Abstract: The problem of Reduced Capability (RedCap) User Equipment (UE) positioning within indoor 5G networks is addressed. While conventional approaches rely on time-domain ranging, the limited signal bandwidth associated with RedCap devices often prevents these methods from satisfying stringent accuracy requirements. As an alternative, this paper proposes a positioning framework based on Angle-of-Arrival (AoA) measurements. The framework incorporates a low-complexity AoA estimation algorithm derived from the analysis of the spatial covariance matrix. This procedure inherently generates a link quality metric which, alongside the AoA estimate, is utilized for final UE localization. The proposed localization algorithm belongs to the class of Weighted Least Squares (WLS) estimators and provides a unified approach to UE positioning in both 2D and 3D physical space. Simulation results demonstrate the effectiveness of the proposed framework under the challenging high-multipath conditions inherent to 5G indoor deployments.

Abstract: Mobile network operators are increasingly adopting 5G Fixed Wireless Access (FWA) to meet the growing demand for high performance services in households. This study evaluated the adoption and Quality of Experience (QoE) of 5G FWA through a multi-phase study. First phase, utilized a systematic literature review to develop a structural equation modeling (SEM) framework, identifying Quality of Service (QoS) and User Experience (UX) factors. A questionnaire survey was then conducted with 42 industry experts and 52 end-users. The SEM analysis shows that UX is not transferable between FTTx and 5G FWA, as the correlation (y = - 0.052, t value = -0.100) was statistically insignificant. The technical QoS FTTx does not influence how users perceive the technical QoS 5G FWA (y = - 0.02, t value = -0.122). Bandwidth and Quality are the most critical drivers for 5G FWA success regarding UX, whereas latency, MoS, and throughput are vital for QoS. Exploratory Factor Analysis for the UX and QoS parameters of 5G FWA showed strong internal consistency across all identified factors. The framework with fit indices reflected excellent model QoS (RMSEA = 0.08, CFI = 0.973, TLI = 0.965, CMINDF = 1.254 and GFI = 0.782) and UX (RMSEA = 0.08, CFI = 0.895, TLI = 0.881, CMINDF = 1.377 and GFI = 0.655). The mathematical SEM model provides empirical evidence of the role of the service factor as observed parameters and introduces a validated theoretical framework QoE-SEM. This research contributes to the academic and telecommunications industries, to deliver a fit observe model for upcoming new technology 5G FWA and assist decision makers in formulating strategic QoE models.

Abstract: UAV-based Multi-Access Edge Computing (MEC) systems are vital solutions in disaster scenarios by providing temporary radio and processing resources to rescue teams and survivors. However, recent schemes in the literature typically treat offloaded tasks as one indivisible units and fail to account for heterogeneous reliability requirements, leading to non-optimal resource utilization and performance deterioration under such emergency conditions. Moreover, the joint optimization of communication, computation, and UAV path planning in cooperative setup remains inadequately addressed. This paper proposes a priority-aware layered task offloading framework for cooperative UAV-MEC networks based on Superposition Coding (SPC) and non-orthogonal access. The proposed design separates tasks into reliability-critical base-layer (BL) and enhancement-layer (EL) components, to assure reliable and timely transmission and execution. BL data is prioritized via intra- and inter-user constraints, while EL data is adaptively processed locally or offloaded via a cooperative UAVs. A joint latency minimization problem is formulated and tackled using an alternating optimization framework with successive convex approximation (AO-SCA). Simulation results demonstrate that the proposed scheme significantly outperforms baseline methods. For 20 users, it achieves a processing efficiency of 92.3%, compared to over 83% for baseline schemes. As the number of users increases to 120, the proposed method maintains superior efficiency at 63.1%, outperforming NC-SPC (40.3%), FT-Coop (51.3%), SL-NOMA (52.4%), and L-OMA (45.3%), highlighting its robustness and scalability in meeting reliability and low-latency requirements in post-disaster scenarios.

