Autonomic Faulty Node Replacement in UAV-Assisted Wireless Sensor Networks: a Test-bed

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows

A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme

20 Years of Evolution from Cognitive to Intelligent Communications

It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems.Comment: The paper has been accepted by IEEE Transactions on Cognitive Communications and Networkin

What Will the Future ofUAV Cellular Communications Be?A Flight from 5G to 6G

What will the future of UAV cellular communicationsbe?In this tutorial article, we address such a compelling yetdifficult question by embarking on a journey from 5G to 6Gand expounding a large number of case studies supported byoriginal results. We start by overviewing the status quo on UAVcommunications from an industrial standpoint, providing freshupdates from the 3GPP and detailing new 5G NR features insupport of aerial devices. We then dissect the potential andthe limitations of such features. In particular, we demonstratehow sub-6 GHz massive MIMO can successfully tackle cellselection and interference challenges, we showcase encouragingmmWave coverage evaluations in both urban and suburban/ruralsettings, and we examine the peculiarities of direct device-to-device communications in the sky. Moving on, we sneak a peekat next-generation UAV communications, listing some of the usecases envisioned for the 2030s. We identify the most promising6G enablers for UAV communication, those expected to takethe performance and reliability to the next level. For each ofthese disruptive new paradigms (non-terrestrial networks, cell-free architectures, artificial intelligence, reconfigurable intelligentsurfaces, and THz communications), we gauge the prospectivebenefits for UAVs and discuss the main technological hurdles thatstand in the way. All along, we distil our numerous findings intoessential takeaways, and we identify key open problems worthyof further study