Special Issue "Wireless Sensors Networks in Activity Detection and Context Awareness"
Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 45823
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
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Nowadays, with the boom of Internet-of-Things (IoT) solutions, context-aware systems have become more commonly implemented in our surroundings, which is due to their reduced cost and ease of use and integration. Furthermore, wireless sensor networks (WSNs) are widely used to collect environmental parameters in homes, buildings, vehicles, etc., where they are a source of information that supports the decision-making process and, in particular, aids activity monitoring and learning. However, the rapid deployment of WSNs requires new solutions in both, machine learning algorithms that identify contexts and activities, and distributed computing architectures that enable the ingestion and processing of vast amounts of new data. Regarding the machine learning solutions, new clustering and classification techniques, reinforcement learning methods, or data quality approaches are required. Related to the distributed computing architectures, new fod/edge computing models, energy harvesting methodologies, or device-to-device communication paradigms are needed.
This Special Issue expects innovative work to explore new frontiers and challenges in the field of WSNs in activity monitoring and context awareness research, including the mentioned new machine learning models, distributed computing architectures, as well as new sensor deployments and use-cases of application of activity monitoring and context awareness in smart environments.
The particular topics of interest include, but are not limited to:
- Sensor deployments for context awareness.
- Use-cases of context awareness and activity monitoring.
- Clustering and classification algorithms for activity monitoring.
- Deep and reinforcement learning in activity monitoring.
- New audio processing algorithms for context recognition.
- New image processing algorithms for context recognition.
- Big Data analytics for context awareness and activity monitoring.
- Data quality and false data detection in WSN.
- Fod/edge computing for WSNs for context awareness.
- Energy harvesting in WSNs for context awareness.
- New device-to-device paradigms for WSNs in context awareness.
- Security in WSNs for context awareness.
- Data privacy in activity monitoring.
- Blockchain and distributed ledger solutions for data veracity and privacy in WSNs.
- Multi Agent Systems
- Organization Based Multiagent Systems
- Virtual Organizations
- Industry 4.0
- NFV and SDN for WSNs.
Dr. Javier Prieto Tejedor
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- context awareness
- activity monitoring
- Fod/Edge computing
- energy harvesting