Abstract: Heterogeneous sensor systems generate measurements in incompatible physical units, which complicates their direct integration with photonic stochastic processors. This study proposes a universal edge frontend that converts heterogeneous sensor channels into unified event-oriented probabilities and then into Bernoulli bitstreams compatible with polarization-encoded optical interfaces. The framework combines sensor-to-probability mapping, weighted event-level fusion, stochastic bitstream generation, and system-level control of correlation and synchronization. Its performance was investigated through reproducible Colab-based modeling using baseline validation, weighting-strategy comparison, static and time-varying decorrelation/synchronization studies, and robustness/scaling analysis. The results show that the stochastic event estimate converges toward the float reference with increasing bitstream length, reliability-aware weighting outperforms equal and tested data-driven weighting in the benchmark, independent stream generation provides the best inference quality, and synchronization mismatch becomes measurable in time-varying fusion. The frontend also demonstrates graceful degradation under channel corruption and favorable scaling under mixed informative, weak, redundant, conflicting, and noisy channel configurations. These findings indicate that heterogeneous sensors can be interfaced with photonic stochastic systems through a common event-level representation and that weighting, decorrelation, synchronization, and robustness must be treated as core frontend design variables.

Abstract: UAV-assisted data collection often suffers from spatial data holes and communication unfairness, a challenge exacerbated in Wireless Powered Communication Networks (WPCNs) by the inherent doubly near-far problem. To bridge these gaps, this paper proposes a novel Spatio-Temporal Trajectory-Driven Dynamic Time-Division Multiple Access (STD-TDMA) scheduling strategy. Deviating from conventional discrete hovering paradigms, we introduce a continuous-flight framework that exploits the UAV's mobility to provide seamless spatial coverage. By jointly optimizing the UAV's flight speed and dynamic time-slot allocation, the proposed strategy ensures that each sensor node can interact with the UAV at its optimal channel condition along the trajectory, thereby effectively mitigating the doubly near-far effect and ensuring absolute nodal fairness. To solve the formulated non-convex optimization problem, we develop a low-complexity algorithm that integrates Binary Search for speed optimization with the Hungarian algorithm for spatio-temporal mapping. Extensive simulations demonstrate that our STD-TDMA strategy significantly enhances nodal fairness and boosts overall task execution efficiency compared to conventional baseline schemes.

Abstract: This article presents the design and analysis of microstrip MIMO antennas intended for operation in the 5G high-frequency band (High-Band). The proposed antenna structures include 2 and 4 element MIMO configurations operating in the millimeter-wave spec-trum with a center frequency of 38 GHz. The aim of the article was to develop compact antenna systems with performance parameters suitable for 5G mmWave applications, while addressing the limitations of microstrip technology. This article describes the development process of a single radiating element and its subsequent integration into multi-antenna structures. Particular attention is paid to impedance matching, port isolation, and mutual coupling mitigation, which represent key challenges in implementing MIMO antennas within the millimeter-wave band. Furthermore, the impact of the number of antenna elements on the radiation pattern, gain, and overall efficiency of the MIMO system is analyzed. The obtained results confirm that MIMO microstrip antennas in 2- and 4-element con-figurations can be an effective solution for 5G High-Band applications, providing ade-quate radio parameters while maintaining small dimensions and the ability to integrate with RF front-end systems. The presented solutions can be used in user terminals, CPE modules, and compact access stations of 5G systems operating in the millimeter-wave band.

