@proceedings {Maisano2018, title = {Reducing Complexity of 3D Indoor Object Detection}, journal = {IEEE 4th International Forum on Research and Technologies for Society and Industry, RTSI 2018 - Proceedings}, year = {2018}, note = {cited By 0; Conference of 4th IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2018 ; Conference Date: 10 September 2018 Through 13 September 2018; Conference Code:143080}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Palermo, Italy - 10-13 September 2018}, abstract = {

This work deals with the problem of amodal perception of 3D object detection in indoor environments. We revisited a novel method of 3D object detection [4], in terms of complexity and runtime speed. 3D detection regards not only objects localization in the 3D world, but also estimating their physical sizes and poses, even if only parts of them are visible in the RGB-D image. By following the 2.5D representation approach, the system under study achieves a better mean average precision in detection (40.1\%) with respect to all recent methods, but the complexity of the system is very high and, at the moment, its implementation doesn{\textquoteright}t fit in a small device with low resources in memory and computation. We revisited the referenced system through a variation in its network architecture by introducing a well-adapted and {\textquoteright}fine-tuned{\textquoteright} MobileNet from Google, with the goal of reducing the complexity of the whole system. Considerable reduction in complexity, computational cost (MAC operations) and memory requirements have been achieved. Many detected classes showed an acceptable level of accuracy and also the speed of the recognition system increased. Final experiments have been conducted on NYUV2 dataset. {\textcopyright} 2018 IEEE.

}, keywords = {3-D object detections, Complex networks, Complexity reduction, Computational costs, Embedded device, Indoor environment, Memory requirements, MobileNet, Network architecture, Object detection, Object recognition, Recognition systems}, isbn = {9781538662823}, doi = {10.1109/RTSI.2018.8548514}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060018242\&doi=10.1109\%2fRTSI.2018.8548514\&partnerID=40\&md5=e5013d2f13df31fcdea840d7f8859acf}, author = {Roberta Maisano and Valeria Tomaselli and Alessandro Capra and Francesco Longo and Antonio Puliafito} } @proceedings {Kushwaha2018864, title = {Providing sensor services by data correlation: The $\#$SmartME approach}, journal = {Conference on Complex, Intelligent, and Software Intensive Systems}, volume = {611}, year = {2017}, note = {cited By 0; Conference of 11th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2017 ; Conference Date: 10 July 2017 Through 12 July 2017}, pages = {864-874}, publisher = {Springer Verlag}, address = {Torino, Italy - 10-12 July 2017}, abstract = {

In the current era Internet is the most used medium for sharing and retrieving the information for building applications which are commonly developed for enhancing the user experience in terms of comfort, communication. For this, the need of real-time sensor data gains importance. The data collected from the physical objects should be easily available for different applications. Semantic representation of the sensor data directly addresses the problem of storing it in logical, easily accessible and extensible manner. Our paper works towards converting the already collected sensor data of the $\#$SmartME project into semantic format and also proposes real-time storage of semantically enriched sensor data. To build applications using these sensor data the authors consider mainly three kinds of sensors, i.e., Temperature, Humidity, Pressure. Predicting the observed value of any sensor data is the main aim of this work. The analysis leverages other sensors \& environmental parameters such as Date, Time, Longitude, Latitude, Altitude etc. Correlation among these parameters and the accuracy of the predicted results showed the suitability of our proposed idea. {\textcopyright} Springer International Publishing AG 2018.

}, keywords = {Building applications, Complex networks, Correlations, Data correlations, Data mining, Digital storage, Environmental parameter, Real time sensors, Semantic representation, Semantic Web, sensor networks, SmartME, Stack4Things}, isbn = {9783319615653}, issn = {21945357}, doi = {10.1007/978-3-319-61566-0_82}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026317817\&doi=10.1007\%2f978-3-319-61566-0_82\&partnerID=40\&md5=7b4cb65c4b09ea03df77bbea893f4002}, author = {Nidhi Kushwaha and Giovanni Merlino and Francesco Longo and Dario Bruneo and Antonio Puliafito and O.P. Vyas} } @proceedings {Merlino2015268, title = {Enabling mechanisms for Cloud-based network virtualization in IoT}, journal = {IEEE World Forum on Internet of Things, WF-IoT 2015 - Proceedings}, year = {2015}, note = {cited By 3; Conference of 2nd IEEE World Forum on Internet of Things, WF-IoT 2015 ; Conference Date: 14 December 2015 Through 16 December 2015; Conference Code:119271}, pages = {268-273}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Milan (Italy)}, abstract = {

