@article {Dautov20181475, title = {Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms}, journal = {Software - Practice and Experience - John Wiley \& Sons, Ltd.}, volume = {48}, number = {8}, year = {2018}, note = {cited By 0}, pages = {1475-1492}, publisher = {John Wiley and Sons Ltd}, abstract = {

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a {\textquotedblleft}horizontal{\textquotedblright} manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed {\textquotedblleft}vertically{\textquotedblright} to the cloud. Copyright {\textcopyright} 2018 John Wiley \& Sons, Ltd.

}, keywords = {Big Data, cloud computing, Distributed Smart Cameras, Edge computing, Face recognition, Information management, Intelligent surveillance systems, Internet of Things, monitoring, Multiple video streams, Network security, Public safety and securities, Security systems, Smart city, Stack4Things, Stream processing, Surveillance systems, Technological advances}, issn = {00380644}, doi = {10.1002/spe.2586}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049578094\&doi=10.1002\%2fspe.2586\&partnerID=40\&md5=25de910451975bb24c9cfbdf6ca69066}, author = {Rustem Dautov and Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito and Rajkumar Buyya} } @article {Distefano2017439, title = {Personalized Health Tracking with Edge Computing Technologies}, journal = {BioNanoScience}, volume = {7}, number = {2}, year = {2017}, note = {cited By 0}, pages = {439-441}, publisher = {Springer New York LLC}, abstract = {

The health monitoring component is the essential block, a pillar of several e-health systems. Plenty of health tracking applications and specific technologies such as smart devices, wearables, and data management systems are available. To be effective, promptly reacting to issues, a health monitoring service must ensure short delays in data sensing, collection, and processing activities. This is an open problem that distributed computing paradigms, such as Internet of Things (IoT), Cloud, and Edge computing, could address. The solution proposed in this paper is based on Stack4Things, an IoT-Cloud framework to manage edge nodes such as mobiles, smart objects, network devices, workstations, as a whole, a computing infrastructure allowing to provide resources on-demand, as services, to end users. Through Stack4Things facilities, the health tracking system can locate the closer computing resource to offload processing and thus reducing latency per the Edge computing paradigm. {\textcopyright} 2016, Springer Science+Business Media New York.

}, keywords = {Clouds, Computing infrastructures, Data management system, Distributed computer systems, Edge computing, Health, Health monitoring, Health tracking systems, human, human computer interaction, Information management, Internet, Internet of Things, Internet of Things (IOT), monitoring, Stack4Things, Tracking application, Wearable technology}, issn = {21911630}, doi = {10.1007/s12668-016-0388-5}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019121810\&doi=10.1007\%2fs12668-016-0388-5\&partnerID=40\&md5=e5aa843f2f869945fe04d5f62c97a6c5}, author = {Salvatore Distefano and Dario Bruneo and Francesco Longo and Giovanni Merlino and Antonio Puliafito} } @proceedings {Dautov2017, title = {Towards a global intelligent surveillance system}, journal = {11th International Conference on Distributed Smart Cameras, ICDSC 2017}, year = {2017}, note = {cited By 2; Conference of 11th International Conference on Distributed Smart Cameras, ICDSC 2017 ; Conference Date: 5 September 2017 Through 7 September 2017; Conference Code:132201}, publisher = {Association for Computing Machinery}, address = {Stanford, USA - 05-07 September 2017}, abstract = {

Recent technological advances have led to the rapid development of Intelligent Surveillance Systems (ISSs), ubiquitously present in modern urban spaces are constantly generating streams of raw data. As most of the actual Internet traffic is nowadays constituted by visual data streams, often originated by ISSs, it is important to properly manage these avalanches of data so as to support sustainability of this technological trend, which will very likely saturate the current network bandwidth in few years. This paper aims to combine existing technologies and paradigms from the Internet of Things, Cloud, Edge Computing and Big Data into a common framework to enable a shared approach for ISSs at a wide geographical scale, thus envisioning a Global ISS. The proposed solution is based on the idea of pushing data processing tasks as close to data sources as possible, thus increasing security and performance levels, usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of the proposed approach, the paper presents a case study based on a distributed ISS scenario in a crowded area, implemented on clustered edge devices able to offload tasks in a {\textquoteright}horizontal{\textquoteright} manner. {\textcopyright} 2017 Association for Computing Machinery.

