1-18. doi:10.1109/TSMC.2017.2780618. 2018.
4. Casadei R., Fortino G., Pianini D., Russo W., Savaglio C., Viroli M. Modelling and
Simulation of Opportunistic IoT Services with Aggregate Computing. In Future
Generation Computer Systems (accepted with minor revision required).

• Book chapters:
1. Fortino, G., Savaglio, C., Palau, C. E., de Puga, J. S., Ghanza, M., Paprzycki, M.,
Montesinos, M., Liotta, A., and Llop, M. Towards Multi-layer Interoperability of
Heterogeneous IoT Platforms: The INTER-IoT Approach. In Integration, Interconnection,
and Interoperability of IoT Systems, 199-232, Springer, Cham. 2018.
2. Fortino, G., Rovella, A., Russo, W., and Savaglio, C. Towards Cyberphysical Digital
Libraries: Integrating IoT Smart Objects into Digital Libraries. In Management of Cyber
Physical Objects in the Future Internet of Things, 135-156. Springer International
Publishing. 2015.

• Conference papers:
1. Fortino, G., Savaglio, C., Zhou, M. Opportunistic Cyberphysical Services: A Novel Paradigm for the Future
Internet of Things. The 4th IEEE World Forum on the Internet of Things (WF-IoT 2018), February 2018.
2. Fortino, G., Savaglio, C., Ghanza, M., Paprzycki, M., Badica C., and Ivanovic, M. Agent-based computing
in the Internet of Things: a survey. International Symposium on Intelligent and Distributed Computing, 307-
320. Springer, Cham. October 2017.
3. Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., and Savaglio, C. A mobile multi-
technology gateway to enable IoT interoperability. Internet-of-Things Design and Implementation (IoTDI),
2016 IEEE First International Conference on, 259-264. IEEE. 2016.
4. Fortino, G., Savaglio, C., Zhou, M. Modeling Opportunistic IoT Services in Open IoT Ecosystems. Proc.
18th Workshop Objects to Agents (WOA17), 90-95. July 2017.
5. Fortino, G., Savaglio, C., Zhou, M. Toward Opportunistic Services for the Industrial Internet of Things.
Proceedings of 13th IEEE Conference on Automation Science and Engineering (CASE), 825-830. IEEE.
August 2017.

• Conference papers:
6. Savaglio, C., Fortino, G., and Zhou, M. Towards interoperable, cognitive and autonomic IoT systems: An
agent-based approach. Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on, 58-63. IEEE.
December 2016.
7. Fortino, G., Russo, W., and Savaglio, C. Simulation of Agent-Oriented Internet of Things Systems. Proc.
17th Workshop Objects to Agents (WOA16), 8-13. 2016.
8. Fortino, G., Russo, W., and Savaglio C. Agent-oriented modeling and simulation of IoT networks. Federated
Conference on Computer Science and Information Systems (FedCSIS), 90-95. IEEE. September 2016.
9. Savaglio C., and Fortino, G. Autonomic and Cognitive Architectures for the Internet of Things.
International Conference on Internet and Distributed Computing Systems, 9258: 39-47. G. Di Fatta, G.
Fortino, W. Li, M. Pathan, F. Stahl, and A. Guerrieri, Eds. Springer International Publishing 2015.
10. Fortino, G., Guerrieri, A., Russo, W., and Savaglio, C. Towards a development methodology for smart
object-oriented IoT systems: A metamodel approach. Systems, Man, and Cybernetics (SMC), 2015 IEEE
International Conference on, 1297-1302. IEEE. October 2015.

Ph.D. Thesis: ‘‘A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems’’
Thank you for your attention.
Any questions?
Coordinator
Prof. Felice CRUPI
Advisor
Prof. Giancarlo FORTINO
Candidate
Claudio SAVAGLIO

Backup slides

we classify IoT systems and SOs in small-medium-large scale on the basis of their physical dimension and density

