Low Power Wide Area Networks (LPWANs) have become an important enabler for the Internet of Things (IoT) connectivity. Application domains like smart cities, smart agriculture, intelligent logistics and transportation, require communication technologies that combine long transmission ranges and energy efficiency. Recent and future trends make Long Range Wide Area Network (LoRaWAN) and Narrowband-IoT (NB-IoT) the most prospective drivers of the IoT business. In this paper, after discussing the main features of the two technologies, we carry out a fair quantitative comparison between the two, investigating different performance indicators, in order to guide designers in the selection of the most appropriate technology, depending on the application requirements.

Low Power Wide Area Network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an Adaptive Data Rate (ADR) algorithm to allocate proper data rates to end devices. However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to end devices one-by-one in an independent way. In this paper we propose a novel and more efficient ADR algorithm, denoted as Collision-Aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of end devices in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation-experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement w.r.t. the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.

Among the low power wide area network communication protocols for large scale Internet of Things, LoRaWAN is considered one of the most promising, owing to its flexibility and energy-saving capabilities. For these reasons, during recent years, the scientific community has invested efforts into assessing the fundamental performance limits and understanding the trade-offs between the parameters and performance of LoRaWAN communication for different application scenarios. However, this task cannot be effectively accomplished utilizing only analytical methods, and precise network simulators are needed. To that end, this paper presents LoRaWANSim, a LoRaWAN simulator implemented in MATLAB, developed to characterize the behavior of LoRaWAN networks, accounting for physical, medium access control and network aspects. In particular, since many simulators described in the literature are deployed for specific research purposes, they are usually oversimplified and hold a number of assumptions affecting the accuracy of their results. In contrast, our simulator has been developed for the sake of completeness and it is oriented towards an accurate representation of the LoRaWAN at the different layers. After a detailed description of the simulator, we report a validation of the simulator itself and we then conclude by presenting some results of its use revealing notable and non-intuitive trade-offs present in LoRaWAN. Such simulator will be made available via open access to the research community.

In the last years, the number of applications for Unmanned Aerial Vehicles (UAVs) has increased. Among them, the possibility to deploy them as flying base stations, namely Unmanned Aerial Base Stations (UABSs), has attracted the attention of industry and researchers. The unmatched mobility of UAVs, together with the unique quality of air-to-ground radio links, allow a boost in the capacity and coverage of existing mobile networks. In this paper, the use of UABSs is studied to assist a terrestrial mobile network aiming at serving moving connected vehicles, denoted as Ground User Equipments (GUEs), implementing Vehicle-To-Anything (V2X) extended sensing applications. To this aim, techniques are presented to tackle two important problems: trajectory design for the UABS allowing for tracking GUEs moving in a complex urban scenario and the scheduling of radio resources used to serve them. The former is solved by leveraging a Deep Reinforcement Learning (DRL) algorithm, Double Dueling Deep Q-Network (3DQN), whereas the latter is modelled via Integer Linear Program (ILP). Since we assume radio resources are all shared among GUEs, Macro Base Stations (MBS) and the UABS, the positioning of the UABS deeply affects interference, that is the radio resource management (RRM) algorithm; therefore, the two problems must be considered and solved jointly, choosing the reward function of the DRL algorithm properly. Two different scenarios are addressed: a coverage limited and a capacity limited one. Performance metrics shown are both machine learning related, delivering the training outcome of the agent, and network related, such as the percentage of satisfied GUEs for different application requirements.

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.

In this paper, we address the deployment of Unmanned Aerial Vehicles (UAVs) as Unmanned Aerial Base Stations (UABSs) which cooperate with Macro Base Stations (MBSs) in an urban environment to serve vehicles, denoted as Ground User Equipments (GUEs), implementing vehicle-to-everything (V2X) services. As vehicles perform extended sensing, exchanging data with nearby GUEs through UAVs and MBSs links, we propose an Integer Linear Programming (ILP) model that jointly optimizes radio resources allocation and beamforming, while accounting for vehicular application requirements, backhaul capacity limits and interference between GUE-UABS and GUE-MBS links. The model allows also to find a trade-off between benefits and cost of UABSs activation. Two system architectures are considered: a distributed model, where MBSs independently run the Radio Resource Management (RRM) algorithm sharing information with each other, and a centralized model, where MBSs send information to the network core, where the optimization algorithm runs. The study investigates interference through two resource allocation approaches, considering splitting and sharing of resources among UABSs and MBSs. Numerical evaluations demonstrate the effectiveness of using UABSs to improve the Quality of Experience (QoE) of GUEs. We also compare the two architectures, considering both resource pool assignments, and highlighting the impact of varying UABSs parameters and activation costs.