@conference {9097595, title = {A Novel Data Collection Framework for Telemetry and Anomaly Detection in Industrial IoT Systems}, booktitle = {2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)}, year = {2020}, pages = {245-251}, author = {F. De Vita and D. Bruneo and S. K. Das} } @conference {9066039, title = {A Mininet-Based Emulated Testbed for the I/Ocloud}, booktitle = {2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)}, year = {2019}, month = {Dec}, pages = {277-283}, abstract = {Considering the proliferation of smart devices connected to the Internet, typically going under the aegis of Internet of Things (IoT), a trend has arisen to promote the Cloud paradigm as a suitable management system for such a complex environment. In this context, an effort to extend the OpenStack ecosystem to make it able to support the management of the IoT infrastructure has been made by virtue of the I/Ocloud approach, leading up to its reference implementation, the Stack4Things (S4T) middleware. S4T provides a set of suitable capabilities and features to make the (remote) IoT devices able to join an edge-based IaaS/PaaS Cloud. In the interest of enhancing the S4T middleware scalability and explore new capabilities in particular, ones related to Fog and Edge paradigms, it is becoming a must to test new features in practice at a low financial cost and particular constraints for instance, number/type of devices, network conditions, etc. For this purpose, the use of network emulation tools is a practical and suitable approach. In this paper, we present an integration between the S4T middleware and an emulation tool namely Containernet. Through the integration approach, we model network conditions (e.g., latency, bandwidth, packet loss) and devices (in forms of containers) using Containernet, and we manage the devices (i.e., containers) by means of S4T.}, keywords = {aegis, cloud computing, Cloud paradigm, complex environment, Containernet, containers, Edge computing, Edge/Fog computing, Emulation, input-output programs, Internet of Things, IoT, IoT devices, IoT infrastructure, low financial cost, middleware, Mininet, mininet-based, model network conditions, network emulation tools, OpenStack, OpenStack ecosystem, reference implementation, S4T middleware scalability, smart devices, Stack4Things middleware, suitable management system, Task analysis, Tools, virtualisation}, doi = {10.1109/MSN48538.2019.00060}, author = {Zakaria Benomar and D. Bruneo and F. Longo and G. Merlino and A. Puliafito} } @conference {8784023, title = {Towards trustless prediction-as-a-service}, booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)}, year = {2019}, month = {June}, pages = {317-322}, keywords = {application program interfaces, Blockchain, cloud computing, cloud provider infrastructure, Computational modeling, Cryptography, Deep Learning, deep learning models, deep neural network models, inference model, inference service, infrastructure acquisition, Machine learning, malicious behaviors, neural nets, neural networks, operation costs, peculiar threat models, prediction API provider, prediction API providers, prediction APIs, prediction-as-a-service, Predictive models, Protocols, security of data, self-hosting costs, software-as-a-service business model, Tendermint}, issn = {null}, doi = {10.1109/SMARTCOMP.2019.00068}, author = {G. Santhosh and F. De Vita and D. Bruneo and F. Longo and A. Puliafito} } @conference {8784003, title = {On the Use of LSTM Networks for Predictive Maintenance in Smart Industries}, booktitle = {2019 IEEE International Conference on Smart Computing (SMARTCOMP)}, year = {2019}, month = {June}, pages = {241-248}, keywords = {Deep Learning, Engines, History, Industry 4.0, Keras, learning (artificial intelligence), Logic gates, LSTM networks hyperparameters, Machine learning, machine learning approach, machine learning techniques, maintenance engineering, maintenance scheduling, predictive maintenance, production engineering computing, recurrent neural nets, remaining life assessment, remaining useful life, RUL, Sensor systems, smart industries, Smart Industry, TensorFlow}, issn = {null}, doi = {10.1109/SMARTCOMP.2019.00059}, author = {D. Bruneo and F. De Vita} } @article {8928171, title = {Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing}, journal = {IEEE Communications Standards Magazine}, volume = {3}, number = {3}, year = {2019}, month = {Sep.