[66] Alessandro Bassi, Martin Bauer, Martin Fiedler, Thorsten Kramp, Rob Van Kranenburg, Sebastian Lange, and Stefan Meissner. Enabling things to talk. Springer, 2016.
[70] Christos Goumopoulos and Achilles Kameas. Smart objects as components of ubicomp applications. Int.l Journal of Multimedia and Ubiquitous Engineering, 4(3):1-20.
[71] Patricia Derler, Edward A Lee, and Alberto Sangiovanni Vincentelli. Modeling cyber-physical systems. Proceedings of the IEEE, 100(1):13-28, 2012.
[73] Michele Ruta, Floriano Scioscia, Giuseppe Loseto, and Eugenio Di Sciascio. Semantic-based resource discovery and orchestration in home and building automation: A multi-agent approach.
IEEE Trans on Industrial Informatics, 10(1):730-741, 2014.
[74] Xiangyu Zhang, Rajendra Adhikari, Manisa Pipattanasomporn, Murat Kuzlu, and Saifur Rahman Bradley. Deploying iot devices to make buildings smart: Performance evaluation and
deployment experience. In Internet of Things (WF-IoT), 2016 IEEE 3rd World Forum on, pages 530-535. IEEE, 2016.
[76] Artem Katasonov, Olena Kaykova, Oleksiy Khriyenko, Sergiy Nikitin, and Vagan Y Terziyan. Smart semantic middleware for the internet of things. ICINCO-ICSO, 8:169-178, 2008.
[77] Vagan Terziyan, Olena Kaykova, and Dmytro Zhovtobryukh. Ubiroad: Semantic middleware for context-aware smart road environments. In Internet and web applications and services (iciw),
2010 fifth international conference on, pages 295-302. IEEE, 2010.
[78] Panagiotis Vlacheas, Raffaele Giaffreda, Vera Stavroulaki, Dimitris Kelaidonis, Vassilis Foteinos, George Poulios, Panagiotis Demestichas, Andrey Somov, Abdur Rahim Biswas, and Klaus
Moessner. Enabling smart cities through a cognitive management framework for the internet of things. IEEE communications magazine, 51(6):102-111, 2013.
[83] Luciano Baresi, Antonio Di Ferdinando, Antonio Manzalini, and Franco Zambonelli. The cascadas framework for autonomic communications. Autonomic Communication, p. 147-168, 2009.
[85] Stamatis Karnouskos and Thiago Nass De Holanda. Simulation of a smart grid city with software agents. In Computer Modeling and Simulation, 2009. EMS’09. Third UKSim European
Symposium on, pages 424-429. IEEE, 2009.
[86] Luca Costantino, Novella Buonaccorsi, Claudio Cicconetti, and Raffaella Mambrini. Performance analysis of an lte gateway for the iot. In World of Wireless, Mobile and Multimedia
Networks (WoWMoM), 2012 IEEE International Symposium on a, pages 1-6. IEEE, 2012.
[87] Gabriele DAngelo, Stefano Ferretti, and Vittorio Ghini. Multi-level simulation of internet of things on smart territories. Simulation Modelling Practice and Theory, 2016.
[90] Sarfraz Alam, Mohammad MR Chowdhury, and Josef Noll. Senaas: An eventdriven sensor virtualization approach for internet of things cloud. In Networked Embedded Systems for
Enterprise Applications (NESEA), 2010 IEEE International Conference on, pages 1-6. IEEE, 2010.
[91] Teemu Lepp¨anen, Jukka Riekki, Meirong Liu, Erkki Harjula, and Timo Ojala. Mobile agents-based smart objects for the internet of things. In Internet of Things Based on Smart Objects,
pages 29-48. Springer, 2014.
[94] Franco Cicirelli, Giancarlo Fortino, Andrea Giordano, Antonio Guerrieri, Giandomenico Spezzano, and Andrea Vinci. On the design of smart homes: A framework for activity recognition in
home environment. Journal of medical systems, 40(9):200, 2016.
[95] Dirk Slama, Frank Puhlmann, Jim Morrish, and Rishi Bhatnagar. Enterprise internet of things, 2015
[96] Tom Collins. A methodology for building the internet of things.
[97] Franco Zambonelli. Towards a general software engineering methodology for the internet of things. arXiv preprint arXiv:1601.05569, 2016.
[98] Nikolaos Spanoudakis and Pavlos Moraitis. Engineering ambient intelligence systems using agent technology. IEEE Intelligent Systems, 30(3):60-67, 2015.
[99] Bogdan Manate, Florin Fortis, and Philip Moore. Applying the prometheus methodology for an internet of things architecture. In Proceedings of the 2014 IEEE/ACM 7th International
Conference on Utility and Cloud Computing, pages 435-442. IEEE Computer Society, 2014.
[101] Paolo Bresciani, Anna Perini, Paolo Giorgini, Fausto Giunchiglia, and John Mylopoulos. Tropos:An agent-oriented software development methodology. Autonomous Agents and Multi-
Agent Systems, 8(3):203-236, 2004.
[102] Inmaculada Ayala, Mercedes Amor, and Lidia Fuentes. The sol agent platform: Enabling group communication and interoperability of self-configuring agents in the internet of things.
Journal of Ambient Intelligence and Smart Environments, 7(2):243-269, 2015.

Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems. Claudio Savaglio

Recommended

Recommended

More Related Content

What's hot

What's hot (15)

Similar to Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems. Claudio Savaglio

Similar to Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems. Claudio Savaglio (20)

Recently uploaded

Recently uploaded (20)

Ph.D. Thesis: A Methodology for the Development of Autonomic and Cognitive Internet of Things Ecosystems. Claudio Savaglio