Mobility modelling plays a pivotal role in evaluating the performance of Mobile Ad Hoc Networks (MANETs), as the movement patterns of nodes strongly influence routing efficiency, connectivity, and network stability. Existing mobility models, particularly entity and group-based frameworks, have proven useful in simulating various MANET applications but remain limited in capturing the complex movement behaviour characteristic of Search-and-Rescue (SAR) operations in disaster scenarios. This study proposes a realistic hybrid mobility model that integrates the essential features of both entity and group mobility paradigms to better represent the coordinated yet flexible dynamics of SAR teams. Three sets of simulations were performed using the Network Simulator 2 (NS2). The first and second simulations focused on generating and combining existing entity and group mobility patterns using the BonnMotion tool, resulting in two composite models (Real1 and Real2). The third simulation compared these models using AODV and DSDV routing protocols under varying node mobility levels and traffic conditions. Key performance metrics, including relative mobility, node degree, partition count, link duration, packet delivery ratio (PDR), throughput, and average end-to-end delay, were analysed. Results demonstrate that it is feasible to concatenate existing mobility models into a coherent hybrid framework. Among the developed models, Real2 exhibited superior performance in most test conditions, yielding higher PDR, lower delay, and more stable connectivity than its constituent models. These findings confirm that the performance of MANETs is highly dependent on mobility realism. Consequently, selecting a model that accurately reflects the behavioural characteristics of the target scenario is essential for credible MANET performance evaluation in emergency response contexts.

Understanding how tropospheric variability influences mobile communication systems is critical for improving network reliability. This need is especially important in regions characterized by rapid atmospheric fluctuations. This study presents a quantitative modeling framework for evaluating tropospheric effects on mobile network performance using key performance indicators (KPIs). Tropospheric parameters including temperature, relative humidity, atmospheric pressure, and wind conditions were synchronized with corresponding network KPIs. These include Call Setup Success Rate (CSSR), Traffic Channel Congestion Rate (TCHCR), Handover Success Rate (HOSR), and Received Signal Strength. Statistical techniques comprising Pearson correlation, multiple regression modeling, and hypothesis testing were employed. These methods were used to determine the magnitude, direction, and significance of atmospheric influences. Results reveal that temperature and humidity exhibit strong, statistically significant associations with signal strength and call reliability. Pressure and wind parameters show moderate but noteworthy effects on congestion and handover performance. The developed models demonstrate that tropospheric conditions account for a substantial proportion of KPI variability. This indicates that atmospheric impairments play a measurable role in network degradation. The study provides a data-driven basis for proactive network optimization. It enables operators to incorporate atmospheric behaviors into predictive maintenance, link budgeting, and adaptive radio-resource management. The findings contribute to an improved understanding of environmental impacts on mobile communication systems. They also support the design of more resilient networks under dynamic tropospheric conditions.

Abstract This project presents NetMapper, a mobile- based system designed to analyze, map, and optimize mobile network performance across diverse locations. The app collects real-time data on network signal strength and internet speed, then visualizes it as a heatmap using Google Maps API. This helps users and telecom providers identify weak signal zones, monitor connectivity, and improve infrastructure planning. The system also features an Admin Dashboard that allows data review, manual ticket generation, and network performance tracking. NetMapper aims to bridge the digital divide between urban and rural areas by empowering providers to make data-driven decisions about network improvement and tower placement. Keywords: Network Strength, Signal Mapping, Heatmap Visualization, Real- Time Speed Tracking, Admin Dashboard, Rural Connectivity

The dynamic behavior of the retrial queueing system following the incorporation of Deep Q-Network Reinforcement Learning in 6G mobile communication services is examined in this study. The proposed method lies in analyzing the DQN-RL agent’s learning convergence by using the first- and second-order Markov chain method. By simulating the temporal evolution of reward sequences as Markov and second-order Markov chains, we can quantify convergence characteristics through mixing time analysis. To capture a wide operational landscape, a thorough simulation framework with 120 independent parameter combinations is created. The obtained results indicate that Markov chain analysis confirms 10 training episodes are more than sufficient for policy convergence, and in some cases, as few as 5 episodes allow the agent to enhance the mobile network performance while maintaining low energy consumption. To assess learning stability and system responsiveness, the mixing time of DQN RL rewards is calculated for every episode and configuration. A deeper understanding of the temporal dependencies in the reward process can be gained by incorporating higher-order Markov models. This paper concentrates on studying the learning convergence using an analysis of the Markov model’s spectral gap properties as an indicator. The results provide a rigorous foundation for optimizing 6G queueing strategies under uncertainty by highlighting the sensitivity of DQN convergence to system parameters and retrial dynamics.

