@proceedings {Dautov2018792, title = {Pushing intelligence to the edge with a stream processing architecture}, journal = {Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017}, year = {2017}, note = {cited By 3; Conference of Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 ; Conference Date: 21 June 2017 Through 23 June 2017; Conference Code:134517}, pages = {792-799}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Exeter, UK - 21-23 June 2017}, abstract = {

The cloud computing paradigm underpins the Internet of Things (IoT) by offering a seemingly infinite pool of resources for processing/storing extreme amounts of data generated by complex IoT systems. The cloud has established a convenient and widely adopted approach, where raw data are vertically offloaded to cloud servers from resource-constrained edge devices, which are only seen as simple data generators, not capable of performing more sophisticated processing activities. However, there are more and more emerging scenarios, where the amount of data to be transferred over the network to the cloud is associated with increased network latency, making the results of the computation obsolete. As various categories of edge devices are becoming more and more powerful in terms of hardware resources - specifically, CPU and memory - the established way of off-loading computation to the cloud is not always seen as the most convenient approach. Accordingly, this paper presents a Stream Processing architecture for spreading workload among a local cluster of edge devices to process data in parallel, thus achieving faster execution and response times. The experimental results suggest that such a distributed in-memory approach to data processing at the very edge of a computational network has a potential to address a wide range of IoT-related scenarios. {\textcopyright} 2017 IEEE.

}, keywords = {Apache NiFi, cloud computing, Cluster computing, Computational networks, Data handling, Edge computing, Green Computing, Hardware resources, Horizontal offloading, Internet of thing (IOT), Internet of Things, Memory architecture, Network architecture, Network latencies, Processing activity, Stream processing}, isbn = {9781538630655}, doi = {10.1109/iThings-GreenCom-CPSCom-SmartData.2017.121}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047094836\&doi=10.1109\%2fiThings-GreenCom-CPSCom-SmartData.2017.121\&partnerID=40\&md5=4e5a4b0eaffa179565af183066520cdf}, author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito} } @article {Longo201753, title = {Stack4Things: a sensing-and-actuation-as-a-service framework for IoT and cloud integration}, journal = {Annales des Telecommunications/Annals of Telecommunications - Institut Mines-T{\'e}l{\'e}com and Springer-Verlag France}, volume = {72}, number = {1-2}, year = {2017}, note = {cited By 0}, pages = {53-70}, publisher = {Springer-Verlag France}, abstract = {

With the increasing adoption of embedded smart devices and their involvement in different application fields, complexity may quickly grow, thus making vertical ad hoc solutions ineffective. Recently, the Internet of Things (IoT) and Cloud integration seems to be one of the winning solutions in order to opportunely manage the proliferation of both data and devices. In this paper, following the idea to reuse as much tooling as possible, we propose, with regards to infrastructure management, to adopt a widely used and competitive framework for Infrastructure-as-a-Service such as OpenStack. Therefore, we describe approaches and architectures so far preliminary implemented for enabling Cloud-mediated interactions with droves of sensor- and actuator-hosting nodes by presenting Stack4Things, a framework for Sensing-and-Actuation-as-a-Service (SAaaS). In particular, starting from a detailed requirement analysis, in this work, we focus on the subsystems of Stack4Things devoted to resource control and management as well as on those related to the management and collection of sensing data. Several use cases are presented justifying how our proposed framework can be viewed as a concrete step toward the complete fulfillment of the SAaaS vision. {\textcopyright} 2016, Institut Mines-T{\'e}l{\'e}com and Springer-Verlag France.

}, keywords = {Clouds, Information management, Infrastructure as a service (IaaS), Infrastructure managements, Internet of thing (IOT), Internet of Things, Mediated interaction, OpenStack, Requirement analysis, SAaaS, WAMP, WebSocket}, issn = {00034347}, doi = {10.1007/s12243-016-0528-5}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976292948\&doi=10.1007\%2fs12243-016-0528-5\&partnerID=40\&md5=f334f652432ae0993795644204689e9c}, author = {Francesco Longo and Dario Bruneo and Salvatore Distefano and Giovanni Merlino and Antonio Puliafito} } @proceedings {Merlino2017213, title = {Quantitative evaluation of Cloud-based network virtualization mechanisms for IoT}, journal = {ValueTools 2016 - 10th EAI International Conference on Performance Evaluation Methodologies and Tools}, year = {2016}, note = {cited By 0; Conference of 10th EAI International Conference on Performance Evaluation Methodologies and Tools, ValueTools 2016 ; Conference Date: 25 October 2016 Through 28 October 2016; Conference Code:127816}, pages = {213-216}, publisher = {Association for Computing Machinery}, address = {Taormina; Italy; 25-28 October 2016}, abstract = {

Integration of the Internet of Things (IoT) with the Cloud may lead to a range of different architectures and solutions. Our efforts in this domain are mainly geared towards making IoT systems available as service-oriented infrastructure. Under Infrastructure-as-a-Service (IaaS) scenarios, network virtualization is a core building block of any solution, even more so for IoT-focused Cloud providers. Enabling mechanisms are required to support virtualization of the networking facilities for IoT resources that are managed by the Cloud. This work describes an approach to network virtualization based on popular off-the-shelf tools and protocols in place of application-specific logic, acting as a blueprint in the design of the Stack4Things architecture, an OpenStack-derived framework to provide IaaS-like services from a pool of IoT devices. We quantitatively evaluate the underlying mechanisms demonstrating that the proposed approach exhibits mostly comparable performance with respect to standard technologies for virtual private networks, or at least good enough for the kind of underlying hardware, e.g., smart boards, whilst still representing a more flexible solution. Copyright {\textcopyright} 2016 EAI.

}, keywords = {Application specific, Clouds, Distributed computer systems, Infrastructure as a service (IaaS), Internet of thing (IOT), Internet of Things, Network architecture, network virtualization, OpenStack, Performance evaluation, Platform as a Service (PaaS), Quantitative evaluation, Reverse tunneling, Service-oriented infrastructures, Virtual private networks, Virtual reality, Virtualization}, isbn = {9781631901416}, doi = {10.4108/eai.25-10-2016.2266600}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021354856\&doi=10.4108\%2feai.25-10-2016.2266600\&partnerID=40\&md5=61d1e54a06f72746e6e5bd90c920b1c0}, author = {Giovanni Merlino and Francesco Longo and Salvatore Distefano and Dario Bruneo and Antonio Puliafito} }