Journal of Computer Networks and Communications

Asgharzadeh-Bonab, Akbar; Kalbkhani, Hashem; Azimi, Yaser; Ahmadi, Farid; Pau, Giovanni

doi: 10.1155/jcnc/3350942pmid: N/A

Hybrid satellite–terrestrial networks (HSTNs) represent a crucial technology in the advancement of next‐generation communication systems, including 6 G. This paper presents an in‐depth review of cooperative and cognitive HSTNs, highlighting the key technologies, system models, and performance metrics that define their operation. Our findings reveal that cooperative HSTNs, utilizing techniques like amplify‐and‐forward (AF) and decode‐and‐forward (DF) relay schemes, significantly enhance coverage and data rates by efficiently integrating satellite and terrestrial communication layers. In the cognitive domain, spectrum‐sharing techniques, coupled with dynamic power control, are identified as essential in maximizing spectrum utilization and minimizing interference. The study further explores the impact of hardware impairments and evaluates innovative solutions, such as hybrid analog/digital precoding and rate‐splitting multiple access (RSMA), to improve signal quality and system robustness. Open challenges such as energy efficiency optimization, interference management, and real‐time adaptive resource allocation are discussed, providing a clear roadmap for future research directions. This paper aims to bridge the gap in the existing literature by offering a comprehensive analysis of the technological advancements and challenges in HSTNs, positioning them as a foundational element for the seamless integration of terrestrial and nonterrestrial networks in the 6 G era and beyond.

Beyene, Mulatu Yirga; Seid Musa, Salahadin; Molla Belachew, Habtamu; Tadele Alemu, Behaylu; Andualem Ayalew, Amogne; Lake Tegegne, Melaku; Azam, Farooque

doi: 10.1155/jcnc/8771020pmid: N/A

Advancements in communication technologies have enabled vehicles to be equipped with computing devices, facilitating communication and autonomous operations. This has led to the emergence of a new networking paradigm known as the vehicular ad hoc Network (VANET). A primary objective of VANET is to enhance road safety and traffic efficiency by enabling the exchange of information among vehicles in various intelligent transportation system (ITS) applications. Vehicles regularly transmit safety messages at a fixed rate, typically 10 messages per second. In high‐traffic scenarios, such as multilane highways or densely packed areas, a vehicle may receive an overwhelming number of safety messages. However, before these messages can be reliably used, they must undergo rigorous cryptographic verification. A significant challenge arises when the message reception rate exceeds the verification rate, leading to inefficiencies. In existing schemes, the basic safety messages (BSMs) of nearby vehicles often undergo redundant verification due to consecutive broadcasts, while BSMs from more distant vehicles within the communication range may not receive adequate verification time. To address this issue, we propose a trust‐based approach to improve the efficiency of message verification in VANET. Our simulation results demonstrate that the proposed method optimizes verification time by selectively skipping the verification of one BSM for trusted vehicles, utilizing the road side unit (RSU). This approach enhances vehicle awareness in compliance with the WAVE standard. The study findings indicate that the proposed method achieves an average awareness quality of 85% for neighboring vehicles, outperforming the existing MLPQ‐CA method, which attains only 70% within the same 100‐m communication range.

Lv, C. F.; Li, B.; Wei, J.; Pau, Giovanni

doi: 10.1155/jcnc/8880533pmid: N/A

Fluctuations in traffic within the Internet of Things (IoT) can affect the performance of the control plane. It is important to maintain stable control plane performance by load balancing strategies. To address the issue of controller load balancing in software‐defined Internet of Things (SD‐IoT), and meet the energy consumption requirements of nodes in the IoT during the adjustment process, a load balancing algorithm based on multi‐agent deep reinforcement learning (MADRL) is proposed. This approach models two critical factors: load difference and migration cost, and constructs a load balancing optimization problem based on these two factors. Subsequently, considering the dynamic changes in the state of the SD‐IoT, the load balancing problem is formulated as a Markov game process, and an algorithm is designed based on MADRL to solve this problem. Finally, the algorithm is validated based on real‐world topology, and a comparison is conducted from multiple perspectives including delay, load difference, energy consumption, and migration cost, demonstrating the effectiveness and advantages of the proposed algorithm.

