Beyond‐line‐of‐sight (BLOS) communication is a fundamental component of modern defense strategies, enabling secure and reliable information exchange in environments where traditional line‐of‐sight (LOS) methodologies are ineffective due to obstructions or operational constraints. This article explores the key technologies advancing BLOS communication, with a focus on nanonetworks, aerial relays, and satellite‐based defense communication. To underscore the real‐world applications and challenges of BLOS systems, we present two practical case studies, one examining tropospheric ducting for BLOS maritime communication, highlighting its advantages and implementation challenges, and another on UAV path planning in radar‐threat war zones, demonstrating the role of optimization techniques in enhancing operational efficiency. Furthermore, this study identifies several critical future research directions in BLOS defense communication, including resilience enhancement, heterogeneous network integration, contested spectrum management, advancements in multimedia communication, adaptive methodologies, and the expanding domain of the Internet of Military Things (IoMT). Addressing both technological advancements and real‐world constraints, this research lays a foundation for strengthening BLOS communication systems, fostering cross‐sector collaboration, and driving innovation at the intersection of defense, academia, and industry.

The emerging field of smart agriculture leverages the power of the Internet of Things (IoT) to revolutionize farming practices. This article investigates the transformative potential of long range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems. By critically reviewing existing literature, we identify a lacuna in research specifically focused on LoRa’s prospects and challenges from a communication perspective in smart agriculture. We delve into the details of LoRa-based agricultural networks, encompassing network architecture design, physical layer (PHY) considerations tailored to the agricultural environment, and the development of channel modeling techniques that account for unique soil characteristics. This article further explores relaying and routing mechanisms that address the challenges of extending network coverage and optimizing data transmission in vast agricultural landscapes. Transitioning to practical considerations, we discuss sensor deployment strategies and energy management techniques, providing valuable insights for real-world deployments. A comparative analysis of LoRa with other prevalent wireless communication technologies employed in agricultural IoT applications highlights its strengths and weaknesses within this specific context. Furthermore, this article outlines several future research directions to leverage the potential of LoRa-based agriculture 4.0. These include advancements in channel modeling for heterogeneous farming environments, developing novel relay routing algorithms, integrating emerging sensor technologies like hyper-spectral imaging and drone-based sensing, on-device artificial intelligence (AI) models, and sustainable solutions. This survey can serve as a cornerstone for researchers, technologists, and practitioners seeking to understand, implement, and propel smart agriculture initiatives utilizing LoRa technology.

Recently, the research community has recently shown overwhelming interest in metaverse-enabled wireless devices, due to their compelling proactive learning and self-sustainability attributes. Proactive learning enables machine learning models to be trained before user requests, while self-sustainability allows a system to function with the least amount of assistance from network administrators/users. Because of these features, one can use metaverse to enable various applications (e.g., entertainment and collision avoidance) in intelligent transportation systems. However, the limitations of computing processing power (e.g., in autonomous cars) and communication resources make implementing metaverse-empowered vehicular networks challenging. Motivated by these facts, we present a new framework for cooperative sensing, communication, learning, and task offloading for vehicular networks enabled by the metaverse. Subsequently, we formulate a cost-function minimization problem that accounts for transmission energy and transmission latency. The cost is minimized by optimizing task offloading, wireless resource distribution, transmit power allocation, and sensing interval. We employ a decomposition-based strategy for simultaneous resource allocation, task offloading, sensing interval optimization, and transmit power allocation. Due to the combinatorial nature of the resource allocation and task offloading problems, matching-based solutions are used. For sensing interval optimization, convex optimization is used. On the other hand, due to the non-convex and continuous nature of the transmit power allocation problem, a proximal term is introduced into the objective function to approximate it as convex objective function, which is then solved using a convex optimizer. To gain further insights, the proposed scheme is supported by extensive numerical results.

Underwater Acoustic Internet of Things Networks (UAIoTNs) can furnish excellent technical support and information services for applications involving marine observation and detection, marine disaster prevention and mitigation, and maritime search and rescue, in which accurate positioning information is the fundamental requirement. The combination of high dynamics and complexity of the ocean environment to the high latency and narrowband of underwater acoustic communication are complex challenges in UAIoTNs. Due to these facts, this work investigates the received signal strength (RSS)-based three-dimensional (3D) target localization in UAIoTNs taking into account the absorption effect, uncertain transmission power (UTP), and a time-varying Path Loss Exponent (PLE). Through Taylor’s first-order expansion and certain approximations, we envision the underwater stratified acoustic propagation localization challenge as an Alternating Non-negative Constrained Least Squares (ANCLS) framework. To address the challenges posed by unknown multi-parameters, a robust coarse-to-fine localization algorithm (RCFLA) is proposed. At first, the coarse localization phase utilizes the Active Set Method (ASM), while the subsequent fine localization one employs the improved Broyden-Fletcher-Goldfarb-Sanno (BFGS) trust region method to enhance convergence towards the global optimal solution. The iterative process refines the underwater target location, UTP, and PLE, using the ASM-derived rough solution as the initial estimate. Analysis of computational complexity and derivation of the Cramér-Rao Lower Bound (CRLB) with stratified propagation and absorption effect demonstrates the superiority of RCFLA. Furthermore, Lyapunov’s second stability theorem is used to prove the stability of the RCFLA and presents a complete proof of global convergence. Numerical simulation and experimental results validate the algorithm’s optimal localization accuracy across various scenarios, showing reduced overhead compared to benchmark algorithms.

