Improving IoT Security and Efficiency Through Advanced Data Analysis Method
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 899
Editors
Interests: IoT security; adversarial examples; AI security
Interests: IoT security; AI security; privacy-preserving computation; blockchain and applied cryptography
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Special Issue Information
Dear Colleagues,
The rapid proliferation of the Internet of Things (IoT) has interconnected billions of devices across critical infrastructures, smart cities, industrial automation, and personal ecosystems. While IoT generates unprecedented volumes of data, it also introduces complex challenges in security, resource constraints, and operational efficiency. Traditional approaches often struggle to provide adaptive and scalable solutions for these dynamic, heterogeneous environments.
This Special Issue will focus on leveraging advanced data analysis methods—including machine learning, deep learning, federated learning, and time-series analytics—to holistically enhance IoT systems. We seek submissions that utilize data-driven intelligence to simultaneously fortify security defenses and optimize operational efficiency, thereby enabling the development of smarter, more resilient, and sustainable IoT deployments. We also invite the submission of high-quality original research and review articles that present novel algorithms, practical implementations, and rigorous evaluations, bridging the gap between data analysis theory and IoT system praxis.
Topics of interest include, but are not limited to, the following:
- Security Enhancement:
Anomaly and intrusion detection using streaming analytics;
AI-powered threat intelligence for IoT networks;
Privacy-preserving data analysis (e.g., federated learning, differential privacy);
Adversarial attack and defense for IoT sensor data and embedded ML models.
- Efficiency Optimization:
Predictive maintenance and fault diagnosis using sensor data mining;
Energy-efficient IoT communication and edge computing via data-driven scheduling;
Resource allocation and load balancing using reinforcement learning;
IoT data compression and scalable analytics for constrained devices.
- Integrated Solutions:
Cross-layer frameworks jointly improving security and energy efficiency;
Real-time analytics platforms for large-scale IoT monitoring and control;
Benchmark datasets and evaluation metrics for IoT data analysis methods.
We invite the submission of original research articles, comprehensive reviews, and case studies that demonstrate novel algorithms, practical implementations, and rigorous evaluations. This Special Issue aims to bridge the gap between data analysis theory and IoT system praxis, fostering intelligent, secure, and efficient IoT ecosystems for the future.
Dr. Yaoyuan Zhang
Dr. Zhitao Guan
Dr. Li Tan
Guest Editors
Manuscript Submission Information
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Keywords
- Internet of Things
- IoT security
- advanced data analytics
- machine learning
- federated learning
- privacy-preserving analytics
- adversarial attack and defense
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