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Open Access

ARTICLE

Lightweight AI-Based Threat Detection and Surveillance Secure Edge-IoT Healthcare Systems

Menwa Alshammeri1,*, Khalid Haseeb2, Mamoona Humayun3, Mona Saleh Alzahrani1
1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
2 Department of Computer Science, Islamia College Peshawar, Peshawar, Pakistan
3 Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, UK
* Corresponding Author: Menwa Alshammeri. Email: email
(This article belongs to the Special Issue: AI-Driven Optimization for Secure and Sustainable Edge IoT Services)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.085737

Received 17 May 2026; Accepted 04 September 2026; Published online 17 September 2026

Abstract

Advances in emerging technologies, such as the Internet of Things (IoT) and artificial intelligence, provide a wide range of real-time applications to the development of smart cities. The physical objects, along with IoT systems, not only provide seamless connectivity to the remote environment but also continuously gather the required data through a sensor-based network. They provide timely information to connected devices, meet their needs, and enhance the flexibility of automated IoT-driven systems. Despite improvements in network maintenance and communication robustness, most existing healthcare systems still encounter challenges in addressing congestion and scalability as data traffic increases. Furthermore, as autonomous activities increase, communication trust among health devices decreases, thereby increasing computational overhead due to privacy concerns and potential attacks. This research proposes an intelligent, edge-enabled machine-learning model for an autonomous environment to identify proactive threats and enhance system trustworthiness by mitigating unauthorized and insecure activities in the healthcare network. The devices are controlled efficiently, with uniform load balancing, using edge computing and heuristic techniques to optimize resource allocation and energy efficiency. It intelligently controls network traffic and reduces on-device computational power by dynamically offloading to selected forwarders, without increasing network consumption or overhead. Moreover, the trusted schemes are designed to increase reliability within a bounded network system and to support the integrity of devices while processing health records at the network edges. Simulation results confirm that the proposed framework achieves improvements in energy overhead, latency, throughput, and response time compared to the RePUF-IoT and AIBS-IoTH schemes.

Keywords

Internet of Things; security; edge networks; trust computing; smart system

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