Gelaw, Zinegnaw Libisework; Annamalai, Pushparaghavan; Ayalew, Hailu Dessalegn; Da Silva, Eduardo

doi: 10.1155/jcnc/4243779pmid: N/A

The performance of a gray‐coded dual polarization‐16 quadrature amplitude modulation (DP‐16 QAM)–based multiple input multiple output (MIMO)‐FSO link using coherent detection and digital signal processing (DSP) is investigated. The MIMO link is used to mitigate geometric loss, atmospheric attenuation, and atmospheric turbulence. Additionally, a coherent receiver with DSP algorithms is used to improve channel capacity, receiver performance, and link range. The performance of the system is evaluated by constellation diagrams, RF and optical power spectra, bit error rate (BER), and error vector magnitude (EVM). It is observed that before DSP, the constellation diagrams are distorted, whereas after DSP, distinct constellation symbols are observed. The system possesses a spectral efficiency of around 4.62 b/s/Hz. The longest link range to attain the acceptable BER limit is achieved under 4 × 4 MIMO weak turbulence levels in clear air conditions, which is 11.55 km, while the shortest is achieved under SISO strong turbulence levels in light fog conditions, which is 0.895 km. After the channel impairments are mitigated by DSP, the simulation results show that the MIMO link outperforms the SISO link in all scenarios.

Emon, Md Jahidul Hoq; Hussain, Sheikh Munim; Islam, Azazul; Ahmed, Shafin Shadman; Singh, Debabrata

doi: 10.1155/jcnc/6611890pmid: N/A

This paper presents an integrated framework for intelligent agricultural monitoring and development by combining Internet of Things (IoT) technology, machine learning algorithms, sensor networks and custom hardware design. A comprehensive system was developed using environmental sensors including DHT11, soil moisture probes, BMP180 pressure modules, MQ‐4 gas detectors, rain detection sensors and HC‐SR04 ultrasonic modules, interfaced via custom‐designed printed circuit boards (PCBs) fabricated using Proteus software. NodeMCU ESP8266, ESP32 DevKit and ESP32‐CAM microcontrollers served as the hardware backbone for real‐time data acquisition, wireless transmission, and image capture. Collected sensor data were transmitted to cloud platforms through Adafruit IO for remote visualization and analysis. Machine learning models, including Random Forest and XGBoost classifiers, were trained on features extracted from VGG16‐based image processing to classify plant health conditions with high accuracy. Intelligent irrigation control was achieved through autonomous decision‐making based on real‐time sensor feedback and environmental conditions, dynamically activating a water pump system. The integration of low‐power hardware, efficient PCB layouts, cloud‐based dashboards, and lightweight machine learning models resulted in a scalable, portable, and cost‐effective smart farming solution. Experimental results validate the system’s capability for accurate environmental monitoring, efficient resource utilization, and intelligent crop management, offering significant potential for sustainable agriculture in resource‐constrained settings.

Dafhalla, Alaa Kamal Yousif; Isam, Hiba Mohanad; Zangana, Shireen; Eldeen, Asma Ibrahim Gamar; Adam, Tijjani; Sadok, Djamel F. H.

doi: 10.1155/jcnc/3635533pmid: N/A

The convergence of the Internet of Things (IoT) with cloud, edge, and fog computing has catalyzed a transformative shift in data processing and real‐time analytics across multiple sectors. This review explores the emerging IoT and cloud continuum, emphasizing its role in enhancing system scalability, reducing latency, and enabling intelligent, distributed decision‐making. A central contribution of this work is the design and implementation of a real‐time greenhouse monitoring and control system based on an FPGA platform, specifically the DE2‐115 development board with a Cyclone IV EP4CE115F29C7 device. The system integrates real‐time environmental sensing based on temperature, humidity, soil moisture, and light status with dynamic control of actuators such as fans, water pumps, LEDs, and humidifiers. Operating in both manual and autonomous modes, the system demonstrates high efficiency, utilizing only 2% of logic elements and < 3% of logic array blocks, while providing robust data visualization via onboard displays and a mobile interface. This practical implementation is contextualized within the broader IoT‐cloud continuum, illustrating how edge processing with FPGA complements cloud‐based analytics for precision agriculture. Moreover, the review investigates the architectures and protocols that underpin the continuum, addressing challenges such as security, privacy, and interoperability. Special attention is given to the role of AI, machine learning, and predictive analytics in enhancing decision‐making. The paper concludes with future research directions, highlighting the potential of emerging technologies including 5G, edge AI, and blockchain to overcome current limitations, enhance data processing, and drive innovation. By integrating real‐world application with theoretical advancement, this work offers a scalable, efficient, and intelligent model for future smart systems in agriculture, healthcare, smart cities, and beyond.