Abstract: Mobile Reliable Opportunistic Routing (MROR) protocol improves the reliability in data forwarding in Cognitive Radio Sensor Networks (CRSNs) by mobility-conscious virtual contention groups and handover zoning. Regardless of its advantages, the problem-solving essence of heuristic decision-making in MROR is poor both in highly dynamic spectrum access and random node mobility. To address this shortcoming, we present DRR-MROR, which is a refined framework that incorporates Deep Reinforcement Learning (DRL) to provide smart routing, adaptive functionality. The users in DRAOMR are autonomous agents that are referred to as secondary users (SUs), and they constantly observe their own local state - including primary user activity, link quality, residual energy and neighbor mobility patterns. These agents acquire an ideal routing policy through a Deep Q-Network (DQN), optimised to expand the long-term network utility in throughput, delay, and energy efficiency. We define the routing problem as a Markov Decision Process (MDP) and use experience replay whereby prioritized sampling is used to guarantee convergence of learning. Extensive simulations show that DRL-MROR has better performance in comparison to the original MROR protocol and modern AI-based solutions (AIRoute) under various conditions. Our results show vast improvements: up to 38% increased throughput, 42% increased goodput, 29% decreased in energy consumed per packet, and about 18% improvement in network lifetime, all and at the same time ensuring high route stability and fairness. Also, the DRL-MROR minimizes control reduces both overhead by 30% and average end-to-end delay by 32% , maintaining high performance even when under stress at elevated PU rates and velocity of nodes. The transformation of the non-adaptive opportunistic routing to a cognitive and self-adaptative one can be successfully achieved by learning makes it compatible with the requirements of the next-generation IoT and smart infrastructure by making it more paradigm-driven.

Abstract: Reliable and resilient communication systems are indispensable for first responders, enabling rapid coordination and effective emergency response. However, traditional communication networks frequently encounter congestion, interoperability failures, and infrastructure collapse during large-scale disasters. To address these deficiencies, specialized networks such as the First Responder Network Authority (FirstNet) have been developed, leveraging advancements in Long-Term Evolution (LTE), Fifth-Generation New Radio (5G NR), and priority access mechanisms to enhance reliability and coverage. This comprehensive review examines the technological evolution of first-responder communication systems from legacy Land Mobile Radio (LMR) and Project 25 (P25) systems through modern broadband solutions. We systematically analyze key enablers including network prioritization with Quality of Service (QoS), Priority, and Preemption (QPP); dedicated spectrum allocation on Band 14 (758–768/788–798 MHz); Mission Critical Push-to-Talk (MCPTT), Mission Critical Video (MCVideo), and Mission Critical Data (MCData) standards defined across 3GPP Releases 13–18; network slicing for dedicated emergency virtual networks; and Multi-access Edge Computing (MEC) for ultra-low-latency field processing. Additionally, this study assesses the integration of artificial intelligence and machine learning for predictive network management, digital twin technology for infrastructure resilience simulation, Internet of Things (IoT) sensor ecosystems for enhanced situational awareness, and satellite communication systems—including emerging Low Earth Orbit (LEO) constellations—for connectivity in infrastructure-denied environments. We further examine real-world deployments through case studies encompassing Hurricane Katrina (2005), the September 11 attacks (2001), Hurricane Harvey (2017), the California wildfires (2018–2025), and the 2011 Great East Japan Earthquake, alongside global initiatives including the United Kingdom’s Emergency Services Network (ESN), the European Union’s Public Protection and Disaster Relief (PPDR) framework, and South Korea’s PS-LTE SafeNet. By synthesizing recent advancements across more than 120 scholarly and technical sources, this review provides a forward-looking roadmap addressing Sixth-Generation (6G) networks, terahertz communications, holographic situational awareness, augmented and extended reality for field operations, blockchain-secured data sharing, and cybersecurity frameworks. We conclude with policy recommendations and identify critical research gaps necessary to ensure a seamless, intelligent, and globally interoperable communication infrastructure for first responders.

Abstract: The sixth-generation (6G) mobile networks are envisioned to deliver seamless 3D coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents the first comprehensive survey on aerial corridors. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space.

