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Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities – Part 1: Technologies

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IoT and WSN based Smart Cities: A Machine Learning Perspective

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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.

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Notes

  1. 1.

    Some researchers have used the phrase “situation awareness”; in this chapter we consider both terms describing the same discipline.

  2. 2.

    Other advanced AI fields (not further discussed in this chapter, but with theoretical applicability to situational awareness) include cognitive computing (systems that endeavor to understand and emulate human behavior, while also providing more natural and intuitive interface to the machine) and natural language processing (NLP) systems that allow machines to understand written language or voice commands (also including natural language generation that enables the machine to communicate in “spoken conversation”).

  3. 3.

    Google Scholar identifies over 3,100,000 papers on the topic of “machine learning.”

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    Daniel Minoli, Andrzej Koltun & Benedict Occhiogrosso

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  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Shalli Rani

  2. Intel Corporation, Folsom, CA, USA

    Vyasa Sai

  3. Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, India

    R. Maheswar

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Minoli, D., Koltun, A., Occhiogrosso, B. (2022). Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities – Part 1: Technologies. In: Rani, S., Sai, V., Maheswar, R. (eds) IoT and WSN based Smart Cities: A Machine Learning Perspective. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-84182-9_8

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