Security and Privacy Challenges, Solutions, and Performance Evaluation in AIoT-Enabled Smart Societies

Shahab Ali Khan¹, Tehseen Mazhar^2,3,*, Syed Faisal Abbas Shah⁴, Wasim Ahmad¹, Sunawar Khan², Afsha BiBi⁵, Usama Shah¹, Habib Hamam^6,7,8,9
1 School of Arts and Creative Technology, University of Greater Manchester, Manchester Bolton, UK
2 School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
3 Department of Computer Science and Information Technology, School Education Department, Government of Punjab, Layyah, Pakistan
4 Depertment of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
5 Department of Computer and Software Technology, University of Swat, Swat, Pakistan
6 Faculty of Engineering, Université de Moncton, Moncton, NB, Canada
7 School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
8 International Institute of Technology and Management (IITG), Av. Grandes Ecoles, Libreville, Gabon
9 Bridges for Academic Excellence-Spectrum, Tunis, Tunisia
* Corresponding Author: Tehseen Mazhar. Email: email
(This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.075882

Received 10 November 2025; Accepted 29 January 2026; Published online 10 March 2026

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Abstract

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled Artificial Intelligence of Things (AIoT) systems that support intelligent and responsive smart societies, but it also introduces major security and privacy concerns across domains such as healthcare, transportation, and smart cities. This Systemic Literature Review (SLR) addresses three research questions: identifying major threats and challenges in AIoT ecosystems, reviewing state-of-the-art security and privacy techniques, and evaluating their effectiveness. An SLR covering the period from 2020 to 2025 was conducted using major academic digital libraries, including IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Wiley Online Library, with a focus on security- and privacy-enhancing techniques such as blockchain, federated learning, and edge AI. The SLR identifies key challenges including data privacy leakage, authentication, cloud dependency, and attack surface expansion, and finds that emerging techniques, while promising, often involve trade-offs related to latency, scalability, and compliance. The study highlights future directions including lightweight cryptography, standardization, and explainable AI to support secure and trustworthy AIoT-enabled smart societies.