Abstract: This paper presents the design and experimental evaluation of a microstrip antenna intended for operation in the DVB-T2 digital terrestrial television system within the UHF band. The antenna was fabricated on an FR4 dielectric substrate with a relative permittivity of and a thickness of mm. The developed structure is characterized by compact dimensions ( mm), which facilitates integration with receiving devices. This paper presents the results of numerical simulations and laboratory measurements concerning the electrical performance and radiation characteristics of the proposed antenna. The analysis encompasses the reflection coefficient (), voltage standing wave ratio (VSWR), input impedance, and antenna gain. The proposed microstrip antenna is characterized by a minimum reflection coefficient (S11) of –27.19 dB, a peak gain of 3.22 dBi, and a wide operating bandwidth of 640 MHz, which corresponds to a relative bandwidth of 103.22%. The experimental section of this study also includes a comparative analysis of the RF and signal quality parameters of the DVB-T2 signal. The performance of the proposed antenna was evaluated against four other receiving antennas of different configurations, specifically log-periodic and dipole designs. The analysis of the experimental data, including received signal levels and multiplex reception stability, enabled the evaluation of the developed antenna under real-world operating conditions in comparison with commercially available benchmarks. The results demonstrate that the proposed microstrip antenna provides an effective and compact alternative for DVB-T2 digital terrestrial television reception systems.

Abstract: Radio signals carry information in three natural ways: by changing how strong the signal is (amplitude), how high or low its tone is (frequency), and how its timing shifts within the wave (phase). In most communication systems, engineers use only one of these features at a time. As a result, much of the signal’s potential to carry information remains unused. This paper explores a simple but powerful idea: using all three features of a radio wave simultaneously to transmit information on a single carrier signal. By combining amplitude, frequency, and phase modulation together, a single radio wave can carry far more information without requiring additional bandwidth.To explain and analyze this concept, the work introduces an intuitive geometric framework inspired by a four-dimensional shape called a \emph{tesseract}, often described as a “four-dimensional cube.” In this framework, three directions represent the three information channels—amplitude, frequency, and phase—while the fourth represents time. This geometric picture provides a clear way to visualize how the three channels coexist without interfering with each other.As a simple demonstration, the phrase “I Love You” is encoded by assigning each word to a different feature of the signal: “I” is carried by amplitude changes, “Love” by frequency variations, and “You” by phase shifts. Colourful waveform plots, three-dimensional visualizations, and a novel “tesseract slicing” illustration help make the four-dimensional behaviour easier to understand.The proposed framework has potential applications in satellite communication, future 5G/6G networks, radar systems, and signal-processing education. By using all three dimensions of a signal at once, this approach reveals previously unused communication capacity and shows how a single radio wave could deliver substantially more information without consuming extra spectrum.

Abstract: High gain and low sidelobe level remain challenges for 5G millimeter-wave antenna systems. This paper presents a low-sidelobe, high-gain microstrip array antenna based on non-uniformly slotted identical-sized radiating patch, designed to simultaneously enhance gain and suppress sidelobe levels for 5G millimeter-wave (mmWave) communication systems. The key innovation lies in the use of an intermediate-deep, edge-shallow non-uniform slotting technique to precisely control the surface current distribution of the radiating elements. thereby achieving significant sidelobe level (SLL) suppression and antenna isolation enhancement without increasing the physical footprint of each element. The final design operates at a center frequency of 78.5 GHz, achieving a maximum gain of 15 dB and suppressing the first sidelobe below −20 dB, outperforming conventional linear arrays. Notably, the patch width is reduced to only 1 mm—compared to Chebyshev-distributed arrays—resulting in a compact array layout with over 40% unit width size reduction while simultaneously improving inter-element isolation by more than 18 dB. This current-distribution engineering approach offers a novel, structure-efficient pathway for designing high-performance, densely packed mmWave antenna arrays, circumventing the need for additional decoupling structures or enlarg the antenna spacing，simulation results show that the average isolation has increased by more than 5 dB from 76 GHz to 79 GHz. Finally, the same design method was used to design a 24GHz antenna, which was then fabricated and tested. The antenna achieved a sidelobe suppression of -17 dB.

