The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

Direct optical communication between underwater and aerial nodes such as drones offers a transformative approach for accessing underwater devices and sensors in shallow water environments. Unlike traditional underwater communication systems, which rely on buoys or surface vehicles to relay data between underwater devices and ground stations. Direct waterair optical communication enables greater operational flexibility and cost efficiency by utilizing drones to directly collect data or transmit commands. However, a significant challenge in implementing such systems is their limited reliability and short operational range, typically restricted to a few meters in both water and air. To address this limitation, this paper proposes and implements a LoRa-based modulation scheme for optical water-air communication using software-defined radio (SDR) technology. LoRa, a widely recognized radio frequency (RF) technology, is specifically designed for energy-efficient, low-rate communication, making it a promising candidate for enhancing the reliability of optical links. This paper presents a systematic approach to optimizing system parameters to improve link reliability across various scenarios. The experimental results demonstrate that the implementation of optical LoRa-based modulation offers a significant improvement in performance, achieving a gain of 7 dB to 15 dB compared to traditional Binary Phase Shift Keying (BPSK) modulation. This substantial gain underscores the effectiveness of LoRa-based modulation in enhancing the reliability and robustness of optical water-air communication links.

The common and well-accepted architectures for monitoring underwater (UW) environments involve UW drones for collecting information and fixed buoys to relay the information outside the water. Fixed buoys lead to some disadvantages, such as their high cost and limited coverage of UW devices. To address these problems, new architectures are considered in the literature, where the buoys are eliminated and instead aerial drones are used for collecting data from the UW ones. These architectures potentially lead to improved and more flexible UW monitoring and control. The existing literature for these novel architectures includes some channel models for optical wireless communication through the water-air (W-A) interface. The primary drawback of these models lies in the fusion of the received rays. Furthermore, the mathematical analysis is mostly carried out in 2D and then extended to 3D, leading to reduced modeling accuracy. In this paper, an accurate 3D ray-tracing solution is proposed for channel modeling and simulation of communication through W-A, where the rays observed at the receiver through multi-path are properly combined. Numerical analysis reveals insights and suggestions on optical channel gain variation over time, and also some conclusions on the overall communication system design, such as the best depth of UW devices and the best height of aerial drones.

In this paper, a Universal Underwater Software Defined Modem (UniSDM) architecture is proposed that may operate in different modes (acoustic, magnetic induction, optical and RF), in order to utilize the advantages of each mode and accordingly satisfy the requirements of many latest use cases in underwater communication systems. A detailed description of the novel UniSDM architecture is presented first. The novelty of this architecture is its flexibility, i.e., allowing the designers to produce a device that may include any type of modes operating seamlessly and jointly by exchanging data, control and synchronization. Many challenges, including high system costs and coordination between different modes, are addressed in the paper. Moreover, numerical evaluation is conducted to assess the performance of the proposed UniSDM architecture. Finally, the performance evaluation shows that the utilization of the UniSDM allows to decrease the transmission latency and improve the energy efficiency, while maintaining high reliability and robustness in underwater communication systems.

In recent years, many underwater communication applications have been proposed and tested, leading to the Internet of underwater things (IoUT) concept. In the IoUT, sensors may be deployed individually on sea surface and seabed as well as in water at different depths. They also may be integrated into underwater items such as fish, plants, autonomous underwater vehicles (AUVs), remotely operated underwater vehicles (ROVs), and divers. Depending on the application, different connectivity requirements must be satisfied such as data rate, latency, and reliability. However, the underwater communication channels pose a significant challenge to meet these requirements. To accommodate various applications with different service level agreements (SLAs), a multimode (acoustic, optical, and magnetic induction (MI)) communication system is proposed to take advantage of the modes' complementary features. Furthermore, an automatic network slicing (ANS) solution is proposed to provide globally optimized resource management, enhanced quality of service (QoS), simplified network operation, reduced deployment cost, and functional isolation of services. Taking the channel characteristics of the acoustic, optical, and MI communication modes into consideration, an optimization problem is formulated and solved for multimode IoUT (MM-IoUT) to enable admission control, routing, and dynamic resource allocation based on the different SLAs. Numerical analysis is conducted to verify the proposed solution and evaluate its performance.

The challenge of wildfire management and detection is recently gaining increased attention due to the increased severity and frequency of wildfires worldwide. Popular fire detection techniques, such as satellite imaging and remote camera-based sensing suffer from late detection and low reliability while early wildfire detection is a key to prevent massive fires. In this article, we propose a novel wildfire detection solution based on unmanned aerial vehicles assisted Internet of Things (UAV-IoT) networks. The main objective is to: 1) study the performance and reliability of the UAV-IoT networks for wildfire detection and 2) present a guideline to optimize the UAV-IoT network to improve fire detection probability under limited system cost budgets. We focus on optimizing the IoT devices’ density and the number of UAVs covering the forest area such that a lower bound on the wildfires detection probability is maximized within a limited time and system cost. At any time after the fire ignition, the IoT devices within a limited distance from the fire can detect it. These IoT devices can then report their measurements to nearby UAVs. Discrete-time Markov chain (DTMC) analysis is utilized to compute the fire detection and false-alarm probabilities. Numerical results suggest that given enough system cost, the UAV-IoT-based fire detection can offer a faster and more reliable wildfire detection solution than state-of-the-art satellite imaging techniques.

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs' assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the end-to-end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.

The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. Most of these devices are expected to be of low-complexity, low-cost, and limited power supply, which imposes stringent constraints on the network operation. In this regard, this paper investigates aerial data aggregation and field estimation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, a UAV flies over the field and collects the required data for two prominent missions: data aggregation and field estimation. To accomplish these tasks, the field of interest is divided into several subregions, over which the UAV hovers to collect samples from the underlying nodes. To this end, we formulate and solve an optimization problem to minimize the total hovering and traveling time of each mission. While the former requires the collection of a prescribed average number of samples from the field, the latter ensures, for a given field spatial correlation model, that the average mean-squared estimation error of the field value is no more than a predetermined threshold at any point. These goals are fulfilled by optimizing the number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. The proposed formulation is shown to be NP-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times, such that the total time for collecting the required samples is minimized.

The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. The majority of these devices are expected to be of low-complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. In this regards, this paper proposes aerial data aggregation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, an UAV flies over the field and collects the required data. Particularly, the field is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, an optimization problem is formulated and solved to find the optimal number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations such that an average number of samples are collected from the field in minimal time. The proposed formulation is shown to be np-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times such that the total time for collecting the required samples is minimized.