}, keywords = {Big Data, Clouds, Edge, Geographical scale, Information management, Intelligent surveillance systems, Internet of Things, monitoring, Network security, Security and performance, Security systems, Stream processing, Surveillance systems, Technological advances, Technological trends}, doi = {10.1145/3131885.3131918}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047726635\&doi=10.1145\%2f3131885.3131918\&partnerID=40\&md5=a84eba85cc0facc8c5bb0cd664d7d5f0}, author = {Rustem Dautov and Salvatore Distefano and Giovanni Merlino and Dario Bruneo and Francesco Longo and Antonio Puliafito} } @proceedings {Bruneo201562, title = {A framework for the 3-D cloud monitoring based on data stream generation and analysis}, journal = {Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)}, year = {2015}, note = {cited By 0; Conference of 14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015 ; Conference Date: 11 May 2015 Through 15 May 2015; Conference Code:113524}, pages = {62-70}, publisher = {IEEE Computer Society}, address = {Ottawa, Canada, 11-15 May 2015}, abstract = {

Cloud monitoring is one important aspect for effective cloud management. Currently, cloud monitoring solutions can be classified into three groups: the ones not considering multiple layers or real time data analysis, the ones considering multiple layers but not real time data analysis, and the ones only considering real time data analysis. However, all these solutions fail to provide frameworks able to combine together monitoring in multiple layers and data stream analysis for detecting situations where multiple management actions are applicable in different layers of the cloud environment. This paper addresses this gap and proposes the Ceiloesper framework. Such a framework extends the OpenStack Ceilometer technology with Esper CEP and enables collection and analysis of information according to the principles defined in the 3-D cloud monitoring model, proposed in a previous work. The main contributions of this paper are: (i) the definition of the concept of Situation of Interest (SoI) leading to multiple management actions; (ii) the Ceiloesper architecture for a monitoring solution combining traditional monitoring elements with CEP; (iii) extensions to the Ceilometer OpenStack technology. We tested the Ceiloesper framework on a scenario based on the Wordpress application and the experimental results show its effectiveness. {\textcopyright} 2015 IEEE.

}, keywords = {Cloud environments, Cloud managements, Cloud monitoring, Data communication systems, Data handling, Data stream, Different layers, Information analysis, Meteorological instruments, monitoring, Multiple layers, Network management, Real time data analysis, Scenario-based}, isbn = {9783901882760}, doi = {10.1109/INM.2015.7140277}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84942574737\&partnerID=40\&md5=9727c6a66767d639f5c5cfd3bd7d96c9}, author = {Dario Bruneo and Francesco Longo and Clarissa Marquezan} } @proceedings {39, title = {The Need of a Hybrid Storage Approach for IoT in PaaS Cloud Federation}, journal = {Proceedings of the 28th International Conference on Advanced Information Networking and Applications Workshops}, year = {2014}, month = {May}, pages = {779{\textendash}784}, publisher = {IEEE COMPUTER SOC}, address = {Washington, DC}, abstract = {

Monitoring activities over many different types of sensors are very challenging to support advanced services for Internet of Things (IoT) and its future. However, one of the major issues is the explosion of the amount of heterogeneous information that has to be stored and processed, thus causing the well known Big Data problem. Some Cloud strategies have been investigated to offer IoT-oriented services, but they do not specifically address solutions for Big Data management. In this paper, we present a two-layer architecture based on a hybrid storage system able to support a Platform as a Service (PaaS) federated Cloud scenario. The proposed architecture combines the benefits of both storage approaches. In particular, it allows us on one hand to extend SQL-like legacy systems, and on the other hand to manage Big Data through an XML-like, non-SQL distributed storage system according to a Cloud federation approach.

}, keywords = {Big Data, cloud computing, federation, IoT, monitoring, PaaS, sensor network}, doi = {10.1109/WAINA.2014.162}, author = {M. Fazio and A. Celesti and M. Villari and A. Puliafito} }