*5.2. Air Taxi Scenario*

Air taxi strategic paths have been obtained using the same weights of the previous section. The strategic costs breakdown and computational time for the two missions are reported in Table 4, where the nominal solution (i.e., the one having the minimum cost) for each mission has been highlighted in green. A lower computational time (about 20 s) is required in this case to obtain the solution, which depends on the different scenario geometry. Mission 2 does not have the *D*<sup>1</sup> solution, because the point located in the business center falls below its associated GNSS coverage map. The selected strategic path is the one associated with *D*2. When mission 1 is accounted for, the lowest cost trajectory is the one associated to *D*1, even if a shorter length is obtained using *D*2. This is due to the large landing site weighting factor, which tries to push the trajectory far from the shortest length one in order to make it pass over landing site locations. Results for Mission 1 and 2 are reported for the first step of the strategic planning algorithm in Figure 10a,b, respectively, along with the paths of the strategic intruders.

**Table 4.** Air taxi scenario. Strategic solution costs breakdown.


**Figure 10.** STEP I strategic solution and strategic mobile obstacles. Air taxing scenario (**a**) Mission 1 and (**b**) Mission 2. *αs* = *αw* = 1, *αr* =4, *αl* = 2.

The trajectory updates during the tactical phase are shown in Figures 11 and 12 for mission 1 and 2, respectively. A zoomed portion of the scenario, which encloses the trajectory in each mission, has been reported to better visualize the tactical variation of the path. For each figure, the *Level 1* solution in terms of velocity history is reported in subfigure b, whereas the *Level 2* and *3* trajectory deviation from the strategic path are shown in subfigure a. As in the previous section, the strategic (nominal) trajectory is also reported, as well as the trajectories of both the strategic and tactical intruders (in top view) and the GNSS coverage map associated with the strategic solution (in lateral view).

**Figure 11.** Tactical solution—Air taxi scenario, mission 1. (**a**) *Level 2* and *3* trajectories. (**b**) *Level 1* velocity norm history.

The tactical paths' cost breakdown is reported in Table 5, which also states the computational cost and the maximum navigation error. The latter, as in the previous case, slightly differs from the strategic one, thus not exceeding the maximum limits. Computation time is very low for the *Level 1* (below the second) and is at a maximum 6 s when *Level 2* and *3* are considered. Figure 12 again shows that the path obtained with *Level 2* locally deviates from the strategic trajectory by providing less modification, also in terms of path cost. Conversely, when the *Level 3* solution is used, path cost increases because the path is completely rebuilt without any knowledge of the ground information and costs. This could sometimes lead to a reduction of the trajectory length and duration (as in Mission 1). However, in all the cases, an increase of overall cost is provided.

**Figure 12.** Tactical solution—Air taxi scenario, mission 2. (**a**) *Level 2* and *3* trajectories. (**b**) *Level 1* velocity norm history.


**Table 5.** Air taxi scenario. Tactical solution costs breakdown.

#### **6. Conclusions**

Strategic and tactical planning algorithms to tackle UAV flight in urban environments have been presented and tested in this work, with the aim to provide an adaptive and scalable framework for urban operations. Indeed, the developed planning algorithms can deal with multiple sources of information by using the whole set of data or a subset of them. The design of the strategic path can be tailored to the user's needs by acting on the weighting cost factor, which spatially deviates the solution path towards the highest priority requirement. In addition, tactical modification to the trajectory allows reacting to unfavorable conditions, still ensuring the safety and effectiveness of the path. The entire algorithmic chain has been tested on two scenarios that involve air taxi within a very complex and obstacle-dense urban environment, and medical delivery from mainland to island. Results demonstrate the effectiveness of the proposed algorithms in yielding optimized and time-saving trajectories, thus highlighting the advantage of using unmanned aircraft to perform such operations. The promising results of the current work fulfill the SMARTGO ambition by creating an approach that can be used as a milestone for future urban air mobility planning algorithm design. Future efforts are aimed at further developing the conceived architecture and assessing its performance in very high traffic density, including flight rules and/or structured airspace. As an example, the tactical planner computational burden can be further reduced in order to better comply with dense, rapidly evolving scenarios, thus requiring better software engineering, which is foreseen as further algorithm improvement.

**Author Contributions:** Conceptualization, F.C. and G.F.; methodology, F.C., A.F. and G.F.; software, F.C. and A.F.; validation, F.C. and A.F.; formal analysis, F.C.; investigation, F.C. and A.F.; resources, F.C.; data curation, F.C.; writing—original draft preparation, F.C.; writing—review and editing, A.F. and G.F.; visualization, F.C.; supervision, G.F. and F.C.; project administration, G.F.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work has been carried out in the framework of the project SMARTGO, funded by the Italian Space Agency (ASI).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing is not applicable to this article.

**Acknowledgments:** The authors want to thank Valerio Pisacane from Euro.Soft S.r.l. and Alberto Mennella and Annamaria Tortora from TopView S.r.l. for their contribution to risk map derivation and use cases scenario definition, respectively.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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**Ronghua Xu <sup>1</sup> , Sixiao Wei 2, Yu Chen 1,\* , Genshe Chen <sup>2</sup> and Khanh Pham <sup>3</sup>**


**Abstract:** Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilienceoriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution.

**Keywords:** unmanned aerial vehicle (UAV); lightweight blockchain; drone security; assurance; authentication; resilience
