Advanced Edge Computing in Wireless Sensor Network
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 41
Special Issue Editors
Interests: networking; cloud computing; 5G/6G cellular networks; cybersecurity; AI
Special Issues, Collections and Topics in MDPI journals
Interests: cybersecurity; security in sensor-based healthcare
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid and widespread proliferation of wireless sensing and actuating devices has driven the need for services that are computation-intensive, delay-sensitive, context-aware, and energy-efficient. Wireless sensors lack the necessary storage and computing resources, and traditional cloud-centric models face issues like latency, bandwidth constraints, and data communication security. Edge computing addresses these challenges by bringing data processing and storage closer to points from where wireless sensor networks collect data. Advanced edge computing further enhances this approach by integrating AI for predictive data analysis, 5G for improved connectivity, and blockchain for enhanced security and energy-efficient design. This Special Issue of Sensors explores the convergence of these technologies with edge-empowered WSNs to transform data collection and processing in agriculture, healthcare, industrial automation, other critical infrastructure systems.
This Special Issue aims to serve as a platform for researchers to contribute original research articles, reviews, frameworks, surveys, and case studies that present recent advances, practical insights, and future directions in advanced edge computing for WSNs. Topics of interest include, but are not limited to, the following:
- Intelligent computation offloading and resource management in WSNs;
- Edge AI and distributed learning in sensor networks;
- Intelligent computation offloading and resource management in WSNs;
- QoS-aware computing offloading in WSNs;
- Secure, privacy-preserving architectures for edge-enabled WSNs;
- Secure communication protocols and trust models for WSN-edge systems;
- Hierarchical AI and edge–cloud coordination for robust security;
- Collaborative sensing and AI-driven inference models;
- Energy-aware and sustainable edge computing in WSN environments;
- Energy-efficient resource management for edge-enabled sensor networks;
- Edge computing for IoT-integrated WSN environments;
- Integration with 5G/6G, IoT, and next-generation internet architectures;
- Hybrid quantum–classical edge computing for WSNs;
- Edge-enhanced WSN applications in smart agriculture, healthcare, industry, and critical infrastructure;
- Field experiments and testbeds for edge intelligence in WSNs;
- Real-time data fusion, quality optimization, and analytics at the edge;
- LLMs for WSN data augmentation and contextual understanding.
Dr. Lav Gupta
Dr. Tara Salman
Guest Editors
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Keywords
- artificial intelligence
- machine learning
- wireless sensor networks
- edge computing
- distributed computing
- predictive data analysis
- energy-efficient edge
- security and privacy
- 5G/6G integration
- QoS-aware offloading
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