}, pages = {71-78}, keywords = {5G mobile communication, Base stations, Computer architecture, Edge computing, Long Term Evolution, Quality of service, Reinforcement learning}, issn = {2471-2833}, doi = {10.1109/MCOMSTD.2019.1900011}, author = {F. De Vita and G. Nardini and A. Virdis and D. Bruneo and A. Puliafito and G. Stea} } @conference {8421336, title = {A Deep Learning Approach for Indoor User Localization in Smart Environments}, booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)}, year = {2018}, month = {June}, pages = {89-96}, keywords = {Buildings, cloud computing, Deep Learning, deep learning approach, Global Positioning System, Indoor environments, indoor localization, indoor navigation, indoor user localization, indoor user location, integral part, learning (artificial intelligence), Machine learning, radionavigation, smart environments, smart services, telecommunication computing, TensorFlow, user position, Wi-Fi fingerprint, Wireless communication, Wireless fidelity, wireless LAN}, doi = {10.1109/SMARTCOMP.2018.00078}, author = {F. De Vita and D. Bruneo} } @conference {8597889, title = {A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing}, booktitle = {2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K)}, year = {2018}, month = {Nov}, pages = {1-8}, keywords = {5G, 5G mobile communication, cloud computing, computational resources, data migration, Deep Reinforcement Learning, deep reinforcement learning approach, distributed services, Edge computing, Keras machine learning, Keras machine learning framework, learning (artificial intelligence), Long Term Evolution, LTE, MEC scenarios, Multi-access Edge Computing, multiaccess edge computing, network performance, OMNeT++/SimuLTE simulator, parameter settings, push data, Quality of service, Reinforcement learning, Resource allocation, resource utilization, Servers, SimuLTE}, doi = {10.23919/ITU-WT.2018.8597889}, author = {F. D. Vita and D. Bruneo and A. Puliafito and G. Nardini and A. Virdis and G. Stea} } @conference {8726749, title = {Extending Openstack for Cloud-Based Networking at the Edge}, booktitle = {2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)}, year = {2018}, month = {July}, pages = {162-169}, abstract = {Cloud-controlled virtual networking at the edge can be considered a critical feature, highly in demand in the IoT infrastructure management domain. Fostering the vision of the Cloud as a suitable control surface for IoT, an integration between the two ecosystems is achievable at a genuinely Infrastructure-as-a-Service level. This work thus describes an integration design between an IoT-centric infrastructure Cloud framework, already capable of limited networking functionality, and Neutron, the networking subsystem belonging to the core services of the OpenStack platform. Design considerations and trade-offs are detailed in the paper.}, keywords = {Bridges, Cloud, cloud computing, Cloud-controlled virtual networking, control surface, critical feature, Edge computing, IaaS, infrastructure management domain, Infrastructure-as-a-Service level, integration design, Internet of Things, IoT, IoT-centric infrastructure Cloud framework, Linux, network topology, network virtualization, networking functionality, networking subsystem, Neutron, Neutrons, OpenStack, OpenStack platform, Topology, Virtualization}, doi = {10.1109/Cybermatics_2018.2018.00058}, author = {Zakaria Benomar and D. Bruneo and S. Distefano and K. Elbaamrani and N. Idboufker and F. Longo and G. Merlino and A. Puliafito} } @article {275, title = {Energy control in dependable Wireless Sensor Networks: a modeling perspective}, journal = {PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS. PART O, JOURNAL OF RISK AND RELIABILITY}, volume = {225}, year = {2011}, pages = {424{\textendash}434}, abstract = {

