International Journal of Mobile Network Design and Innovation (6 papers in press)

Regular Issues

• QoS Scheme for Vehicular Communication on VANETs for Detecting Messages characteristics  
  by Pramod Kumar Sagar, Suman Avdhesh Yadav, Smita Sharma, S. Vikram Singh 
  Abstract: Vehicular Ad-Hoc Networks (VANETs) are widely recognized as a promising technology for enhancing road safety and improving the efficiency of transportation systems. Delivering Quality of Service (QoS) for Vehicle-to-Everything (V2X) networks, also known as Vehicle-to-Network (V2N) or Vehicle-to-Everything (V2E), is a serious challenge due to the mobility features of vehicles. This paper also investigates an art QoS-based framework for VANETs that applies message properties to recognise and assign priority means to the messages. Through this proposed method, the network configurations are adjusted, allowing critical messages to be prioritised and transmitted according to the guarantee. The proposed model obtained a 95.14% packet delivery ratio, 4.86% end-to-end delay, 93.62% network throughput, 89.98% Transmission Data rate, and 93.87% Bandwidth Utilisation. Simulation results prove the effectiveness of the proposed method in improving QoS parameters. With QoS support, VANETs can be used for practical applications, which can significantly improve VANETs by ensuring reliability and efficiency.
  Keywords: Vehicular Ad-Hoc Networks; Quality of Services; Infrastructure and Delay; Network Throughput; Transmission Data Rate; Network Configurations; Reliability and Efficiency.
  DOI: 10.1504/IJMNDI.2026.10074896
   
  
• Dynamic Bandwidth Allocation and Task Scheduling in Large-Scale IoT Networks  
  by Xuefei Xu 
  Abstract: The rapid advancement of mobile Internet and the proliferation of Internet of Things (IoT) applications have substantially increased demands for data transmission and processing. However, IoT models often operate in resource-constrained environments where real-time processing is critical, limiting their performance. To address these challenges, we propose Fishnet-6G, a novel framework that employs a mesh-based packet ordering and resource allocation strategy to optimise 6G networks. The network architecture is based on the Sierpinski Triangle, and Quantised Density Peak Clustering (QDPC) is used for efficient device connectivity. Cluster Heads (CHs) and Substitute CHs are dynamically selected using real-time data. Traffic prediction is enhanced through fair queue state assessment and an adaptive-rate Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm. Scheduling is managed using a Bayesian Game-Theoretic Approach (BGTA). Simulated using Network Simulator-3.26, Fishnet-6G demonstrates superior performance in throughput, latency, energy efficiency, and packet loss, offering a robust solution for 6G-IoT networks.
  Keywords: Bandwidth Allocation; Io T Networks; Fishnet-6G; Edge server; BGTA algorithm; IMPDDPG method.
  DOI: 10.1504/IJMNDI.2026.10075385
   
  
• Big DataPowered Artificial Intelligence Approaches for Security and Anomaly Detection in Mobile Network Infrastructures  
  by Shifu Zhang, Yawei Zhang, Boqun Cheng 
  Abstract: This project investigates anomaly detection and mobile network cybersecurity through the integration of AI and deep learning. The proposed method enables scalable deployment within network infrastructures and enhances detection accuracy by leveraging large-scale data collection, pre-processing, feature extraction, and hybrid model training. With the rapid expansion of 5G and the Internet of Things (IoT), ensuring security and anomaly detection presents critical challenges. AI-powered approaches offer adaptive solutions to address evolving threats effectively. While prior research has primarily focused on auto encoders, LSTM networks, and CNNs for IoT intrusion detection, limited attention has been given to anomaly detection and predictive modelling in cellular networks. The proposed framework incorporates automated encoding, CNNs, LSTMs, and federated learning to detect vulnerabilities in real time. Experimental results demonstrate superior performance, achieving 97.5% accuracy, 96.2% recall, and 96.8% F1-score, thereby validating the framework's effectiveness in advancing anomaly detection in mobile networks.
  Keywords: Big Data Analytics; Artificial Intelligence (AI); Machine Learning; Anomaly Detection; Mobile Network Security; Intrusion Detection; Cybersecurity; Data-Driven Intelligence.
  DOI: 10.1504/IJMNDI.2026.10075471
   
