Flying ad-hoc networks are becoming a promising solution for different application scenarios involving unmanned aerial vehicles, like urban surveillance or search and rescue missions. However, such networks present various and very specific communication issues. As a consequence, there are several research studies focused on analyzing their performance via simulation. Correctly modeling mobility is crucial in this context and although many mobility models are already available to reproduce the behavior of mobile nodes in an ad-hoc network, most of these models cannot be used to reliably simulate the motion of unmanned aerial vehicles. In this article, we list the existing mobility models and provide guidance to understand whether they could be actually adopted depending on the specific flying ad-hoc network application scenarios, while discussing their advantages and disadvantages.

Scalable routing for wireless communication systems is a compelling and challenging task. To this aim, routing algorithms exploiting geographic information have been proposed. These algorithms refer to nodes by their location, rather than their address, and use those coordinates to route greedily towards a destination. With the advent of unmanned airborne vehicle (UAV) technology, a lot of research effort has been devoted to extend position-based packet routing proposals to three dimensional environments. In this context, Flying Ad-hoc Networks (FANETs), comprised of autonomous flying vehicles, pose several issues. This work focuses on the state-of-the-art, stateless geographic packet routing protocols conceived or adapted for three-dimensional network scenarios. Proposals are evaluated over a common scenario through a comprehensive comparative analysis.

The ever increasing pace of IoT deployment is opening the door to concrete implementations of smart city applications, enabling the large-scale sensing and modeling of (near-)real-time digital replicas of physical processes and environments. This digital replica could serve as the basis of a decision support system, providing insights into possible optimizations of resources in a smart city scenario. In this article, we discuss an extension of a prior work, presenting a detailed proof-of-concept implementation of a Digital Twin solution for the Urban Facility Management (UFM) process. The Interactive Planning Platform for City District Adaptive Maintenance Operations (IPPODAMO) is a distributed geographical system, fed with and ingesting heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and build synthetic indexes measuring the activity level inside an area of interest. IPPODAMO takes into account potential interference from other stakeholders in the urban environment, enabling the informed scheduling of operations, aimed at minimizing interference and the costs of operations.