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The extensions of the environment with the integration of sensing systems in any space, in conjunction with ubiquitous computing are enabling the so-called Smart Space Sensor Networks. This new generation of networks are offering full connectivity with any object, through the Internet of Things (IoT) and/or the Web, i.e., the Web of Things. These connectivity capabilities are making it feasible to sense the behaviours of people at home and act accordingly. These sensing systems must be integrated within typical elements found at home such as furniture. For that reason, this work considers furniture as an interesting element for the transparent location of sensors. Furniture is a ubiquitous object, i.e., it can be found everywhere at home or the office, and it can integrate and hide the sensors of a network. This work addresses the lack of an exhaustive study of the effect of furniture on signal losses. In addition an easy-to-use tool for estimating the robustness of the communication channel among the sensor nodes and gateways is proposed. Specifically, the losses in a sensor network signal due to the materials found within the communication link are evaluated. Then, this work proposes a software tool that gathers the obtained results and is capable of evaluating the impact of a given set of materials on the communications. This tool also provides a mechanism to optimize the sensor network deployments during the definition of smart spaces. Specifically, it provides information such as: maximum distances between sensor nodes, most suitable type of furniture to integrate sensors, or battery life of sensor nodes. This tool has been validated empirically in the lab, and it is currently being used by several enterprise partners of the Technological Centre of Furniture and Wood in the southeast of Spain.

Sensors integrated within a product can provide lots of information about how the product is used and its status. Easy to extract information, such as hours used per day, temperature or humidity could be very valuable to the manufacturer in order to improve its product for real use. The manufacturer may realize that his/her initial estimations do not match reality and may decide to select different materials or design based on real data.For non-technological products the integration of sensors is not trivial and has not been analyzed prior to this work. The selection of suitable sensors is critical in order to achieve an accurate description about product use. This paper analyzes this problem focusing on a furniture product, more specifically a sofa, as a case study of a non-technological product. First, we compare different kinds of suitable sensors in relation to furniture integration, how good they describe the product usage and other important variables such as material degradation. We later describe the experiments that have been used to validate the previous assumptions. The experimental results summarize the usefulness and accuracy of each sensor data for describing the product use and/or degradation. Finally, the paper proposes an architecture for a complete "web of things" system capable of gathering the information from the WSN and sending it to a remote server. This architecture has been implemented with a ZigBee WSN and a coordinator node with Ethernet connectivity.

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In order to improve the emergency response of medical services to motorcycle accidents, the EU-funded i-VITAL project has developed an integrated system for providing real-time vital sign readings to emergency teams so that an adequate emergency response can be prepared in advance. The use of helmets is compulsory and they already include other enhanced functionalities (such as Bluetooth hands-free headset, or even GPS support). However, ECG has been performed almost solely through skin contact to both sides of the body, whereas direct electrical heart signal monitoring (ECG-like) in helmets remains an unsolved problem. This paper presents the work and results on ECG-like measurements in the head area using EPIC sensors. This work was part of the i-VITAL project research, with the goal of constructing a novel vital sign monitoring system for seamless integration into helmets