With a diverse range of applications in both private and public sector, the market value of Unmanned Aerial Vehicles (UAVs) skyrocketed to 17.31 billion USD in 2024 and is expected to reach 32.95 billion USD by 2030. 3GPP considered UAVs in Release 15 and will include them in Release 18 as part of the 5G-Advanced technology. To provide smooth and safe inclusion in the current airspace, fast and reliable communications between UAVs and between ground base stations and UAVs are needed. Several research studies have been conducted, but there is no comprehensive picture of their advancements in terms of wireless communications and networking for UAVs. This survey paper provides detailed information on the current status of UAV communications and networking, including important aspects such as architectural solutions, protocols and design options related to spectrum management, resource optimization and security requirements. Efforts related to standardization and integration with different applications are also covered. The survey will help researchers and practitioners in the field of wireless communications, UAV service provision and telecommunications learn about the current state of the art and open the door to future research and development avenues in their fields.

Accurate mapping of soil organic carbon (SOC) in intensive croplands is important for climate change mitigation and for guiding sustainable agricultural management. Despite the growing use of Sentinel-2 composites, evidence remains limited on how composite design affects SOC mapping accuracy in croplands and on whether satellite observations can capture management-relevant signals linked to SOC. This study compared four temporal Sentinel-2 spectral composites for SOC mapping using LUCAS 2015 and 2018 observations in Italy’s Po Plain. Three machine learning models, random forest, XGBoost, and CatBoost, were trained, and SHAP was used to interpret variable contributions. Across models, composites targeting the bare soil period, based on multispectral reflectance and non-photosynthetic vegetation indices, achieved the best performance. CatBoost performed best and produced a high-resolution SOC map for the Po Plain. In contrast, traditional vegetation indices such as NDVI and EVI showed limited relevance across all composites. Importantly, we found a robust negative association between SOC and bare soil frequency derived from multi-temporal Sentinel-2 observations, with lower bare soil frequency corresponding to higher SOC. This highlights bare soil exposure duration as a practical indicator for monitoring and suggests that management practices that shorten bare soil windows may help maintain or enhance SOC. Overall, this study optimized Sentinel-2 temporal composites with machine learning to improve SOC mapping in the Po Plain and provides actionable insights for cropland management in intensively cultivated regions.

LoRaWANs, a widely accepted IoT connectivity solution, adopt a simple (ALOHA-like) MAC layer, enabling low-power communication at the cost of scalability due to packet collisions. Hence, current studies on LoRaWAN conclude that the network does not support dense deployments. Several alternative MACs are proposed but they stumble upon well-known limitations: time division eliminates the asynchrony of LoRa nodes but requires feedback from the gateways; carrier-sensing-based protocols are heavily constrained by the reduced sensing ranges of the devices, thus creating a large number of hidden terminals, leading to collisions. To enhance LoRaWAN to cater to both low- and high-density deployments, in this paper, we propose Spreading Factor MAC (SFMAC), a novel, practical, distributed, and energy-efficient MAC protocol. SFMAC, a channel-sensing-based MAC, takes an unconventional approach to eliminate hidden terminals - by operating with pairs of SFs, wherein the higher SF is used for channel sensing and the lower for data transmission. Bleeps are transmitted in the higher SF as they can be sensed at longer ranges. SFMAC does not require any change in hardware or the LoRaWAN protocol. We demonstrate that the fundamental tradeoff made by SFMAC - utilizing two SFs per data transmission instead of using all for data - works extremely well due to the elimination of hidden terminals. Through real-world experiments on 30 SX1261 devices and data-driven ns-3 simulations, we showcase that SFMAC increases goodput and channel utilization by manifolds over state-of-the-art protocols such as p-CARMA, np-CECADA, and LMAC.

Haptic teleoperation is a promising technology with applications in telemaintenance and disaster management. However, it faces significant challenges when the application is subjected to a high network latency and environments with moving objects. This work aims to extend Model Mediated Teleoperation (MMT) to overcome challenges in supporting dynamic environments. Instead of striving for perfect model alignment, we acknowledge the inevitable mismatch between the remote environment and its model at the operator. We propose a set of design principles and an accompanying framework for designing MMT solutions that prioritize operator intent. Our approach is exemplified through an application where an operator, located 8000 km away (The Netherlands - India) and subjected to an average of 179 ms end-to-end latency, guides a robot arm to draw on a whiteboard whose position is actively altered. We evaluate the effectiveness of our approach through a user study. We show a 3-point improvement on a 7-point Likert scale when users utilize our approach to teleoperate over significant network latency of up to 1 s.

