Forthcoming and Online First Articles

International Journal of Internet Protocol Technology

International Journal of Internet Protocol Technology (IJIPT)

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 Articles will not be published until the final proofs are validated by their authors.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 Internet Protocol Technology (3 papers in press)

Regular Issues

  • Deep Learning Models based Classification of Solid Waste   Order a copy of this article
    by Anuradham Muthukrishnan, Santhosh Krishna B. V, Murali Krishna Atmakuri, Usha D 
    Abstract: In order to give the best socio-economic qualities such as environmental preservation, economic sustainability and a decrease in health-related issues Municipal Solid Waste (MSW) management currently needs to be carefully studied. Wastes might be identified by computer algorithms, which would also facilitate their conversion into useful energy. Owing to their high error rate and low accuracy, the present methods of trash classification in municipal solid waste continue to have issues. Convolutional Neural Networks (CNNs) and CNNs built from the ground up using ResNet V2 models trained by transfer learning are intended for the purpose of picture classification. The percentage of occurrences in the validation data set that were correctly classified is known as the validation accuracy, and it stands at 0.938. The model effectively adapts what it learnt from the training data set to the validation data set, as seen by the validation accuracy of 93.8%.
    Keywords: Waste management; organic; non-recyclable; recyclable; CNN; ResNet; VGGNet; InceptionNet; fully-connected DNN; XGB; RF; random forest; XGBoost.
    DOI: 10.1504/IJIPT.2024.10067425
     
  • Design and Implementation of Internet Protocol System - Application for the IOT Platform   Order a copy of this article
    by Rajalingam A, Balaji S. R, Chitra Devi, Sheshang Degadwala 
    Abstract: Virtual objects are quickly becoming an integral component of all Internet of Things systems, and this trend is expected to continue. When something exists in the physical world, it is referred to as a virtual item. A virtual item is the digital version of that object. The Internet of Things platform that is being proposed is different from others that are currently in existence since it provides clients with the opportunity to plug and play both hardware and software services on a single platform. A user interface that is easy to understand has been proposed for the Internet of Things platform. Other features include reliability and security. By using virtual objects, it is possible to complete the tasks of monitoring and controlling Internet of Things devices. As part of a side-by-side comparison that we carried out, the Internet of Things platform that was recommended was tested alongside FIWARE.
    Keywords: internet of things (IoT); cloud of things (CoT); Amazon elastic compute cloud (EC2); IoT marketplace; IoT platform; Raspberry Pi; virtualization.
    DOI: 10.1504/IJIPT.2024.10067599
     
  • Deep Learning Algorithms Providing Security for Wireless Sensor Networks against Malicious Attacks   Order a copy of this article
    by Dinokumar Kongkham 
    Abstract: Small sensor nodes that have limited energy are the building blocks of wireless sensor networks, often known as WSNs. Wireless sensor networks are self-sufficient and space-distributed. A wireless sensor network (WSN) is vulnerable to security concerns because it lacks a central authority and deploys its nodes in a random fashion across the network. A malicious assault is a well-known kind of attack in wireless sensor networks (WSN). This type of attack involves a hacked node impersonating as one of the network nodes and fooling other nodes. Either via the use of cryptographic techniques or by the synchronisation of time, a variety of strategies are created to defend against these attacks. However, due to the autonomous nature of WSNs, these strategies may not be successful. To protect against malicious assaults, this article presents a technique that is both effective and efficient, which is known as the Hamming residue method (HRM).
    Keywords: deep learning; security; wireless sensor network; malicious attacks.
    DOI: 10.1504/IJIPT.2024.10067606
     

 

Return to top