Vol. 2 · No. 1
Volume 2, Number 1 · 2026 · Published 3 Jun 2026
Table of contents
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01
Federated Learning with Differential Privacy for Healthcare IoT: A Privacy-Preserving Framework for Collaborative Clinical Modeling
Wearable sensors and connected medical devices have made healthcare Internet of Things (IoT) systems a rich and highly sensitive source of clinical data. Centralizing these data for model training raises privacy, regulatory, and security concerns. This paper proposes a privacy-pr…
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02
A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling in Cloud-Edge Computing Environments
The growth of latency-sensitive applications at the network edge has made task scheduling a central problem in cloud-edge computing, where energy consumption, response time, and resource utilization must be balanced at the same time. Scheduling in heterogeneous, distributed envir…
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03
Explainable AI for Predictive Maintenance in Industry 4.0
Predictive maintenance has become a cornerstone of Industry 4.0, where machine learning models analyze sensor data to anticipate equipment failures and reduce unplanned downtime. The most accurate models, however, are often opaque, and maintenance engineers are reluctant to act o…
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04
Lightweight Deep Learning for Real-Time Anomaly Detection on Intelligent Edge Devices
The proliferation of Internet-of-Things sensors has created a need to detect anomalies where data are generated, yet deep anomaly detectors are typically too large and power-hungry for microcontroller-class devices at the network edge. Streaming raw data to the cloud incurs laten…
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05
Smart Supply Chain Management: A Comprehensive Framework Integrating Artificial Intelligence, Blockchain, and Big Data Analytics
Industry 4.0 technologies have changed how firms plan, execute, and monitor supply chain operations, increasing the need for frameworks that are simultaneously predictive, auditable, and analytically grounded. This paper proposes a Smart Supply Chain Management (SSCM) framework t…
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06
A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling
Energy use is now a primary constraint in heterogeneous computing systems: schedules that minimize the makespan of dependent tasks must also limit the power drawn by the processors that execute them. Energy-aware scheduling on heterogeneous processors is NP-hard, so exact methods…