Aiming to boost the throughput performance in mobile networks, network-coded multiple access (NCMA) has been proposed as a new framework in non-orthogonal multiple access (NOMA). Normally, user-centric design is adopted in NCMA to allocate the same power to different users. However, devices and the corresponding messages may have different quality-of-service (QoS) requirements (such as delay or throughput) due to the potential content diversity in Internet of Things (IoT) networks. In this paper, we present a power-diverse NCMA (PD-NCMA) system. In particular, a two-stage framework in NCMA downlink is adopted. Based on the throughput requirement of content, messages are given different levels, and content-centric clustering is solved dynamically in the first stage. Then, power allocation in each cluster is realized in the second stage. Numerical results demonstrate the feasibility of the proposed PD-NCMA, and the throughput of PD-NCMA significantly outperforms the traditional NCMA system.

This study evaluates the performance of Mobile Ad Hoc Networks (MANETs) in disaster recovery, addressing the gap in existing research that primarily focuses on network performance metrics. The study aims to provide a comprehensive evaluation using the Balanced Scorecard (BSC) framework, considering financial, user, process, and innovation perspectives. A quantitative approach is employed, synthesizing data from existing literature, case studies, and empirical research on MANET deployments in disaster scenarios. Key performance indicators (KPIs) are categorized into the four BSC dimensions: network efficiency (process), cost-effectiveness (financial), usability (user), and innovation capacity. The study finds that MANETs significantly enhance communication resilience during disasters but face challenges in scalability, energy consumption, and security. The BSC framework identifies high deployment feasibility and operational efficiency but highlights limitations in long-term sustainability and integration with satellite/terrestrial networks. Unlike previous studies focused solely on technical parameters, this research offers a holistic evaluation by integrating the BSC framework, providing a more comprehensive analysis. The findings suggest that adaptive routing, AI-driven optimizations, and hybrid MANET-Satellite models could improve network performance. Future research should explore real-world deployments, energy-efficient protocols, and enhanced security models using blockchain.

The primary objective of this research is to design a secure and intelligent routing framework that effectively detects and mitigates wormhole attacks while improving overall routing performance in Mobile Ad Hoc Networks (MANETs). Traditional routing protocols are highly vulnerable to wormhole intrusions, resulting in severe packet loss, malicious data manipulation and degraded communication reliability. To overcome these limitations, the study adopts a machine learning–based approach using four supervised classifiers—Decision Tree, Logistic Regression, Support Vector Machine and Random Forest—to identify abnormal routing behaviors. A simulated MANET testbed was created to generate both legitimate and wormhole attack traffic for training and evaluation. The framework is further enhanced with three optimization techniques—Modified Genetic Algorithm (MGA), Grey Wolf Optimizer (GWO) and Ant Colony Optimization (ACO)—to enable adaptive and efficient route selection under dynamic mobility. Experimental results show that the Random Forest model delivers the best performance, achieving 98.64% detection accuracy, 72.40% packet delivery rate and reducing stolen packets to 1%. Among hybrid models, RF + MGA provides the most balanced security and routing performance, RF + GWO achieves superior energy efficiency and RF + ACO ensures faster path convergence suitable for high-mobility scenarios. Overall, the proposed system significantly enhances network security, stability and sustainability, making it ideal for mission-critical MANET applications such as military operations, emergency communication and large-scale IoT deployments

This research proposes a unified AI-based framework to enhance mobile network performance using edge computing. It introduces ARMA for latency reduction and CETO for energy optimization. Both algorithms rely on predictive analytics and adaptive task management. Implemented in Python and validated using NS-3 simulations and real telecom data, ARMA reduced latency by up to 50%, while CETO decreased energy use by 35%. Results were statistically significant (p < 0.05) across urban and rural scenarios. The framework provides a scalable, efficient, and secure solution for edge deployment, supporting real-time applications such as IoT and autonomous systems.

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions.

