Forthcoming and Online First Articles

International Journal of Mobile Network Design and Innovation

International Journal of Mobile Network Design and Innovation (IJMNDI)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Mobile Network Design and Innovation (4 papers in press)

Regular Issues

  • Congestion Aware Optimal Routing Path Selection using Hybrid Meta Heuristic Algorithms in Sparse Wireless Sensor Networks   Order a copy of this article
    by G. Mohan Ram, E. Ilavarsan 
    Abstract: A novel energy-efficient clustering-based congestion-awareness routing mechanism has been developed for WSN. In the first stage, some set of sensor nodes are initialized in the WSN environment. The cluster is formed from the group of nodes by the advanced k-means clustering (A-KDC) model. The cluster head (CH) is selected based on some of the objective functions like distance, energy and so on using an exaggerated manta ray optimization algorithm (EXMro). The optimal routing selection has been formed to transmit the packet from source to destination. The packets are transferred from the CHs to the base station using hybrid Multi-objective May Honey Badger optimization (MHB), which selects the optimal path without congestion. The performance of the proposed approach is examined using the NS2 platform. The proposed approach has an average throughput of 99.06% and an average packet delivery ratio (PDR) of 98.45%, indicating higher efficiency than other existing systems.
    Keywords: K-means clustering; Manta ray optimization; May Honey Badger optimization; Optimal routing strategy; Packet delivery ratio; Energy consumption.
    DOI: 10.1504/IJMNDI.2024.10066268
     
  • Machine Learning-Based Routing Protocols for Wireless Sensor Network   Order a copy of this article
    by Ravi Kumar  
    Abstract: An important part of intelligent transportation systems (ITS) is the use of car ad hoc networks, or vehicular ad hoc networks (VANET). There are still a lot of security issues with VANETs, including catastrophic blackhole threats, even though they have a lot of benefits. The deep-learning-based secure routing (DLSR) protocol and the deep-learning-based clustering (DLC) protocol are the part of this work. The DLSR protocol uses deep learning (DL) at each node to decide between secure routing and normal routing. It also builds safe routes at the same time. Its also possible to find out what bad nodes are doing, which helps us choose the best next hop based on how well its fitness function works. To make the fitness function better in both the protocols, we build a deep neural network (DNN) model. The proposed system improves the localisation accuracy.
    Keywords: routing protocol; wireless sensor network; machine learning; deep learning.
    DOI: 10.1504/IJMNDI.2024.10067248
     
  • Network data Security based on Routing Algorithm: Application in Vehicular ad-hoc Networks   Order a copy of this article
    by Sonammittal M.S., Prasanth S.P., Julia Faith S 
    Abstract: One crucial element of intelligent transportation systems (ITS) is the use of vehicle ad hoc networks, or VANETs. Among the many advantages, these networks provide are increased road safety and decreased traffic. Despite their many benefits, VANETs are nonetheless vulnerable to a variety of security risks, such as devastating blackhole attacks. The following is a summary of the primary characteristics and contributions this article made. First, deep learning (DL) capabilities are included into every node in the DLSR protocol, enabling it to build secure routes and decide between secure and conventional routing. Furthermore, we may look at the fitness function value of each choice to decide which is preferable for the next hop by analysing the activity of malicious nodes. Second, it is thought that the DLC protocol acts as a foundation that reduces control overhead and improves node-to-node communication.
    Keywords: deep learning; secure routing; clustering; blackhole; vehicular ad-hoc networks.
    DOI: 10.1504/IJMNDI.2024.10067298
     
  • Adaptable CPW Feed Antenna Optimisation for WLAN and Sub-6 GHz 5G Connectivity   Order a copy of this article
    by Ponraj A, Mohamed Meeran S, Keerthian S 
    Abstract: In the evolving environment of wireless communications driven by 5G networks, the need for antennas that are dynamic and able to adapt to various needs has become important. This article provides a comprehensive review of antennas adapted for 5G applications to provide a basis for design and performance standards. This article first presents the main points and issues arising from 5G networks, especially when considering the additional scales, regarding the need for antennas to adapt to the frequency band and support MIMO settings. Examines cutting-edge reconfigurable antenna design, highlighting advances in tunable components, phased arrays, and advanced materials to increase flexibility in the 5G spectrum. In addition, this research also focuses on the impact of the upgrade on key performance indicators for 5G applications. Through a combination of theoretical analysis and empirical research, this article carefully examines the trade-offs and synergies in the development of antennas for some applications.
    Keywords: 5G,Reconfigurable Antenna; Hexagonal Shape; WLAN; Sub-GHz; S-parameter; VSWR; Peak Gain.
    DOI: 10.1504/IJMNDI.2024.10067310
     

 

Return to top