Wireless sensor networks (WSN) are composed of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, battery-operated sensors undergo cycles of sleeping-active periods that reduce their ability to send/receive data. Starting from the Markov reward model theory, this paper presents a dependability model to analyse the reliability of a sensor node. Also, a new dependability parameter is introduced, referred to as producibility, which is able to capture the capability of a sensor to accomplish its mission. Two different model solution techniques are proposed, one based on the evaluation of the accumulated reward distribution and the other based on an equivalent model based on non-Markovian stochastic Petri nets. The obtained results are used to investigate the dependability of a whole WSN taking into account the presence of redundant nodes. Topological aspects are taken into account, providing a quantitative comparison among three typical network topologies: star, tree, and mesh. Numerical results are provided in order to highlight the advantages of the proposed technique and to demonstrate the equivalence of the proposed approaches.

}, keywords = {Energy consumption, Markov reward models, network topology, non-Markovian stochastic Petri nets, producibility, Reliability, Wireless sensor networks}, doi = {10.1177/1748006X10397845}, author = {D. Bruneo and A. Puliafito and Marco Scarpa} } @proceedings {276, title = {Adaptive Swarm Intelligence Routing Algorithms for WSN in a Changing Environment}, year = {2010}, month = {4 novembre 2010}, pages = {1813{\textendash}1818}, publisher = {IEEE}, author = {D. Bruneo and Marco Scarpa and A. BOBBIO and D. CEROTTI and M. GRIBAUDO} } @proceedings {279, title = {Dependability analysis of Wireless Sensor Networks with active-sleep cycles and redundant nodes}, journal = {DYADEM-FTS {\textquoteright}10 Proceedings of the First Workshop on DYnamic Aspects in DEpendability Models for Fault-Tolerant Systems}, volume = {Supplemental Volume of the 8th European Dependable Computing Conference}, year = {2010}, month = {30 Aprile, 2010}, pages = {25{\textendash}30}, publisher = {ACM}, address = {New York, NY}, author = {D. Bruneo and A. Puliafito and Marco Scarpa} } @proceedings {277, title = {Dependability Evaluation of Wireless Sensor Networks: Redundancy and Topological Aspects}, journal = {Sensors, 2010 IEEE}, year = {2010}, month = {4 novembre 2010}, pages = {1827{\textendash}1831}, publisher = {IEEE}, address = {Piscataway, NJ}, author = {D. Bruneo and A. Puliafito and Marco Scarpa} } @proceedings {282, title = {Performance evaluation of gLite Grids through GSPNs}, journal = {IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS}, volume = {21}, year = {2010}, pages = {1611{\textendash}1625}, abstract = {

Grid Computing supports the shared and coordinated use of several resources in dynamic Virtual Organizations. In the last few years, it is evolving into a business-innovating technology that is driving commercial adoption. Such a new scenario calls for powerful strategies able to guarantee stringent QoS requirements in order to meet Service Level Agreements (SLAs) between customers and providers. For this reason, it is necessary to analyze and predict performance with respect to different load conditions or management strategies. In this paper, we present a methodology to analyze performance in gLite Grids through the use of Generalized Stochastic Petri Nets (GSPNs). We introduce a cluster-level model of a typical gLite site taking into account the coexistence between normal and MPI-based jobs. We investigate the influence of different strategies (e. g., scheduling) on the performance of the whole site, highlighting aspects related to both customer and provider point of views. We also provide a business-oriented performance analysis introducing two different SLA typologies and highlighting how the site configuration may influence the expected profit of the service provider.

}, keywords = {generalized stochastic petri nets, gLite middleware, Grid computing, MPI-based jobs, performance measurements, service level agreement}, doi = {10.1109/TPDS.2010.35/10.1109/TPDS.2010.35}, author = {D. Bruneo and Marco Scarpa and A. Puliafito} } @proceedings {287, title = {Analytical Modeling Of Swarm Intelligence In Wireless Sensor Networks Through Markovian Agents}, year = {2009}, month = {22 October}, abstract = {

Publisher: The Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST)

}, author = {D. Bruneo and Marco Scarpa and A. BOBBIO and D. CEROTTI and M. GRIBAUDO} } @proceedings {292, title = {Mobile Middleware In Enterprise Systems}, journal = {Handbook of Enterprise Integration}, year = {2009}, pages = {115{\textendash}132}, publisher = {Mostafa Hashem Sherif}, address = {Tinton Falls, New Jersey, USA}, author = {D. Bruneo and A. Puliafito and Marco Scarpa and A. Zaia}, editor = {Mostafa Hashem Sherif} } @proceedings {297, title = {A GSPN Model To Analyze Performance Parameters in Glite Grids}, journal = {Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2008. WETICE {\textquoteright}08. IEEE 17th}, year = {2008}, month = {25, 2008}, pages = {198{\textendash}203}, publisher = {IEEE CS}, address = {Washington, DC}, abstract = {

Winner of the ETNGRID 2008 workshop best paper awardWinner of the ETNGRID 2008 workshop best paper awardWinner of the "ETNGRID best paper award" - ISSN:

}, doi = {10.1109/WETICE.2008.39}, author = {D. Bruneo and Marco Scarpa and A. Puliafito} } @proceedings {307, title = {Communication Paradigms for Mobile Grid Users}, journal = {Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003. 3rd IEEE/ACM International Symposium on}, year = {2003}, month = {15}, pages = {669{\textendash}676}, publisher = {IEEE / Institute of Electrical and Electronics Engineers Incorporated:445 Hoes Lane:Piscataway, NJ 08854:(800)701-4333, (732)981-0060, EMAIL: subscription-service@ieee.org, INTERNET: http://www.ieee.org, Fax: (732)981-9667}, address = {Piscataway, NJ}, author = {D. Bruneo and A. Puliafito and Marco Scarpa and A. Zaia} }