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.

Underwater monitoring and exploration benefit from Internet of Underwater Things (IoUT). However, the lifetime of IoUT networks is limited due to batteries that require frequent replacement, which is costly and unfeasible in a hostile environment. To maximize IoUT device lifetimes and reduce system costs, we propose on-demand wake-up radios, activated by wake-up calls from deployed surface buoys via acoustic, optical, and magnetic induction communication. Using stochastic geometry tools, we analyze the wake-up scheme’s performance, deriving analytical solutions for success and false wake-up probabilities. We characterize the scheme’s performance under different design parameters and highlight its benefits.

This study investigates the secrecy performance of a rate-splitting multiple-access system inspired by cognitive radio principles, where the primary user takes on the role of a potential eavesdropper seeking to intercept the secondary user's message. The on-off scheme is utilized to guarantee the transmission of the primary user. We provide closed-form expressions for critical metrics, including the transmission probability of the system under consideration, the secrecy outage probability, and the reliable outage probability of the cognitive user. Monte Carlo simulation results are presented to substantiate our findings and explore the impact of system parameters, such as transmit power and the number of antennas at the base station, on system performance.

Cooperative nonorthogonal multiple access (NOMA) technology has the potential to greatly extend network coverage and elevate overall system performance. The NOMA-based coordinated direct and relay transmission (CDRT) scheme takes full advantage of successive interference cancellation results to effectively reduce interference from forward signals and improve the efficiency of parallel link transmission. However, the existing literature has overlooked an important premise of the CDRT system: the center user must successfully decode the signal for the edge user. In this work, new adaptive transmission schemes are proposed to enhance the performance of NOMA-based CDRT systems. Specifically, dynamic power allocation and maximum ratio transmission schemes are utilized to improve the transmission quality of the first hop, and directional antenna technology is utilized to enhance the transmission quality of the second hop. The closed-form expressions for the exact effective sum throughput are derived. Finally, simulation results are presented to validate the correctness of the theoretical analysis and these results demonstrate the effectiveness of the proposed scheme when compared to benchmark schemes.

Underwater acoustic Internet of Things Networks (UAIoTNs) play a crucial role in oceanographic and environmental monitoring, necessitating precise localization for optimal functionality. However, the underwater setting introduces significant challenges, encompassing the stratification effect arising from underwater heterogeneity, uncertainty in anchor positions due to currents, and variations in the signal transmission environment. These factors collectively impede the accurate estimation of location. Consequently, this paper addresses these challenges by analyzing and deriving a closed-form solution using a time-of-arrival (TOA)-based technique for 3D localization in UAIoTNs. The investigation establishes an underwater stratified propagation model, drawing inspiration from ray tracing theory and Snell’s law. Employing the Cramér-Rao lower bound (CRLB) framework, we explore scenarios both with and without considering perturbed anchors, utilizing the Banachiewicz-Schur theorem. To quantify the impact of the stratification effect and perturbed anchors on CRLB and mean square error (MSE), we further analyze and derive an MSE expression, employing Taylor-series linearization. Building on our analysis of the detrimental effects of stratification and inaccurate anchors, we introduce a multiple-weighted least squares (MWLS) algorithm to alleviate potential performance losses. This approach integrates a matrix operator in the update step, eliminating variable dependencies and resulting in a closed-form solution that circumvents the need for iterative processes. Our simulation results validate our analytical findings and demonstrate the effectiveness of the proposed method, showcasing improved localization accuracy across various scenarios when compared to state-of-the-art approaches.

Water covers 71% of the Earth’s surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and space networks. This article presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. This article first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, this article presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of Things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Internet of Underwater Things (IoUT) is a technological revolution that could mark a new era for scientific, industrial, and military underwater applications. To mitigate the hostile underwater channel characteristics, this article considers a multimodal underwater network that hybridizes acoustic and optical wireless communications to achieve an ubiquitous control and high-speed low-latency networking performance, respectively. Since underwater optical wireless communications (UOWCs) suffer from limited range, it requires effective multihop routing solutions. In this regard, we propose a sector-based opportunistic routing (SectOR) protocol. Unlike the traditional unicast routing (TUR) techniques, which send packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in TUR. Contingent upon the performance characterization of a single-hop link, we obtain a variety of local and global metrics to evaluate the fitness of a candidate set (CS) and develop candidate prioritization techniques for various OR metrics. Since rate$\leftrightarrow $error and range$\leftrightarrow $beamwidth tradeoffs yield different CS diversities, we develop a candidate filtering and searching algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that the SectOR protocol can perform even better than optimal unicast routing protocols in well-connected underwater networks.

