Secure Edge AI and IoMT: From Personalized Healthcare to Smart Agriculture
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 20 September 2026 | Viewed by 809
Editors
Interests: internet of medical things (IoMT); edge artificial intelligence; smart healthcare; wearable devices; federated learning; smart agriculture
Special Issue Information
Dear Colleagues,
The rapid expansion of the Internet of Medical Things (IoMT) and edge artificial intelligence is revolutionizing how we monitor health, nutrition, and living environments. However, the proliferation of connected devices introduces significant vulnerabilities, making cybersecurity a paramount concern. As systems transition from cloud-centric architectures to edge-based processing, there is a critical need for frameworks that balance real-time performance with rigorous security, data privacy, and threat mitigation. This Special Issue, titled "Secure Edge AI and IoMT: From Personalized Healthcare to Smart Agriculture," explores the development of intelligent, resilient, and adaptive frameworks that bridge the gap between theoretical machine learning and secure deployment in resource-constrained environments.
The scope of this Special Issue encompasses systems that not only detect anomalies in user health but also defend against cyber threats in the device network. We seek high-quality research focusing on hardware–software co-design, secure algorithmic frameworks, and advanced sensor fusion.
Topics of interest include, but are not limited to, the following:
- Cybersecurity in IoMT: Intrusion detection systems, lightweight encryption protocols, and secure authentication architectures for resource-constrained edge devices.
- Privacy-Preserving Intelligence: Implementations of federated learning and secure multiparty computation to protect user data in distributed ecosystems while enabling collaborative model training.
- Smart Healthcare and Assistive Technologies: Secure wearable architectures for continuous physiological monitoring, fall prediction, and elderly safety, utilizing adaptive deep neural networks.
- Precision Nutrition and Food Computing: Computer vision models for automated dietary logging that leverage edge processing to minimize data exposure and latency.
- Smart Agriculture and Livestock Management: Precision livestock management (PLM) systems utilizing secure wearable sensors for automated health surveillance and disease detection in farm animals.
This collection aims to supplement the existing literature by demonstrating how edge AI can be implemented securely to solve real-world problems in healthcare and agriculture.
Dr. Laavanya Rachakonda
Dr. Anand Kumar Bapatla
Guest Editors
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Keywords
- cybersecurity
- edge AI
- internet of medical things (IoMT)
- federated learning
- network security
- smart healthcare
- precision livestock farming
- wearable sensors
- privacy-preserving computing
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