The efficient execution of inferences at the edge is becoming increasingly critical for communication systems that are expected to provide users with fast and accurate mobile data analytics. These inference tasks are inherently latency-sensitive and computationally demanding, whereas edge nodes are limited by energy budgets and heterogeneous resources. This article studies how a set of edge nodes can collaborate in executing demanding streaming inference tasks to optimize their aggregate performance. Such collaborative task exchange schemes enable the sharing of scarce computing resources and machine learning (ML) models (which perform the inferences) and constitute a scalable approach to this intricate problem. We formulate this exchange process as an online convex optimization (OCO) problem and design a dynamic task assignment algorithm, which is proven to have optimality guarantees even when the network and service parameters (resources and task properties) are unknown and vary arbitrarily over time. The algorithm aims to maximize inference accuracy while minimizing overall task latency and energy (including for data transfers) and simultaneously ensures that collaborating nodes do not suffer imbalanced energy costs. Through a series of data-driven experiments, we quantify the cooperation benefits under different weight combinations and validate the convergence and adaptability of the proposed learning algorithm across diverse conditions, including variations of system parameters, as well as heterogeneity across nodes and tasks.

We present HUM-High-frequency UAV Messaging: an acoustic side channel communication system we design for localized drone-to-drone communications. We generate Pulse Width Modulated (PWM) signals from drone motors to carry information and improve communication reliability by mitigating propeller noise interference through modifications to the propeller's physical design. These modifications reduce propeller noise in the designated acoustic spectrum by up to 7 dB. We deploy a custom ultrasonic microphone shield specifically designed for decoding in the receiver. HUM's improved signal-to-noise ratio enables up to 80x higher data rates compared to the existing design from the literature while providing better scalability. HUM supports simultaneous decoding across 16 drones within 8 m, range as seen in real flight tests. The cost of this performance is minimal; we experimentally demonstrate that HUM has a marginal impact on flight dynamics and battery life.

Smart autonomous vehicles can cooperatively drive as platoons offering benefits like enhanced safety, traffic efficiency, and fuel conservation. While traditionally platoons have followed a single-lane, train-like structure they face challenges when scaling that include communication range limitations and lane-change difficulties. In this article, we propose a new paradigm of multi-lane platoons that spreads platoons across multiple lanes. We explore the characteristics of multi-lane platoons particularly focusing on communication parameters. Additionally, we propose a cross-layer mechanism to seamlessly integrate this concept within the existing communication standard, ETSI. Our work significantly enhances platoon communication performance in mixed traffic scenarios and we propose optimizations to improve its effectiveness.

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

The increasing popularity of helium-assisted blimps for extended monitoring or data collection applications is hindered by a critical limitation-single-point failure when the balloon malfunctions or bursts. To address this, we introduce Janus, a hybrid blimp-drone platform equipped with integrated balloon failure detection and recovery capability. Janus employs a triggered mechanism that seamlessly transitions the platform from a blimp to a standard quad-rotor drone. Utilizing multiple sensors and fusing their readings, we have developed a robust balloon failure detection system. Janus demonstrates omnidirectional mobility in blimp mode and transitions promptly into quadrotor mode upon receiving the signal. Our results affirm the successful recovery of the system from balloon failure, with a rapid response time of 66 ms to balloon failure detection. The drone morphs into a quadrotor and achieves recovery within 0.362 seconds in 90% of cases. By amalgamating the enduring flight capabilities of blimps with the agility of quad-rotors within a morphing platform like Janus, we cater to applications demanding both prolonged flight duration and enhanced agility.

This chapter presents a summary of the description and preliminary results of the use case related to the implementation of artificial intelligence tools in the emerging technology called wireless avionics intra-communications (WAICs). WAICs aims to replace some of the cable buses of modern aircraft. This replacement of infrastructure leads to: (1) complexity reduction of future airplanes, (2) creation of innovative services where wireless links are more flexible than wireline links, and mainly (3) a considerable weight reduction, which in turn leads to fuel consumption efficiency, increase of payload, as well as range extension. Therefore, WAICs is expected to have a large impact on the aeronautics industry, propelling a new generation of greener, more efficient, and less expensive aeronautical services. However, there are still several reliability, trust, interoperability and latency issues that need to be addressed before this technology becomes commercial. It is expected that AI will boost the applicability of this technology, contributing to the realization of the concept of “fly-by-wireless”.