Many wireless sensor networks must sustain long lifetimes on limited energy resources. Two major approaches, transmission power control and sleep scheduling, have been proposed to reduce the radio power consumption in the transmission state and the idle state, respectively. In this paper, we first review existing transmission power control and sleep scheduling approaches and then describe a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in wireless sensor networks. It has two key components: (1) a novel optimization approach called Minimum Power Configuration that minimizes the aggregate radio power consumption of all ratio states and (2) a Unified Power Management Architecture (UPMA) that aims to support the flexible cross-layer integration of different power management strategies. A novel feature of UPMA is that it enables cross-layer coordination and joint optimization of different power management strategies that exist at multiple network layers. additional energy at run time, the amount of energy available remains scarce. Therefore, power management is crucial for making WSNs viable in many realworld applications.Radio is a major source of energy consumption in WSNs. Table I shows the power characteristics of two representative radio interfaces widely used in existing wireless sensor platforms. Two observations can be drawn from this table. First, the transmission power consumption has a wide tunable range, which offers opportunities for significant energy saving. Second, the power consumption in sleep state is several orders of

Wireless sensor networks (WSNs) have emerged as a novel solution to many of the challenges of structural health monitoring (SHM) in civil engineering structures. While research projects using WSNs are ongoing worldwide, implementations of WSNs on full-scale structures are limited. In this study, a WSN is deployed on a full-scale 17.3m-long, 11-bay highway sign support structure to investigate the ability to use vibration response data to detect damage induced in the structure. A multi-level damage detection strategy is employed for this structure: the Angle-between-String-and-Horizon (ASH) flexibility-based algorithm as the Level I and the Axial Strain (AS) flexibility-based algorithm as the Level II. For the proposed multi-level damage detection strategy, a coarse resolution Level I damage detection will be conducted first to detect the damaged region(s). Subsequently, a fine resolution Level II damage detection will be conducted in the damaged region(s) to locate the damaged element(s). Several damage cases are created on the full-scale highway sign support structure to validate the multi-level detection strategy. The multi-level damage detection strategy is shown to be successful in detecting damage in the structure in these cases.Keywords: multi-level damage detection; full scale structure; wireless sensor network Recently a number of efforts have been conducted using WSNs for SHM in civil engineering community. Researchers at Clarkson University have developed a wireless monitoring system for bridge condition assessment. In the hybrid wireless system, accelerometers are used to obtain dynamic characteristic of the structure, and strain sensors are employed for assessing load rating and static analysis. The performances of the system have been tested on a single-span bridge with 20 wireless sensor nodes (Gangone et al. 2009). Researchers at the University of Illinois at Urbana-Champaign have deployed a large scale WSN for SHM in the new Jindo Bridge in South Korea. This implementation is the first dense deployment of WSNs on a cable-stayed bridge in the world and the largest (in the sense of the number of wireless sensors deployed) of its kind for civil infrastructure to date (Spencer 2009). Researchers at the University of Michigan have validated three output-only modal identification methods (peak-picking, random decrement, and frequency domain decomposition) using a distributed WSN. Experimental validation has also been performed on the balcony of a historic theater in metropolitan Detroit ). Researchers at Lehigh University have proposed an iterative modal identification algorithm for SHM with WSNs. Onboard computational power of wireless nodes is utilized to reduce communication burden and energy consumption for modal identification tasks (Dorvash et al. 2013). Researchers at Georgia Institute of Technology have implemented a wireless mobile sensor network for system identification on a space-frame bridge. Four mobile wireless nodes navigate autonomously to different sections of the bridge for measuring ...

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