Abstract: In this paper, we propose an enhanced preamble scheme for the physical random access channel (PRACH) applied to low-altitude integrated sensing and communication (ISAC) systems, aiming to expand the sensing capability of traditional mobile networks with PRACH frames based on ZC sequences. To enable the network to possess target sensing capability before successful terminal access, we transform PRACH from a mere initial access channel into an ISAC system capable of supporting high-speed terminal access and user equipment sensing by introducing a time-frequency orthogonal block structure and orthogonal cover codes (OCCs). Specifically, we first derive the Cramér-Rao lower bound (CRLB) for estimating the distance and velocity of user equipment using OCC-ZC sequences, and establish the evaluation metric for communications named detection probabilities. Then, the ISAC problem is formulated as a multi-objective optimization function. Since the multi-objective optimization problem is non-convex, we propose the NSAG-II algorithm to solve it, simultaneously improving the estimation accuracy of distance and velocity in the sensing aspect and the detection probability in the communication aspect.

Abstract: This is a follow-on review of progress in development and applications of hollow core optical fibers (HCFs) after publication of earlier review in 2023 [1], to be read together with it. Progress after 2023 in several fields is significant. Loss of best HCFs was reduced down to 0.05–0.10 dB/km at 1550 nm [2–6], while lowest loss achieved in single mode fiber with pure silica core is 0.14 dB/km [7]. Polarization mode dispersion (PMD) has been reduced to a level typical for SMFs by means of fiber spinning [8]. In November 2024, Microsoft announced a 2-year plan to install 15,000 km of HCF cables between data centers providing data processing for Microsoft Azure cloud services, and inside these facilities [9,10]. Besides UK-based Microsoft Azure Fiber and two Microsoft subcontractors: Corning Inc. and Heraeus Covantics, two major HFC manufacturers: YOFC and Linfiber emerged in China. Unfortunately, progress in standardization and elimination of loss introduced by contaminants in the fiber was absent. Standardization is blocked by multiple fiber designs being tried, with no clear winner yet. Despite this, hollow core fibers have successfully made large-scale commercial debut in Microsoft Azure data centers.

Abstract: The constant growth of IP data traffic, driven by sustained annual increases surpassing 26\%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising costly-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10⁻³–10⁻² dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission.

Abstract: With the deployment and application of the fifth-generation communication technology as well as the research on the sixth-generation communication technology, the space-air-ground-sea integrated network has emerged as a key vision for future communications. Unmanned aerial vehicles (UAVs), serving as aerial nodes, can be utilized in emergency rescue and disaster relief, mapping, environmental monitoring, and enhancement of communication coverage, among other areas. In terms of enhancing communication coverage, the integrated space-ground network, with UAVs as an important component, can provide seamless communication coverage to remote areas, deserts, oceans, and other all-domain three-dimensional spaces. UAVs have become important research objects due to their low cost and high flexibility, and the enhancement of communication coverage in the form of base station-relay UAV-slave UAV based on one-hop relaying has become a significant direction. However, the high mobility and extensive coverage of UAVs also give rise to synchronization challenges. In this work, to tackle the challenges of round-trip delay (RTD) from long-distance transmission and Doppler frequency offset in uplink synchronization between ground base stations and relay UAVs, a long-range random access preamble design is proposed. An enhanced two-step detection framework is introduced, where two distinct root sequence preambles are utilized for RTD estimation and random access respectively, and Doppler frequency offset is mitigated via pre-compensation. For the uplink synchronization in the sidelink between slave UAVs and relay UAVs, to address Doppler frequency offset, improve access efficiency, reduce resource consumption, and simultaneously account for the asynchrony among different users, an asynchronous non-orthogonal multiple access (A-NOMA)-based two-step random access scheme is developed. The scheme leverages existing physical random access channel (PRACH) preamble sequences with paired indexing for Doppler frequency offset estimation; on this basis, a successive interference cancellation algorithm based on Doppler frequency offset and phase compensation is designed to demodulate user data. For downlink synchronization between slave UAVs and relay UAVs, improvements to frequency offset estimation are achieved through redesigned sidelink synchronization signal block (S-SSB) resource allocation. Alongside this, a down-sampling-based detection scheme is designed to reduce UAV power consumption given energy constraints, with a comprehensive link algorithm developed to support implementation.

