Editorial: Artificial Intelligence and Cybersecurity in Pervasive Computing
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Abstract
Pervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer vision, and three cybersecurity articles covering cryptography, medical devices, and maritime security.
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References
“State of IoT 2024: Number of connected IoT devices growing 13% to 18.8 billion globally,” IoT Analytics. Accessed: Sep. 29, 2024. [Online]. Available: https://iot-analytics.com/number-connected-iot-devices/
A. Ltd, “What is Ubiquitous Computing?,” Arm | The Architecture for the Digital World. 2024. [Online]. Available: https://www.arm.com/glossary/ubiquitous-computing
“Fine-tuning now available for GPT-4o.” [Online]. Available: https://openai.com/index/gpt-4o-fine-tuning/
“Introducing Orion, Our First True Augmented Reality Glasses,” Meta. [Online]. Available: https://about.fb.com/news/2024/09/introducing-orion-our-first-true-augmented-reality-glasses/
“Llama 3.2,” Meta Llama. [Online]. Available: https://www.llama.com/
N. Sharghivand, F. Derakhshan, L. Mashayekhy, and L. Mohammadkhanli, “An Edge Computing Matching Framework With Guaranteed Quality of Service,” IEEE Transactions on Cloud Computing, vol. 10, no. 3, pp. 1557–1570, 2022, doi: 10.1109/TCC.2020.3005539.
K. Tocze and S. Nadjm-Tehrani, “The Necessary Shift: Toward a Sufficient Edge Computing,” IEEE Pervasive Computing, vol. 23, no. 2, pp. 7–16, 2024, doi: 10.1109/MPRV.2024.3386337.
A. Kumar, T. Braud, S. Tarkoma and P. Hui, "Trustworthy AI in the Age of Pervasive Computing and Big Data," IEEE International Conference on Pervasive Computing and Communications Workshops, 2020, pp. 1-6, doi: 10.1109/PerComWorkshops48775.2020.9156127.
H. Gou, G. Zhang, E. P. Medeiros, S. K. Jagatheesaperumal, and V. H. C. de Albuquerque, “A Cognitive Medical Decision Support System for IoT-Based Human-Computer Interface in Pervasive Computing Environment,” Cognitive Computation, vol. 16, no. 5, pp. 2471–2486, 2024, doi: 10.1007/s12559-023-10242-4.
A. Bimpas, J. Violos, A. Leivadeas, and I. Varlamis, “Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges,” Comput. Network, vol. 239, p. 110156, 2024, doi: 10.1016/j.comnet.2023.110156.
L. Ahuja, R. Simon, and A. Thakur, “Privacy and Security Considerations in Healthcare: Navigating the Challenges of IoT and Ubiquitous Computing,” Smart Technologies in Healthcare Management, CRC Press, 2024.
E. Ahmady, A. R. Mojadadi, and M. Hakimi, “A Comprehensive Review of Cybersecurity Measures in the IoT Era,” Journal of Social Science Utilizing Technology, vol. 2, no. 1, 2024, doi: 10.70177/jssut.v2i1.722.
A. Pasdar, N. Koroniotis, M. Keshk, N. Moustafa, and Z. Tari, “Cybersecurity Solutions and Techniques for Internet of Things Integration in Combat Systems,” IEEE Transactions on Sustainable Computing, pp. 1–20, 2024, doi: 10.1109/TSUSC.2024.3443256.
V. Raju, “Origins and Evolution of Pervasive Computing: A Historical Perspective,” Ubiquitous Computing and Technological Innovation for Universal Healthcare, IGI Global, 2024, pp. 1–32. doi: 10.4018/979-8-3693-2268-0.ch001.
F. Zaman, M. Afzal, P.S. Teh, R. Sarwar, F. Kamiran, N.R. Aljohani, R. Nawaz, M.U. Hassan, and F. Sabah, “Intelligent Abstractive Summarization of Scholarly Publications with Transfer Learning,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 256–270, 2024. doi: 10.33093/jiwe.2024.3.3.16
R. Paulus, C. Xiong, and R. Socher, “A Deep Reinforced Model for Abstractive Summarization,” International Conference on Learning Representations, 2018. [Online]. Available: https://openreview.net/forum?id=HkAClQgA-
A. Vaswani et al., “Attention Is All You Need,” arXiv.org. 2024. [Online]. Available: https://arxiv.org/abs/1706.03762v7
R. Sarwar, B. Ahmad, P.S. Teh, S. Tuarob, T. Thaipisutikul, F. Zaman, N.R. Aljohani, J. Zhu, S.U. Hassan, R. Nawaz, A.R. Ansari, M.A.B. Fayyaz, “HybridEval: An Improved Novel Hybrid Metric for Evaluation of Text Summarization,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 233–255, 2024. doi: 10.33093/jiwe.2024.3.3.15.
A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes, “Supervised Learning of Universal Sentence Representations from Natural Language Inference Data,” Conference on Empirical Methods in Natural Language Processing, M. Palmer, R. Hwa, and S. Riedel, Eds., Copenhagen, Denmark: Association for Computational Linguistics, 2017, pp. 670–680. doi: 10.18653/v1/D17-1070.
R. Goel, M. Alamgir, W. Wahab, M. Alamgir , I. Mehmood , H. Ugail , A. Sinha, “Sibling Discrimination Using Linear Fusion on Deep Learning Face Recognition Models,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 214–2 32., 2024. doi: 10.33093/jiwe.2024.3.3.14
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 2015, arXiv: arXiv:1409.1556. doi: 10.48550/arXiv.1409.1556.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 815–823. doi: 10.1109/CVPR.2015.7298682.
J. Kim, T.S. Ng, and A.B.J Teoh, “Conditional Deployable Biometrics: Matching Periocular and Face in Various Settings,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 302–313, 2024. doi: 10.33093/jiwe.2024.3.3.19.
S. T. Jimoh and S. Al-Juboori, “Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 271–289. 2024. doi: 10.33093/jiwe.2024.3.3.17
A. A. Hady, A. Ghubaish, T. Salman, D. Unal, and R. Jain, “Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study,” IEEE Access, vol. 8, pp. 106576–106584, 2020, doi: 10.1109/ACCESS.2020.3000421.
J. S. Teh and A. Abba, “Towards Analysable Chaos-based Cryptosystems: Constructing Difference Distribution Tables for Chaotic Maps,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 290-301. 2024, doi: 10.33093/jiwe.2024.3.3.18
Y. H.-S. Kam, K. Jones, R. Rawlinson-Smith, and K. Tam, “In Search of Suitable Methods for Cost-Benefit Analysis of Cyber Risk Mitigation in Offshore Wind: A Survey,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 314-328, 2024, doi: 10.33093/jiwe.2024.3.3.20.