Forthcoming and Online First Articles

International Journal of Ad Hoc and Ubiquitous Computing

International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC)

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International Journal of Ad Hoc and Ubiquitous Computing (4 papers in press)

Regular Issues

  • Security of STARRIS using Solar Energy Harvesting   Order a copy of this article
    by Raed Alhamad, Hatem Boujemaa 
    Abstract: The security of wireless communications using solar energy is computed in this article. The source S recovers energy from sun to forward data to the destination D. The eavesdropper will receive the data and tries to decode it. The strictly positive secrecy capacity (SPSC) and the secrecy outage probability (SOP) are computed when the source recovers energy from sun. We study the evolution of SOP and SPSC with respect to the hour t when the source recovers energy from sun. The SOP decreases and the SPSC increases at t = 11 versus t = 9, 7 AM as the recovered energy is very large. The security enhances as the harvesting time 0 < < 1 increases. The security enhances as S is close to D. The security of wireless communications using solar energy was not yet derived. We also enhance the security by adding simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STARRIS) between S and D. The use of STAR-RIS enhances the secrecy outage probability by 10 and 30 dB when 8 and 12 reflectors are used.
    Keywords: Solar energy; security; STARRIS; SPSC; SOP.
    DOI: 10.1504/IJAHUC.2024.10065989
     
  • RFID-Enabled Protocol based on Eigenvalues and Eigenvectors of a Matrix   Order a copy of this article
    by Haradhan Ghosh, Pramod Kumar Maurya, Satya Bagchi 
    Abstract: Radio Frequency Identification technology has been rapidly embraced throughout different wireless communication domains as one of the primary identification solutions for the Internet of Things. Most present RFID authentication techniques are susceptible to a wide range of malicious attacks. A typical security feature in RFID authentication protocols is key updating, which leads to desynchronisation attacks. We propose a shared key update approach to overcome this issue by the hash function, properties of eigenvectors of a matrix, and addition and concatenation operations. Formal and informal security analyses of the proposed protocol reveal that it is durable to numerous assaults. To simulate the protocol, we apply the Scyther tool, and the findings suggest that the proposed protocol may endure severe assaults. The performance analysis also shows that it has less computation costs on the tag side, making it suitable for use in real-time control systems.
    Keywords: Eigenvalue; Eigenvector; RFID security; Authentication protocol; Privacy.
    DOI: 10.1504/IJAHUC.2024.10066287
     
  • Clustering-Assisted Privacy Perseveration Model for Data Mining   Order a copy of this article
    by Mohana S., T.M. Nithya, S.Nikkath Bushra, R. Vasanthi, K.S. Guruprakash, Sudha Rajesh 
    Abstract: Data mining techniques are used to examine the data in order to reveal hidden patterns. While preserving the privacy of individual records, privacy preserving data mining (PPDM) technology enables us to extract meaningful information from massive volumes of data. This paper proposes the two stages of the privacy preservation are data sanitisation and data restoration. The clustering, key generation and key pruning elements of the data sanitisation process are all carried out in a distributed environment. The key is pruned using the deep maxout model to make any last modifications after being formed using the hybrid optimisation, Tasmanian updated Namib beetle optimisation (TUNBO), which combines the Tasmanian devil optimisation (TDO) and Namib beetle optimisation (NBO) algorithms. In the data restoration step, which is the reverse of sanitisation, the sanitised data is also retrieved. In the meantime, the correlation coefficients are 85.64%, 88.76%, 75.94%, 74.67%, and 82.67%, compared to other models.
    Keywords: data mining; privacy preservation; deep maxout; k-means; hybrid optimisation.
    DOI: 10.1504/IJAHUC.2024.10066400
     
  • Deep Learning-Based Lung Cancer Detection using CT Images   Order a copy of this article
    by Suguna Mariappan, Diana Moses 
    Abstract: This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.
    Keywords: computed tomography; bilateral filter; squeeze U-SegNet; EfficientNet; ShuffleNet.

 

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