Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of “communicating concretes,” which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions.
The rapid development of Internet of Things has enabled intelligent products that can be applied to the Industrial 4.0, Smart City, Smart Supply Chain and Smart Buildings. Its autonomy and flexibility make possible to construct intelligent manufacturing systems. In this paper, we present the McBIM project that is based on the concept of communicating materials. A holonic manufacturing approach is proposed to handle these problems. It aims to include a WSN to the product to make it more sensitive and aware of its own internal conditions. In our approach, the "sensing device" is an embedded Wireless Sensor Network that can collect "internal" data of the intelligent material. In this context, Physical and digital parts are defined and related to four main challenges. Some existing solutions of data collection architectures are discussed for the physical part. Furthermore, these algorithms are compared with the required performance metrics of our application. Then we conclude with some perspectives for future work.
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