Smart City Gnosys

Smart city article details

Title Situational Awareness For Law Enforcement And Public Safety Agencies Operating In Smart Cities – Part 1: Technologies
ID_Doc 49008
Authors Minoli D.; Koltun A.; Occhiogrosso B.
Year 2022
Published EAI/Springer Innovations in Communication and Computing
DOI http://dx.doi.org/10.1007/978-3-030-84182-9_8
Abstract This chapter and the one that follows discuss the use of Internet of Things (IoT) concepts, technologies, and processes, in support of situational awareness applications for law enforcement and public safety agencies operating in smart city environments. Situational awareness facilitates efficiently discerning the physical environment, knowing what is going on in the ecosystem under consideration. Typically, there are many actors, many events, many “moving parts,” many input sensors, and many stakeholders. Advanced analytics are needed to parse, analyze, and boil down, often in real time, the large dataset generated by the sensors, which often consists of extensive visual information of various types. Artificial intelligence methods are increasingly needed to enhance the cloud analytics’ ability to extract actionable information for law enforcement stakeholders within the timeframe of interest. Part 1 of this two-chapter set highlights a number of artificial intelligence methods that may be utilized by the situational awareness platforms that are being deployed at this time in the field. Part 2 of this two-chapter set discusses specific SA tool used by law enforcement and some practical challenges affecting the actual rollout of these platforms in urban and municipal police departments. Some ethics issues are also highlighted, along with a brief assessment of ongoing research in this fast-evolving field. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords Always – on video; Analytics; Command center; Decision support; Deep learning; Machine learning; Situational awareness


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