  
• Intelligent Signal Processing and Spectrum Management: A Hybrid Framework Combining Optimised Stockwell Transform and Deep Learning for Wide Area Network Planning and Radar Wave Classification  
  by Bedilu Ababu Teka, Demissie Jobir, R.A.M. SEWAK SINGH, Vivek Singh Bhadouria, Bijaya Paikray 
  Abstract: Recent methods, such as spectrograms, Wigner-Ville distributions, or wavelet transforms, are highly sensitive to noise and interference and may produce distorted TFR, reducing the classification accuracy of radar wave classification. To address these problems, this study proposes a hybrid model integrating convolutional neural networks (CNNs), machine learning, and an optimised Stockwell transform (OpST). The optimisation of the Stockwell transform leverages particle swarm optimisation to maximise energy concentration. This proposed method captures high-resolution features when processed through CNN transfer learning models such as MobileNetV2 (MNv2). The simulation results have shown that the MNv2 model and support vector machine (SVM) achieved a precision of 100%, a recall of 99%, an F1-score of 99% for radar wave classification, and an average Accuracy of 99.61% among all the evaluated CNN Models. This innovative combination provides a comprehensive, and interference mitigation, marking a substantial advancement in the fields of signal processing and spectrum management.
  Keywords: Transfer Learning; Time-frequency; convolutional neural network; K-Nearest Neighbors; MobileNetV2; Support Vector Machine; Radar waveforms; Wide Area Network.
  DOI: 10.1504/IJMNDI.2026.10075472
   
  
• Energy-Efficient Wireless Sensor Networks Using Hybrid Mutation Harris Hawks Optimisation for Cluster  
  by Palanikumar S, Ponraj A, Arunjunai Karthik K, Amsaveni M 
  Abstract: Clustering is the most promising approach that can reduce energy consumption in WSNs. However, optimal clustering is decided based upon the cluster head (CH), which should be energy efficient. As a result, these inefficiencies cause massive network lifetime and overall performance loss. These ultimately reduce the network's efficiency and its lifespan. Inspired by the efficient foraging behaviours of the albatrosses, the proposed algorithm optimized a selection in WSNs of cluster heads that resulted into the minimum possible value of the EC while ensuring network coverage and DT reliability. Therefore, HMAOA-DE strikes a balance between exploitation and exploration within a radical time synergy so that the better results are achievable towards energy efficiency and network longevity compared to traditional algorithms. The proposed approach is demonstrated to be effective and robust by extensive simulations along with comparisons with existing methods; it thus promises real-world deployment in energy-constrained WSN environments.
  Keywords: Wireless Sensor Networks (WSN); Energy Efficient; Clustering (EEC); Hybrid Albatross Optimization Algorithm with Differential Evolution (HAOA-DE).
  DOI: 10.1504/IJMNDI.2026.10075896
   
  
• Design and realisation of a low-power, high-concurrency SDR-based communication system at 230 MHz  
  by Li Shang, Jiaju Zhang, Xianglong Meng, Wenjiang Pei, Junpeng Zhang 
  Abstract: Software-defined radio (SDR) devices have significantly advanced communication networks by reducing the cost and development time of radio frequency (RF) designs. Their programmability enhances system capabilities, making them valuable for both research and application-driven tasks. With the growth of the Internet of Things (IoT), the need to validate, process, and decode numerous incoming signals has increased - an area where SDRs are highly effective. This paper explores the integration of surface acoustic wave (SAW) devices and SDRs for wireless, in situ sensor response measurements. SAW devices are employed for time delay analysis, while SDRs capture signal data across the 218-230 MHz range using 1921 samples. The inverse Fourier transform is applied to convert frequency-domain data to the time domain. Signal quality is evaluated against measurements from a commercial vector network analyser (VNA). using a LimeSDR Mini, achieves reliable time delay detection with results closely matching those of the VNA.
  Keywords: power amplifier (PA); local oscillator (LO); radio frequency (RF); high frequency (HF); AOA; angle of attack; FIR; finite impulse response.
  DOI: 10.1504/IJMNDI.2026.10073945