Abdollahpour, Hassan; Hosseini, S. Abolfazl; Raeisi, Nima; Azam, Farooque

doi: 10.1155/jcnc/5285765pmid: N/A

Channel models are the basis of system design, theoretical analysis, performance evaluation, optimization, and deployment of communication systems in wireless fading environments. LTE, a global standard for high‐speed data transmission, relies on channel modeling techniques due to its multiple‐input multiple‐output (MIMO) architecture. In MIMO systems, the correlation coefficient is a key metric for evaluating antenna performance, particularly in terms of how effectively the system can exploit spatial diversity and multiplexing gains. This manuscript proposes a three‐dimensional geometry channel modeling method based on the correlation of received signals in a multipath channel, aiming to enhance the performance of MIMO communication systems. We address the fading problem by employing diversity transmission with state‐transparent modulation codes, thereby enhancing link robustness and performance in noisy environments. This method uses geometric diffusion models where poly‐ellipsoid curves or surfaces define scattering locations. The model includes geometrical scattering for indoor environments. We establish an elliptical area and uniformly distribute a specified number of objects within it, taking into account the maximum excess delay and the distance between transmitters and receivers. The transmitter emits data as spherical waves, which scatter upon encountering objects before reaching the antennas. The statistical properties of the received data are derived from the calculated wave parameters, utilizing the common probability distribution function (PDF) of wave amplitudes. Furthermore, essential receiver parameters can be determined, such as signal‐to‐noise ratio, bit error rate for any antenna array, and correlation coefficients between antenna pairs. By utilizing correlation coefficients, we can more accurately represent the interdependencies between various parameters within the LTE framework. The proposed 2‐GHz indoor 1 × 2 MIMO model demonstrates significant enhancements, including 53%–135% reduced SNR fluctuations, 27%–59% improved antenna decorrelation, and 25%–31% increased channel capacity compared to industry‐standard models such as WINNER II, COST 2100, and QuaDRiGa. Furthermore, it achieves a near‐ideal diversity order (1.82–2.0). These advantages stem from its physics‐accurate modeling of spherical wave propagation and poly‐ellipsoid scattering, making it suitable for reliable 5 G/6 G indoor deployments utilizing compact antenna arrays. The proposed model is generated using a ray‐tracing simulation, with numerical results confirming its effectiveness.

Chaudhry, Umair B.; Ian Phillips, Chris; Setiadi, De Rosal Ignatius Moses

doi: 10.1155/jcnc/8888509pmid: N/A

Collecting data from nodes in mobile ad hoc wireless sensor networks is a persistent challenge. Traditional methods rely on specialized routing protocols designed for these environments, with research often aimed at improving efficiency in terms of throughput and energy consumption. However, these improvements are often interconnected, where gains in one area can lead to compromises in another. An alternative approach uses unmanned vehicles (UVs), particularly unmanned aerial vehicles (UAVs), due to their adaptability to various terrains. Unlike traditional methods, UAVs can collect data directly from mobile nodes, eliminating the need for routing. While most existing research focuses on static nodes, this paper introduces a multiple objective evolutionary approach “Strength Pareto Evolutionary Algorithm for Dynamic UAV Paths” (SPEA‐DUP) for UAV data collection that predicts the future positions of caching‐enabled mobile ad hoc wireless sensor network nodes. SPEA‐DUP aims to maximize encounters with nodes and gather the most valuable data in a single trip. The proposed technique is tested across different simulation scenarios, movement models, and parameter configurations and is compared to our genetic algorithm ‘Genetic Algorithm‐Aerial Paths’ (GA‐AP) counterpart to evaluate its effectiveness.

C. R., Reshma; Ramaswamy, Arun Kumar Banavara; Kumar, Shreyas Arun; Prasad, Mahadeshwara; Gaber, Jaafar

doi: 10.1155/jcnc/2972993pmid: N/A

Data privacy is a major concern in the present data‐driven era when sensitive information is being increasingly shared and analyzed across distributed systems. The existing mechanisms for privacy preservation, such as differential privacy (DP), local differential privacy (LDP), homomorphic encryption, and secure multiparty computing (SMPC), often face challenges in maintaining a balance between utility and privacy, especially in dynamic and heterogeneous environments. This paper introduces partial DP as a flexible framework for addressing the above challenges across a variety of data settings, such as federated learning, decentralized systems (blockchain and IoT), graph data, and streaming analytics. Dynamic sensitivity differential privacy (DSDP) employs adaptive noise mechanism and dynamically adjusts the data sensitivity by ensuring the robust privacy without compromising data utility. The experimental evaluations on real‐world datasets prove the superiority of DSDP over traditional approaches with minimal utility loss and high privacy guarantees at efficiency. DSDP is a promising solution in the evolving computer paradigms for data privacy protection. The proposed methods are carried out to evaluate the parameters such as execution time, utility loss, and privacy level. From the experimental results, DSDP achieves up to 15% higher utility than DP in federated learning, 30% reduced latency in decentralized systems, and 25% better structural integrity for graph data for privacy‐preserving guarantees.