@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 = {<p>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 \&amp; Sons, Ltd.</p>
},
	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}
}
@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 = {<p>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.</p>
},
	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}
}