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor's location have been proposed. However, the optimization of the anchors' location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.

On the contrary of low speed and high delay acoustic systems, underwater optical wireless communication (UOWC) can deliver a high speed and low latency service at the expense of short communication ranges. Therefore, multihop communication is of utmost importance to extend the range, improve degree of connectivity, and overall performance of underwater optical wireless networks (UOWNs). In this regard, this paper investigates relaying and routing techniques and provides their end-to-end (E2E) performance analysis under the location uncertainty. To achieve robust and reliable links, we first consider adaptive beamwidths and derive the divergence angles under the absence and presence of a pointing-acquisitioning-and-tracking (PAT) mechanism. Thereafter, important E2E performance metrics (e.g., data rate, bit error rate, transmission power, amplifier gain, etc.) are obtained for two potential relaying techniques; decode & forward (DF) and optical amplify & forward (AF). We develop centralized routing schemes for both relaying techniques to optimize E2E rate, bit error rate, and power consumption. Alternatively, a distributed routing protocol, namely Light Path Routing (LiPaR), is proposed by leveraging the range-beamwidth tradeoff of UOWCs. LiPaR is especially shown to be favorable when there is no PAT mechanism and available network information. In order to show the benefits of multihop communications, extensive simulations are conducted to compare different routing and relaying schemes under different network parameters and underwater environments.

The superiority of optical communications in underwater mediums, in terms of higher data rate and reliability, makes underwater optical wireless communications (UOWC) more favorable to provide ultra-reliable low-latency underwater communications, as compared to other wireless technologies, e.g., acoustic and radio frequency (RF) communications. UOWC limited transmission range, however, remains a major hurdle against assessing its true deployment benefits, which motivates for the necessity of developing practical routing protocols for multi-hop underwater optical wireless sensor networks (UOWSNs). This paper sheds light on the existing state-of-art UOWC routing protocols, the majority of which requires centralized implementation with large end-to-end delay. The article further proposes routing algorithms which can be implemented in a distributed fashion across the multi-hop links, with a reasonable amount of information exchange. The merits of the proposed algorithms are particularly highlighted through illustrative simulations, which show how the proposed strategies outperform the classical protocols, both in terms of reliability and end-to-end latency. Finally, the paper shows how the proposed distributive routing protocols achieve ultra-reliable low-latency underwater communications.

Underwater optical wireless communications (UOWC) is an emerging technology to provide underwater applications with high speed and low latency connections. However, it suffers from limited range and requires effective multi-hop routing solutions for the proper operation of underwater optical wireless networks (UOWNs). In this regard, this paper proposes a distributed Sector-based Opportunistic Routing (SectOR) protocol. Unlike the traditional routing techniques which unicast packets to a unique relay, opportunistic routing (OR) targets a set of candidate relays by leveraging the broadcast nature of the UOWC channel. OR is especially suitable for UOWNs as the link connectivity can be disrupted easily due to the underwater channel impairments (e.g., pointing errors, misalignment, turbulence, etc.) and sea creatures passing through the transceivers' line-of-sight. In such cases, OR improves the packet delivery ratio as the likelihood of having at least one successful packet reception is much higher than that in conventional unicast routing. Contingent upon the performance characterization of a single-hop link, we obtain distance progress (DP) and expected (DP) metrics to evaluate the fitness of a candidate set (CS) and prioritize the members of a CS. Since rate↔error and range↔beamwidth tradeoffs yield different candidate set diversities, we develop a candidate selection and prioritization (CSPA) algorithm to find the optimal sector shaped coverage region by scanning the feasible search space. Moreover, a hybrid acoustic/optic coordination mechanism is considered to avoid duplicate transmission of the relays. Numerical results show that SectOR protocol can perform even better than an optimal unicast routing protocol in well-connected UOWNs.

Location is one of the basic information required for underwater optical wireless sensor networks (UOWSNs) for different purposes, such as relating the sensing measurements with precise sensor positions, enabling efficient geographic routing techniques, and sustaining link connectivity between the nodes. Even though various 2-D UOWSNs' localization methods have been proposed in the past, the directive nature of optical wireless communications and 3-D deployment of sensors require to develop 3-D underwater localization methods. Additionally, the localization accuracy of the network strongly depends on the placement of the anchors. Therefore, we propose a robust 3-D localization method for partially connected UOWSNs, which can accommodate the outliers and optimize the placement of the anchors to improve the localization accuracy. The proposed method formulates the problem of missing pairwise distances and outliers as an optimization problem, which is solved through half quadratic minimization. Furthermore, analysis is provided to optimally place the anchors in the network, which improves the localization accuracy. The problem of optimal anchor placement is formulated as a combination of Fisher information matrices for the sensor nodes where the condition of D-optimality is satisfied. The numerical results indicate that the proposed method outperforms the literature substantially in the presence of outliers.

