On the performance of a uav-aided wireless network based on nb-iot

In recent years, interest in Unmanned Aerial Vehicles (UAVs) as a means to provide wireless connectivity has substantially increased thanks to their easy, fast and flexible deployment. Among the several possible applications of UAV networks explored by the current literature, they can be efficiently employed to collect Internet-of-Things (IoT) data because the non-stringent latency and small-size traffic type is particularly suited for UAVs’ inherent characteristics. However, the implications coming from the implementation of existing technology in such kinds of nodes are not straightforward. In this article, we consider a Narrow Band IoT (NB-IoT) network served by a UAV base station. Because of the many configurations possible within the NB-IoT standard, such as the access structure and numerology, we thoroughly review the technical aspects that have to be implemented and may be affected by the proposed UAV-aided IoT network. For proper remarks, we investigate the network performance jointly in terms of the number of successful transmissions, access rate, latency, throughput and energy consumption. Then, we compare the obtained results on different and known trajectories in the research community and study the impact of varying UAV parameters such as speed and height. Moreover, the numerical assessment allows us to extend the discussion to the potential implications of this model in different scenarios. Thus, this article summarizes all the main aspects that must be considered in planning NB-IoT networks with UAVs

Trajectories and resource management of flying base stations for C-V2X

In a vehicular scenario where the penetration of cars equipped with wireless communication devices is far from 100% and application requirements tend to be challenging for a cellular network not specifically planned for it, the use of unmanned aerial vehicles (UAVs), carrying mobile base stations, becomes an interesting option. In this article, we consider a cellular-vehicle-to-anything (C-V2X) application and we propose the integration of an aerial and a terrestrial component of the network, to fill the potential unavailability of short-range connections among vehicles and address unpredictable traffic distribution in space and time. In particular, we envision a UAV with C-V2X equipment providing service for the extended sensing application, and we propose a UAV trajectory design accounting for the radio resource (RR) assignment. The system is tested considering a realistic scenario by varying the RRs availability and the number of active vehicles. Simulations show the results in terms of gain in throughput and percentage of served users, with respect to the case in which the UAV is not present

Performance evaluation of the dynamic trajectory design for an unmanned aerial base station in a single frequency network

Using an Unmanned Aerial Base Station (UABS) i.e., a base station carried by a UAV (Unmanned Aerial Vehicle) or drone, is a promising approach to offer coverage and capacity to those users that are not being served by the base stations of the terrestrial network. In this paper, we propose an approach to the design of the drone's trajectory to account for the quickly varying user traffic and pattern. This approach is based on the identification of clusters made of nearby users to be served. The decision on which cluster to visit next by the UABS depends on a cost-function considering the distance to the next cluster, the user density and spread in the cluster, and the direction compared to the previously visited cluster. Furthermore, we propose a radio resource assignment algorithm to minimize the interference from the UABS to the terrestrial network when both are operating in the same frequency band. The potential improvements in terms of network capacity (sum throughput) and user satisfaction are estimated in this study

Modeling UAV-Based IoT Clustered Networks for Reduced Capability UEs

In recent years, the use of unmanned aerial vehicles (UAVs) for wireless communications has been shown promising in a plethora of different applications. Their flexible deployment and mobility make them key enablers for the next generation of networks, provided that system design is properly addressed. In this article, we analyze a beyond-5G network where a UAV, acting as an unmanned aerial base station (UAB), is employed to collect data from reduced capability user equipments (UEs), deployed in an urban area. Specifically, we study a cluster-based scenario, where UEs are deployed following a Thomas cluster process, and the UAB flies over cluster centers according to the traveling salesman problem solution. Through the use of a stochastic approach, we mathematically devise the system performance accounting for uplink transmission protocol constraints, random access procedure, finite number of radio resources available, and coverage issues during the UAB flight. The mathematical model, validated via comparison with simulations, allows to optimize some system parameters, like the UAB speed, the number of UEs per cluster, and the number of radio resources to be used for the access and for data transmissions

On the Modelling of UAV-Aided Networks Using NarrowBand-IoT

In this paper, we consider a NarrowBand-Internet of Things (NB-IoT) network where an Unmanned Aerial Vehicle (UAV), acting as Unmanned Aerial Base Station (UAB), is employed to collect data from IoT nodes deployed in a service area. Due to the UAB inherent mobility and NB-IoT protocol constraints, the design of proper parameters setting is not an easy task. To try and solve this problem, we analyse the scenario through stochastic tools, allowing to introduce an appropriate relation between protocol and UAB flight parameters, together with application requirements. Specifically, we study a cluster-based scenario, where IoT nodes are assumed to be deployed according to a Thomas cluster process, and we apply a Traveling Salesman Problem to design the UAB trajectory over the area. Notably, our model considers the protocol constraints, in terms of resource units available in the NB-IoT NPUSCH channel, the random access procedure implemented in the NPRACH channel and the data rate that can be provided to IoT nodes. Results allow to quickly deduce optimal design parameters for achieving the maximum network throughput

Natural and synthetic phosphates as binding agent of Pb, Zn, Cu and Cd in the environment

The sorption of Pb, Zn, Cu and Cd from aqueous multiple-species solutions by synthetic hydroxyapatite (HAP) was investigated. HAP was reacted with solutions containing different heavy metals concentrations (10, 100 and 500 mg/L) for four times (2, 4, 24 and 48h). The results showed that HAP was very effective in removal heavy metals from aqueous solutions. Approximately 95-99% of the Pb applied was removed from solutions, with the best sorption capacity of 497 mg of Pb/g of HAP, while 92-99% of Zn, 93-99% of Cu and 88-99% of Cd added were attenuated, with best removal capacity of 498, 485 and 477 mg/g, respectively. Sorption mechanisms other than dissolution/precipitation of crystalline phases could be involved in attenuating heavy metals concentrations such as ion exchange, coprecipitation, surface complexation and formation of amorphous phases. The study results confirm HAP provides a cost effective method for the decontamination of solutions polluted by heavy metals

Metaheuristic Algorithms for UAV Trajectory Optimization in Mobile Networks

We consider a mobile network in which traditional static terrestrial base stations are not capable of completely serving the existing user demand, due to the huge number of connected devices. In this setting, an equipped Unmanned Aerial Vehicle (UAV) can be employed to provide network connection where needed in a flexible way, thereby acting as an unmanned aerial base station. The goal is to determine the best UAV trajectory in order to serve as many users as possible. The UAV can move at different speeds and can serve users within its communication range, although the data rate depends on the positions of UAV and users. In addition, each user has a demand (e.g., the number of bits the user wants to download/upload from/to the network) and a time window during which requires the service. We propose a Biased Random-Key Genetic Algorithm (BRKGA) and a Simulated Annealing Algorithm (SAA), and compare them on realistic instances with more than 500 users in different settings