Abstract: Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging due to energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture, combining an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrate reliable LTE-M connectivity (4,306 RSSI samples; mean -75.51 dBm) and strong agreement with a regional weather station, with mean deviations of \( -0.71^{\circ} \)C (temperature), \( 4.98\% \)(humidity), and a stable pressure offset of -9.58 hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieves measurement performance comparable to professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications.

Abstract: The evolution of future Internet and sixth-generation (6G) networks is driving a paradigm shift from classical bit-centric communication toward meaning-aware and task-oriented communication models. Traditional information theory, while fundamental for ensuring reliable symbol transmission, does not account for semantic relevance or task effectiveness, which are critical for emerging applications such as autonomous systems, immersive services, and ultra-low-latency communications. This article presents a comprehensive review of Semantic Communications (SemCom) from a future Internet perspective. The review systematically analyses representative extensions of classical information theory aimed at quantifying semantic information, including semantic information measures, semantic channel capacity, and semantic rate–distortion formulations. In addition, the main mathematical and computational frameworks enabling practical semantic communication systems are examined, including the Information Bottleneck principle, learning-based end-to-end communication architectures, and reinforcement learning approaches for task-oriented optimization under network constraints. The review further discusses the role of semantic metrics, contextual modelling, and task-driven performance evaluation in the design of semantic-aware communication systems. The analysis identifies key open challenges, particularly the lack of a unified theoretical framework, the need for robust and context-aware semantic performance metrics, and the integration of semantic awareness into network-level design. Overall, this review highlights Semantic Communications as a promising paradigm for future Internet and 6G networks, where communication efficiency is increasingly determined by semantic relevance and task effectiveness rather than bit-level fidelity alone.

Abstract: The sixth generation of mobile networks (6G) is envisioned as an AI-native and computa-tion-driven infrastructure capable of supporting ultra-low latency, massive connectivity and intelligent services across highly heterogeneous environments. Achieving these objec-tives challenges traditional centralised architectures and motivates a shift towards dis-tributed computing and intelligence at the network edge. This study presents a structured computational analysis of architectural approaches that integrate distributed computing paradigms and Edge Artificial Intelligence (Edge AI) as core enablers of 6G networks. The methodology follows PRISMA guidelines for systematic reviews and is based on a com-prehensive analysis of peer-reviewed literature, architectural proposals and standardisa-tion documents retrieved from major scientific databases, including IEEE Xplore, Scopus, Web of Science, MDPI and arXiv, as well as reports from ITU-R, 3GPP and ETSI. The analysis examines the evolution from cloud-centric to edge-centric computing, key Edge AI techniques—such as Federated Learning, Split Learning and edge-adapted large AI models—and their role in enabling intelligent orchestration, resource optimisation and context-aware services. The results indicate that the tight integration of distributed com-puting and Edge AI enhances network responsiveness, scalability and adaptability, while also revealing persistent challenges related to orchestration complexity, resource con-straints, security and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realisation of 6G networks and for guiding future research and standardisation efforts.

Abstract: The smart agriculture system requires high efficiency to automatically maximize crop yields and minimize losses. Wireless sensor networks (WSNs) are essential for maintaining system sustainability through sensing and connectivity. However, they encounter challenges related to cost, interoperability, and reliability. Efforts have been made to expand sensing capabilities while managing costs and addressing variability in sensor communication and power consumption. Despite these efforts, a comprehensive solution—especially for orchard fields—remains undeveloped. This study introduces a coordinated WSN design to optimize sensing and connectivity in agricultural fields. We employ an integrated sensing and connectivity (ISAC) strategy to create a complete solution. Our hybrid approach combines graphical computation with distance-vector algorithms for reliable, cost-effective deployment. Additionally, resilient connectivity is achieved through effective channel modeling and adaptive beamforming. The proposed method, combined with quantitative heterogeneous network selection using MLR-AHP, addresses interoperability issues and improves network resilience. Results indicate improved sensor placement and wireless ranking, even with only 5 nodes. The solution extends sensor battery life, maintains 99% coverage, and empirical tests validate its effectiveness for designing and deploying WSNs in orchard fields.