Sensing Moisture in Timber: Measurement Techniques and IoT Monitoring Systems for Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".
Deadline for manuscript submissions: closed (20 July 2021) | Viewed by 9963
Special Issue Editor
Interests: hardware implementation of algorithms; wood moisture sensing; IoT for sensor networks; hyperspectral sensing and processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sensing moisture in timber is not trivial when dealing with low moisture content values. Dry timber is essential to avoid wood deterioration and keep its natural strength, especially when used in buildings, supporting frames, furniture, floors, walls, or any architectural design based on timber. Traditionally, timber has been used in construction for ages and is still used due to its isolation properties, strength, availability, and sustainability.
However, the main problem for this material lies in the deterioration that may arise in the case of biological attacks or moisture presence. It leads to an alteration of the internal structure losing the strength and, in the long term, causing the collapse of the structure.
Monitoring the moisture content of timber during the complete process from rough timber to its final use is important to maintain timber in good condition. In the case of hidden timber structures, it is common that the detection of problems comes too late, necessitating costly processes of restoration.
For adequate monitoring of timber moisture content, two main areas must be combined: an accurate measurement device and a monitoring system. Regarding accurate moisture content measurement, there are multiple techniques based on resistance, capacitance, microwaves, or a combination of different methods. This is an important area of research since moisture content values below 10% and the special characteristics of wood (internal structure, species) make it difficult to obtain an accurate value. Only complex and expensive devices may obtain moisture content values accurately. Low power and reduced size devices able to measure moisture content need to be developed for sensing timber in multiple locations by using remote monitoring. This Special Issue admits novel timber measurement methods providing accurate moisture content.
Traditionally, moisture content measurement was manually done by an operator using a handheld device checking different locations in the structure, as long as they were accessible. With the advent of IoT (Internet of Things), it is possible to deploy sensor networks that can gather information from multiple nodes, extract useful information, infer data trends, and obtain classification values from acquired data. This Special Issue welcomes any contribution related to communication systems valid for data transmission from nodes to a data server using different techniques, media, and protocols. The Special Issue also welcomes any contribution related to complete IoT systems (data server, user interfaces, etc.) and data analysis algorithms intended for timber analysis, especially related to moisture content analysis.
The list of proposed topics, but not limited to are the following:
- Sensors for timber moisture content analysis and other properties in general
- Timber sensing devices for remote sensing
- Timber sensing devices for specific applications: drying ovens, stocked timber, structural timber, furniture, and others
- Communication protocols intended for timber sensing nodes: low power, long-range, etc.
- IoT infrastructure and applications for timber monitoring
- Timber data analysis methods to detect and predict timber deterioration
- Timber monitoring applications: heritage monitoring, civil infrastructures (bridges, columns, parking, public spaces, decorative areas), buildings, and houses
Prof. Dr. Alfredo Rosado
Guest Editor
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Keywords
- timber analysis
- moisture content measurement in wood
- remote monitoring
- IoT applications for timber sensing
- timber data analysis
- timber health detection
- timber deterioration prediction
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