Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks

Abstract

Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for example, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.

Low-Power IoT for Monitoring Unconnected Remote Areas

“…In 3GPP, there is great momentum nowadays on NTN as a component of 5G systems and beyond. Not only UAVs but also High-Altitude Platform (HAP) stations (18-21 km of altitude) and Low-Earth Orbit (LEO) satellites (500-2000 km altitude) are valid solutions to communicate with ground IoT sensor nodes [9,10]. Here, we focus on using UAVs since they can be flexibly deployed where needed and represent cheaper solutions.…”

“…This solution introduces a tradeoff between communication robustness and the spatial density of nodes. This is because the transmission interval needed by a node could be doubled or tripled for one or two retransmissions, thus reducing the maximum density of sensors, as will be explained in relation to the next formula (9). Finally, by enlarging the data transmission time, there is a larger probability that the sensor's transmission will not be completed before the UAV coverage is lost.…”

Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment

“…Our study adopts a duopoly framework, where a finite network of mobile unmanned aerial vehicles is deployed based on a homogeneous Poisson point process (PPP) to serve ground-based IoT devices. These UAVs navigate according to a random waypoint (RWP) model, as described in [23]. As an initial step, we derive expressions for the coverage probability and service probability of each UAV in the current scenario.…”