Underwater optical wireless links have limited range and intermittent connectivity due to the hostile aquatic channel impairments and misalignment between the optical transceivers. Therefore, multi-hop communication can expand the communication range, enhance network connectivity, and provide a more precise network localization scheme. In this regard, this paper investigates the connectivity of underwater optical wireless sensor networks (UOWSNs) and its impacts on the network localization performance. First, we model UOWSNs as randomly scaled sector graphs where the connection between sensors is established by point-to-point directed links. Thereafter, the probability of network connectivity is analytically derived as a function of network density, communication range, and optical transmitters' divergence angle. Second, the network localization problem is formulated as an unconstrained optimization problem and solved using the conjugate gradient technique. Numerical results show that different network parameters such as the number of nodes, divergence angle, and transmission range significantly influence the probability of a connected network. Furthermore, the performance of the proposed localization technique is compared to well-known network localization schemes and the results show that the localization accuracy of the proposed technique outperforms the literature in terms of network connectivity, ranging error, and number of anchors.

This paper proposes a received signal strength (RSS)-based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs, energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy. Network localization is carried out by measuring the RSSs of active nodes, which are modeled based on the underwater optical communication channel characteristics. Thereafter, block kernel matrices are computed for the RSS-based range measurements. Unlike the traditional shortest-path approach, the proposed technique reduces the estimation error of the shortest path for each block kernel matrix. Once the complete block kernel matrices are available, a closed form localization technique is developed to find the location of every optical sensor node in the network. An analytical expression for the Cramer-Rao lower bound is also derived as a benchmark to evaluate the localization performance of the developed technique. The extensive simulations show that the proposed framework outperforms the well-known network localization techniques.

In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization.

Underwater optical wireless networks (UOWNs) have recently gained attention as an emerging solution to the growing demand for broadband connectivity. Even though it is an alternative to low-bandwidth and high-latency acoustic systems, underwater optical wireless communications (UOWC) suffers from limited range and requires effective multi-hop solutions. Therefore, this paper analyzes and compares the performance of multihop underwater optical wireless networks under two relaying schemes: Decode & Forward (DF) and Amplify & Forward (AF). Noting that nodes close to the surface sink (SS) are required to relay more information, these nodes are enabled for retro-reflective communication, where SS illuminates these nodes with a continuous-wave beam which is then modulated and reflected back to the SS receivers. Accordingly, we analytically evaluate important performance metrics including end-to-end bit error rate, achievable multihop data rates, and communication ranges between node pairs. Thereafter, we develop routing algorithms for DF and AF schemes in order to maximize the end-to-end performance metrics. Numerical results demonstrate that multi-hop transmission can significantly enhance the network performance and expand the communication range.

In cognitive radio networks, secondary users (SUs) can share spectrum with primary users (PUs) under the condition that no interference is caused to the PUs. To evaluate the interference imposed to the PUs, the cognitive systems discussed in the literature usually assume that the channel state information (CSI) of the link from a secondary transmitter to a primary receiver (interference link) is known at the secondary transmitter. However, this assumption may often be impractical in cognitive radio systems, since the PUs need to be oblivious to the presence of the SUs. The authors first discuss PU localisation and then introduce an uplink resource allocation algorithm for orthogonal frequency division multiple access‐based cognitive radio systems, where relative location information between primary and SUs is used instead of CSI of the interference link to estimate the interference. Numerical and simulation results show that it is indeed effective to use location information as a part of resource allocation and thus a near‐optimal capacity is achieved.

Recent advancements in micro electro mechanical system and wireless communication have made it possible to use wireless sensor nodes for many real world applications. Wireless sensor nodes are widely been used now a days in many tracking and monitoring problems such as vehicle tracking, environmental and health monitoring. It is one of the most challenging problems to find the geographic locations of nodes given that proximity information only between the nodes. There are large number of localization algorithms, each having different approach to find the final location. Multidimensional Scaling (MDS) is one of that technique which works in more robust fashion for noise sparse networks, even with less number of anchor nodes. The proposed work addresses the problem of location discovery of nodes based on MDS and Levenberg-Maurquardt (LM) method. In this paper we used MDS to find the initial estimated position of nodes and proposed Levenberg-Maurquardt as a refinement algorithm for final position. Our simulation results show that the proposed scheme is very competitive compared to other common schemes.