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

  • Distributed Denial of Service Attack Detection Using Machine Learning Classifiers   Order a copy of this article
    by Gautam Rampalli, R. Padmavathy 
    Abstract: Online services risk distributed denial of service attacks due to their availability. These attacks overload system resources and make them unusable by legitimate users. This study aims to analyse publicly available datasets spanning three years. This analysis uses machine learning classifiers to detect and classify the attacks. The experimental results of this approach demonstrate precise attack detection and classification with minimal false-positive rates. This study utilised publicly available datasets and employed machine learning classifiers. Decision tree and random forest classifiers achieved the highest accuracy rates, and the K-nearest neighbours and support vector machine classifiers took longer to execute. Correlation coefficient and recursive feature elimination approaches gave more insights into the features of the utilised datasets. Machine learning models were used to analyse attacks and determine the best accuracies for detection. Machine learning provided favourable detection rates for DDoS attacks, underscoring the importance of algorithm selection.
    Keywords: Denial of Service; Distributed Denial of Service attack detection; Machine Learning Classifiers; Correlation Analysis; Recursive Feature Elimination.
    DOI: 10.1504/IJAHUC.2024.10064418
     
  • A Hybrid Optimisation Enabled Deep Learning for Object Detection and Multi-Object Tracking   Order a copy of this article
    by Thirumalai J, Gomathi M, Sindhu T. S, A. Senthil Kumar, Puviarasi R 
    Abstract: The potential of multi-object tracking (MOT) in academia and industry has drawn growing attention. Despite the various methods that have been suggested to address this issue, it continues to be difficult because of things like sudden changes in appearance and severe object occlusions. In this paper, a Jaya political search optimisation (Jaya-PSO) enabled ShuffleNet is developed for object detection (OD) and MOT. Initially, the input video is fed to video frame extraction. The extracted frames are fed into the object segmentation phase, where the segmentation is done by the mask-regional convolutional neural network (Mask-RCNN), trained by tangent squirrel search optimisation (TSSO). Here, TSSO is the integration of the tangent search algorithm (TSA) and squirrel search optimisation (SSO). Then, the object recognition is performed using ShuffleNet trained by Jaya-PSO, where the Jaya-PSO is from the Jaya algorithm, political optimiser (PO) and TSA. Finally, MOT is done by the Henry gas solubility optimised unscented Kalman filtering (HGSO-based UKF). The HGSO-based UKF is the integration of Henry gas solubility optimisation (HGSO) and unscented Kalman filtering (UKF). The measures utilised for analysis are accuracy, sensitivity, specificity and multiple object tracking precision (MOTP). The proposed method attained 92.9% accuracy, 92.1%sensitivity, 92.9% specificity, and 91.0% MOTP.
    Keywords: object detection; multi-object tracking; mask-regional convolutional neural network; Mask-RCNN; tangent search algorithm; TSA; Jaya algorithm; political optimiser.
    DOI: 10.1504/IJAHUC.2024.10064483
     
  • Throughput Enhancement of RIS and STARRIS Using Adaptive Modulation and Coding for 6G Systems   Order a copy of this article
    by Nadhir Ben Halima, Sajid M. Sheikh 
    Abstract: In this article, the throughput of reconfigurable intelligent surfaces (RIS) is improved using adaptive modulation and coding (AMC). The best modulation and coding scheme (MCS) is selected at the transmitter using the instantaneous signal to noise ratio (SNR) measured at the receiver and sent back on the feedback channel. RIS is placed between the transmitter and the receiver so that the SNR is maximised at the receiver side as RIS reflections are combined coherently at the receiver and results in significant spatial diversity. All reflections on RIS have a zero phase at the receiver. We show that RIS using AMC offers a larger throughput than conventional RIS with fixed MCS for all SNR range. RIS with AMC offers 48, 42, 36, 30, 24, 18 dB gain for N = 256,128,64,32,16,8 reflectors. We obtained 10 dB gain when RIS uses AMC versus 256-QAM. We also improve the throughput of Simultaneously Transmitting And Reflecting RIS (STARRIS) using AMC. We obtained 13 dB gain when STARRIS uses AMC versus 256-QAM.
    Keywords: AMC; RIS; MCS; throughput enhancement; 6G.
    DOI: 10.1504/IJAHUC.2024.10064800
     
  • Throughput Optimization of Intelligent Omni-Surfaces (IOS) with Hybrid Solar and RF Energy Harvesting   Order a copy of this article
    by Faisal Alanazi 
    Abstract: This contribution computes the throughput of intelligent omni-surfaces (IOS) when the source recovers power from the sun and radio frequency (RF) signals to be able to transmit data to two users Ut and Ur. Ut and Ur are located at the transmit and reflect spaces of IOS. We compute the harvested energy as well as its SINR. The harvesting process is optimised to maximise the throughput. We investigate the impact of harvesting time t and the number of reflectors of IOS denoted by N. IOS with N = 64 reflectors offers 48 dB versus N = 8. The results are valid for any size of the PV system. Hybrid solar and RF energy harvesting offers 32 dB gain and 3 dB gain versus RF and solar energy harvesting.
    Keywords: intelligent omni-surfaces; IOS; solar energy; RF energy harvesting; SINR; Rayleigh channels.
    DOI: 10.1504/IJAHUC.2024.10064827