Maturity Levels of Public Safety Applications using Unmanned Aerial Systems: a Review

Abstract

Unmanned Aerial Systems (UAS) are becoming increasingly popular in the public safety sector. While some applications have so far only been envisioned, others are regularly performed in real-life scenarios. Many more fall in between and are actively investigated by research and commercial communities alike. This study reviews the maturity levels, or “market-readiness”, of public safety applications for UAS. As individual assessments of all applications suggested in the literature are infeasible due to their sheer number, we propose a novel set of application categories: Remote Sensing, Mapping, Monitoring, Human-drone Interaction, Flying Ad-hoc Networks, Transportation, and Counter UAV Systems. Each category’s maturity is assessed through a literature review of contained applications, using the metric of Application Readiness Levels (ARLs). Relevant aspects such as the environmental complexity and available mission time of addressed scenarios are taken into account. Following the analysis, we infer that improvements in autonomy and software reliability are the most promising research areas for increasing the usefulness and acceptance of UAS in the public safety domain.

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

“…To be more specific, and find out the publications that use ML, RL, and FL in 5G and 6G networks we combined (“Machine learning” OR “reinforcement learning” OR “Federate Learning”) AND (“5G” OR “6G”) into to the first search and make $$s5$$. The search started with terms found in relevant SLRs (Haula & Agbozo, 2020; Mualla et al, 2019; Rejeb et al, 2021; Stampa et al, 2021), then continued with the combination of these terms, and the creation of new ones by considering the main keywords of the research questions as well. On the basis of the acquired results, the selected terms were refined until coming to those in Table 2.…”

“…To be more specific, and find out the publications that use ML, RL, and FL in 5G and 6G networks we combined ("Machine learning" OR "reinforcement learning" OR "Federate Learning") AND ("5G" OR "6G") into to the first search and make s5. The search started with terms found in relevant SLRs (Haula & Agbozo, 2020;Mualla et al, 2019;Rejeb et al, 2021;Stampa et al, 2021), then continued with the combination of these terms, and the creation of T A B L E 1 Research questions of the study based on the two types of questions: statistical and specific questions (the number of publications by year (A), by countries (B), funding bodies (C), and the number of published patents (D).…”

“…There is a small number of existing SLRs of FANETs that cover these topics, their joint applications, and their open challenges (Haula & Agbozo, 2020; Mualla et al, 2019; Rejeb et al, 2021; Stampa et al, 2021). However each of them only considers specific aspects and applications of UAVs.…”

“…The SLR in Rejeb et al (2021) provides a survey regarding UAVs in supply chain management and logistics. Mualla et al (2019) and Haula and Agbozo (2020) only focus on UAVs in civilian applications, while in Stampa et al (2021) they address public safety applications of UAVs. Such lack of extensive SLRs about FANETs in the literature motivates the employment of a comprehensive SLR methodology in this paper.…”

On Onboard LiDAR-based Flying Object Detection

“…However, as reported in [4], the maturity of fully autonomous C-UAS' is lacking. The authors rate the maturity of manual C-UAS systems with an Application Readiness Level (ARL) 7 out of 9, which corresponds to "functionality demonstrated", but not proven.…”