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Wireless Sensor Network (WSN) is small embedded devices deployed in large scale network and has capability to sense, compute, and communicate with others. It combines modern sensor, microelectronic, computer, communication, and distributed processing technology. It takes into account in several aspects of live especially in structural health monitoring system of bridge. Due to environmental circumstance, a bridge should be monitored to make sure that it can perform its service safely. Therefore, it is necessary to develop WSN application to monitor bridge condition and send warning message to control room if the bridge is under abnormal condition. This paper proposes the development of automatic WSN system for measuring the level of bridge structural health based on its dynamic responses. The main contribution of this work is developing WSN system for vibration-based bridge condition assessment in which identifies the bridge's fundamental frequency and mode shape. Experimental result shows that the fundamental frequency measured by our proposed system is close to the value analyzed using finite element analysis (FEA) and according to Modal Assurance Criteriation (MAC), our proposed measurement system has correlation with FEA.
Wireless Sensor Network (WSN) consists of embedded devices attaching sensor and microelectronic technology, computational capability, and distributed processing. Due to computing capability, it is interesting to implement in-network processing in WSN. It means that data preprocessing is conducted in network to reduce amount of data and processing time in server. One of in-network processing approach is multiagent-based signal processing. Here, Agents deployed in sensor nodes, including mobile agents, make collaboration for data aggregation and preprocessing. This paper describes our proposed method how multiagent system is implemented in real WSN. The method lists the process how global network information is identified by agents in sensor nodes, mobile agent migration plan is conducted by the intelligent agent in head clusters, and the mobile agent migrates hop by hop and performs collaboration with sensor node agents for data preprocessing. We propose vibration of a concrete beam as the case study. Our simulation and experimental results show that the proposed multiagent system works properly.
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