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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 1366

Editors

Dr. Laavanya Rachakonda

E-Mail Website
Guest Editor
Department of Computer Science, University of North Carolina Wilmington, Wilmington, NC 28403, USA
Interests: internet of medical things (IoMT); edge artificial intelligence; smart healthcare; wearable devices; federated learning; smart agriculture
Dr. Anand Kumar Bapatla

E-Mail Website
Guest Editor
Department of Computer Science and Cybersecurity, University of Central Missouri, Warrensburg, MO 64093, USA
Interests: distributed networks; blockchains; iot; smart healthcare; 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (2 papers)

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Research

21 pages, 412 KB  
Article
Multi-Agent Edge AI for Coordinated Event Prediction over Multi-Layer Wireless Sensor Networks: A Smart Competitive Approach
by Ahshanul Haque and Hamdy Soliman
Electronics 2026, 15(15), 3331; https://doi.org/10.3390/electronics15153331 - 28 Jul 2026
Viewed by 300
Abstract
Distributed event prediction over large-scale Wireless Sensor Networks (WSNs) requires balancing predictive accuracy against the cost of wireless communication. Centralized inference recovers strong accuracy but demands a continuously active uplink, while pure edge inference loses accuracy under partial observability. This paper proposes a [...] Read more.
Distributed event prediction over large-scale Wireless Sensor Networks (WSNs) requires balancing predictive accuracy against the cost of wireless communication. Centralized inference recovers strong accuracy but demands a continuously active uplink, while pure edge inference loses accuracy under partial observability. This paper proposes a communication-aware distributed inference framework that combines Echo State Network (ESN)-based local inference at edge agents with cluster-level majority voting, confidence fusion, and a confidence-gated upward communication rule across a three-level architecture of edge sensor agents, cluster-level agents, and a base station. A tunable confidence threshold θ, selected on a held-out validation set according to a named deployment objective, exposes three operating modes: High Sensitivity, Balanced, and High Efficiency. The empirical relationship between ensemble confidence Cavg and cluster decision accuracy—measured over ten confidence bins—confirms that high Cavg reliably predicts correct cluster decisions, providing direct empirical justification for using Cavg as the communication trigger. The framework is evaluated against a centralized SVM upper bound and a pure edge ESN lower bound across network sizes from 50 to 500 nodes on a synthetic nonlinear benchmark, with results averaged over five independent seeds. In Balanced mode, the multi-agent system achieves a global detection accuracy of 0.820–0.828 against 0.850 for the centralized SVM while reducing upward communication by 68–82% depending on network size. In High Efficiency mode, communication is reduced by 80–88% while maintaining precision above 0.88. A key scalability finding is that, at a fixed detection objective, the validation-selected threshold θ* increases with network size, yielding greater communication savings as the network grows without degrading detection accuracy. Full article
(This article belongs to the Special Issue Secure Edge AI and IoMT: From Personalized Healthcare to Smart Agriculture)
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23 pages, 49319 KB  
Article
iLog 2.2: Volume and Nutrition Estimation for Mixed Foods via Mask R-CNN and Federated Learning
by Indira Devi Siripurapu, Laavanya Rachakonda, Saraju P. Mohanty and Elias Kougianos
Electronics 2026, 15(7), 1460; https://doi.org/10.3390/electronics15071460 - 1 Apr 2026
Cited by 1 | Viewed by 636
Abstract
Accurately estimating calorie intake and nutrient composition from what we eat remains one of the most practical challenges in maintaining a healthy lifestyle. Manual food logging and database-based estimations are often inaccurate because ingredient proportions and preparation styles vary widely. This paper presents [...] Read more.
Accurately estimating calorie intake and nutrient composition from what we eat remains one of the most practical challenges in maintaining a healthy lifestyle. Manual food logging and database-based estimations are often inaccurate because ingredient proportions and preparation styles vary widely. This paper presents a lightweight, privacy-preserving framework that estimates calories and detailed nutrient values from a single image. The model uses a Mask R-CNN-based segmentation network to identify visible food components, measure their area, estimate their volume using preset height values, and map them to nutritional information obtained from reliable datasets such as USDA and Food-a-pedia. The system integrates federated learning (FL) to ensure privacy by allowing the model to improve collaboratively without sharing raw user data. The proposed architecture achieved a mean Average Precision (mAP) of 96% for detection and 92% for segmentation, confirming its precision and efficiency. The model is trained and evaluated on a curated pizza dataset consisting of 1107 images across 50 topping categories, using a standard train-validation-test split (666/219/222) to ensure reliable performance assessment. The proposed system also achieves low nutrition estimation error, with calorie and nutrient deviations remaining within approximately 3.8% to 11.1% across evaluated metrics. A lightweight mobile interface is demonstrated through a Figma-based prototype mockup to illustrate potential real-world deployment and user interaction. Full article
(This article belongs to the Special Issue Secure Edge AI and IoMT: From Personalized Healthcare to Smart Agriculture)
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