Energy Harvesting and Machine Learning in IoT Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 25 November 2025 | Viewed by 1627
Special Issue Editors
Interests: adaptive sensing; edge computing; energy harvesting; environmental monitoring; low-power electronics; machine learning; optimization methods
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; data analysis; energy harvesting; energy management; environmental monitoring; optimization methods
Special Issues, Collections and Topics in MDPI journals
Interests: energy harvesting; interactive electronic systems; electric vehicles; integrated information systems; indirect measurement methods; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in Internet of Things (IoT) technologies have revolutionized the way we approach sensing, data collection, and decision-making processes in various applications. The progressive implementation of machine learning (ML) and embedded intelligence in IoT sensors has paved the way for more adaptive and autonomous systems that are capable of real-time analysis, even with constrained computational and energy resources. These advancements are crucial for developing robust, energy-efficient solutions that can perform reliably in challenging environments, such as agriculture, environmental monitoring, smart cities, smart transportation, and industrial automation.
The aim of this Special Issue is to compile the latest original research and review articles focusing on the integration of energy harvesting (EH), ML, and embedded intelligence in IoT sensors. We encourage submissions that address the challenges of implementing ML algorithms in resource-constrained systems and propose innovative solutions that advance the efficiency and intelligence of IoT networks. The topics of this Special Issue will include, but are not limited to, the following:
- Adaptive and self-learning operations of IoT sensors;
- Embedded intelligence and on-device ML;
- Data collection, processing, and efficient storage mechanisms;
- Edge computing and resource-optimized data analytics;
- EH technologies and adaptive energy management;
- Lightweight and energy-efficient ML models;
- Predictive algorithms for energy optimization and fault detection;
- Signal processing techniques enhanced by ML;
- Anomaly detection and predictive maintenance using artificial intelligence (AI);
- Optimization of data transmission technologies;
- Reliability and resilience analysis of intelligent IoT systems;
- Intelligent operation and resource management in fifth-generation (5G) and IoT networks;
- Smart agriculture, urban infrastructure, and industrial automation applications;
- Rapid prototyping and simulations of energy-aware AI systems;
- Innovative approaches to hardware–software co-design for embedded systems;
- Case studies demonstrating real-world implementations and performance evaluations.
Prof. Dr. Michal Prauzek
Prof. Dr. Petr Musilek
Prof. Dr. Darius Andriukaitis
Guest Editors
Manuscript Submission Information
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Keywords
- energy harvesting
- machine learning in IoT
- embedded intelligence
- adaptive IoT sensors
- predictive maintenance
- energy-efficient ML models
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