As part of a wider effort in integrating Internet of things (IoT) with the Cloud under the guise of infrastructure to be provided as-a-Service, network virtualization plays an essential role, both as an enabler of Infrastructure-as-a-Service scenarios and as a basic building block of the solution for the IoT-focused Cloud provider. Virtualization of the networking facilities for Cloud-managed IoT resources needs mechanisms to deal with the inherent complexity. This work outlines an implementation-agnostic approach to such a problem, reflected in our evolving Stack4Things architecture, derived from OpenStack, and implemented starting from such codebase, by leveraging also a choice of modern tooling and protocols. A specific use case and the discussion that follows are provided to frame the benefits of this strategy. {\textcopyright} 2015 IEEE.

}, keywords = {Basic building block, Cloud providers, Clouds, Complex networks, Infrastructure as a service (IaaS), Inherent complexity, Internet, Internet of Things, Internet of Things (IOT), Network architecture, network virtualization, OpenStack, Virtual reality, Virtualizations, WebSocket}, isbn = {9781509003655}, doi = {10.1109/WF-IoT.2015.7389064}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964453184\&doi=10.1109\%2fWF-IoT.2015.7389064\&partnerID=40\&md5=555a2a5aad4f3af24fac04fc0e4a8280}, author = {Giovanni Merlino and Dario Bruneo and Salvatore Distefano and Francesco Longo and Antonio Puliafito} } @proceedings {Marquezan201555, title = {3-D cloud monitoring: Enabling effective cloud infrastructure and application management}, journal = {Proceedings of the 10th International Conference on Network and Service Management (CNSM)}, year = {2014}, note = {cited By 0; Conference of 10th International Conference on Network and Service Management, CNSM 2014 ; Conference Date: 17 November 2014 Through 21 November 2014; Conference Code:110192}, pages = {55-63}, publisher = {IEEE Computer Society}, address = {Rio de Janeiro, Brazil, 17-21 November 2014}, abstract = {

A cloud environment is a complex environment composed of many different entities and layers. Each of these cloud entities may be furnished with mechanisms offering various management actions. For any given situation, different management actions may be applicable and often simultaneously. Enforcing isolated management actions or combining contradictory management actions may negatively affect cloud application quality and cloud infrastructure performance. This means that correctly selecting and effectively combining these management actions for a given situation becomes an important challenge in cloud computing. In this paper, we address the problem of identifying situations where more than one management action can be performed. The key contributions of our paper are: (1) a three dimensional (3-D) monitoring model for analyzing cloud monitoring information; (2) the concept and formalization of Context of Interest (CoI) that specifies how to retrieve meaningful information from the 3-D model to support the coordination of management actions between cloud infrastructure and application. We conducted experiments in a real testbed using Openstack and the WordPress Web site application. Our results show that analyzing cloud monitoring information using the 3-D model and the CoI can support a more effective identification of management actions to be taken. {\textcopyright} 2014 IFIP.

}, keywords = {Application management, Cloud applications, Cloud environments, Cloud infrastructures, Complex environments, Complex networks, Formalization of context, Monitoring models, Three dimensional computer graphics, Threedimensional (3-d)}, isbn = {9783901882661}, doi = {10.1109/CNSM.2014.7014141}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84922823116\&partnerID=40\&md5=14afe05eea8a48d4fc7c6c8e57dd6208}, author = {Clarissa Marquezan and Dario Bruneo and Francesco Longo and Florian Wessling and Andreas Metzger and Antonio Puliafito} }