The advent of 5G technology demands significant improvements in handover mechanisms to ensure seamless connectivity and optimal performance in mobile networks. In a 5G network setting, the issue of excessive handovers for mobile devices using cell data and pattern analysis was identified. This study proposes a distribution function driven handover solution for 5G mobile networks, aiming to enhance handover efficiency and reliability. Similarly, leveraging advanced statistical distribution functions, our approach dynamically adjusts handover parameters to accommodate varying network conditions and user mobility patterns. Extensive simulations testing demonstrated that our method achieves a 98% accuracy rate in predicting and managing handovers, significantly surpassing the performance of existing methodologies. The proposed solution not only minimizes handover failures and latency but also optimizes resource allocation and network throughput. These results highlight the potential of distribution function driven strategies to revolutionize handover processes in 5G networks, paving the way for more resilient and adaptive mobile communication systems. The results underscore the advantages of using probability distribution functions for handover management in 5G networks. The adaptive and dynamic nature of this approach addresses the limitations of traditional fixed-threshold methods, providing a more resilient and flexible framework suited for the complex and variable conditions of 5G environments.

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration.

Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration

In recent years, virtual private network provides a private and secure network architecture for individuals and organizations by using with the support from Internet. VPN helps in safe data transmissions without the need for dedicated physical connections, allowing to creation of a secure communicational channel between remote users, sites, and corporate offices. The proposed research focused on exploring a systematic literature review on VPN security concerning adaptive multi-tunnelling as a mitigation strategy. The systematic review adopted PRISMA framework to minimizing bias and ensuring the transparency of the survey. The survey used various databases such as Google Scholar, Research Gate, and Scopus were used to collect the research articles published from 2018 to 2025. The keywords used "corporate cybersecurity”, "adaptive security mechanisms,” "multi-tunneling VPN,” and “VPN security”.11 articles were included for final inclusion of the research. The major themes identified in the research are multi-tunnel architecture and deployment models, traffic splitting and load balancing mechanisms encryption and key management strategies and network adaptation and dynamic routing. The implementation of traditional VPN security was enhanced with adaptive multi-tunneling. The major issues associated with VPN, such as mitigation, scalability, and resilience, were addressed by the adaptive multi-tunneling technique. The adoption of adaptive multi-tunneling in VPN security gained a significant advantage in mitigating vulnerabilities and optimizing performance in mobile networks. Potency, latency, and computational overhead were considered major disadvantages in implementing adaptive multi-tunneling in VPN security.

Adaptive bitrate (ABR) streaming provides an effective way to improve the Quality of Experience (QoE) of video users and is now the de facto standard for video delivery. Meanwhile, mobile edge computing (MEC) has been applied to assist ABR streaming, improving the performance of mobile networks and enabling efficient video delivery. However, smooth ABR streaming relies on the bidirectional adaptation between bitrate selection and bandwidth allocation, as they operate on distinct timescales and have different optimization goals. Moreover, since the constrained wireless resources available within a cell are shared by multiple users, their QoE should be optimized not only jointly but fairly. To this end, we propose a QoE-fairness-aware bandwidth allocation (QFA-BA) method for MEC-assisted ABR video transmission. With a novel perspective on buffer occupancy modeling, the relationship between bitrate selection and bandwidth allocation is studied. An enhanced QoE evaluation model is then proposed to correlate bitrate selection with bandwidth allocation and facilitate QFA-BA. Finally, a soft actor-critic (SAC) framework improving both the QoE and QoE-fairness is presented for QFA-BA. Compared with the state-of-the-art methods, our QFA-BA can perceive fine-grained buffer occupancy and stabilize it near a preset value with relatively more and larger bitrate switchings, exhibiting smoother convergence, better QoE (50.29%) and QoE fairness (54.81%).

This study aims to enhance handoff performance in mobile communication networks by addressing the limitations of conventional systems through the implementation of the Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional handoff methods have been constrained by a modest success rate of 80%, leading to unreliable connections during user mobility. These challenges arise from factors such as weak signal strength, high network congestion, and suboptimal mobility management. To tackle these issues, a customised ANFIS model was developed using MATLAB/Simulink, introducing a dynamic and intelligent approach to the handoff decision-making process. The results demonstrated significant performance improvements. The ANFIS-based system achieved a 95% handoff success rate, a substantial increase from the conventional method. Quantitative enhancements included a rise in signal-to-noise ratio (SNR) to 25 dB, an improvement in signal strength to an average of -70 dBm, and an increase in system efficiency to 95%. Moreover, the ANFIS approach facilitated a growth in network capacity, allowing concurrent users to increase from 100 to 150. Quality of Service (QoS) metrics, previously rated as poor, were elevated to good, ensuring a seamless user experience. These findings underscore the transformative potential of ANFIS in managing handoff strategies, offering more reliable connectivity and improved performance in demanding wireless environments. The study advocates policy adjustments to prioritise the adoption of intelligent systems like ANFIS to meet the growing requirements of modern mobile communication networks.

Measurement of mobile network signal power, among other features, is an activity of major importance in the telecommunications industry due to several features associated with it, such as network optimization, Quality of Service or infrastructure planning. Unfortunately, major issues happen when those measurements must be taken in unconventional terrains such as remote, mountainous or rural areas, as traditional methods to gather information (drive or walk testing, crowdsourced data, usage of static measurement stations) quickly become ineffective. By employing Unmanned Aerial Vehicles (UAVs) equipped with signal receivers, this manuscript presents development works that aim to provide a scalable and cost-effective solution for collecting and processing signal measurements. The performed activities aim to use a UAV combined with open-source systems and standardized protocols to obtain and process signal measurements, as well as state-of-the-art software infrastructure that will be used to store and display such data readings in an inexpensive, secure and user-friendly manner. This approach not only enhances the understanding of signal distribution in various environments but also supports the optimization of mobile network performance, ultimately benefiting both public and private sectors in their efforts to improve connectivity and service quality.

In recent years, Machine Learning (ML) has become a crucial and promising tool for forecasting and solving a wide range of complex problems. The rapid development of machine learning is closely linked to technological advancements and has also driven the growth of the AI community and open- source tools (e.g., TensorFlow, Keras, PyTorch, fast.ai). This enables researchers to deploy and apply machine learning algorithms more effectively. This paper provides an overview of mobile network traffic at BTS stations, conducted from a data-driven perspective, focusing on extracting and transforming data into information that serves production and business purposes within mobile networks, as well as describing the characteristics of user traffic. The authors used the Google Colab environment to analyze network time statistics to determine traffic in each area. Leveraging large volumes of information helps improve mobile network performance and address various issues (e.g., anomaly detection) that may impact network infrastructure. The study's findings contribute to addressing certain practical challenges in deployment, optimization, resource allocation, and energy savings for mobile networks. Keywords: 5g traffic; base transceiver station; 5G BTS; 5G Traffic; 5G/BTS Traffic.

With the growth of processed data for wireless networks and the establishment of new applications and services, mobile operators keep searching for solutions to deal with the issues raised by nextgeneration networks (NGN). The fifth generation (5G) mobile networks, for example, should enhance their performance without consuming a lot of energy. For these reasons, software-defined networks (SDN) appear to be the promising technology that will make achieving the architectural agility required for the upcoming 5G mobile networks easier. SDN is a clever solution for providing innovation and enforcing the primary drivers in 5G mobile networks, such as flexibility, dependability, service-oriented management, and cost reduction through the control and management of the 5G core network. In this paper, the integration of SDN and multiple controllers with the 5G core network is investigated to determine how these two technologies can affect mobile IP network performance. An energy model suitable for the proposed network architecture has been developed. A widely used Diamond network is being considered for the core network. Open-Flow controllers were used to improve the routing performance by considering load balancing of IP traffic. According to such a model, reduced energy consumption is experienced with SDN. The addition of an SDN controller results in a 11% reduction in consumed energy. The results also show that the use of SDN and multi-controller with NGN can improve the performance further. Using SDN reduced the average delay of the network by 14%. A great reduction in packet error rate (PER) is also achieved when SDN is used.

The rapid expansion of 5G networks has revolutionized mobile communication by offering unprecedented speed, low-latency connections, and the ability to support vast numbers of connected devices. However, these advancements bring new challenges in maintaining consistent and reliable signal strength, critical for ensuring optimal Quality of Service (QoS). Traditional models, such as ARIMA, Random Forest (RF), and K-means clustering, struggle to capture the complex, nonlinear, and dynamic behaviour of 5G networks, leading to suboptimal prediction accuracy. In this study, we propose a novel hybrid model, Clustered Temporal Memory Networks (CTMN), which integrates DBSCAN clustering with Long Short-Term Memory (LSTM) networks to improve signal strength prediction in mobile networks. The CTMN model combines DBSCAN's ability to handle spatial variability and outliers in 5G data, combined with LSTM's capacity for modelling long-term dependencies and nonlinear time-series patterns. Our empirical analysis demonstrates that CTMN outperforms traditional methods, achieving up to a 20.82% improvement in prediction accuracy across key performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These findings indicate that CTMN provides a scalable, robust solution for enhancing signal strength prediction and optimizing network performance in next-generation mobile networks.

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.