Technical advances in ubiquitous sensing, embedded computing, and wireless communication are leading to a new generation of engineered systems called cyber-physical systems (CPS). This field is attracting more and more attention from researchers, practitioners, as well as the governments. Technically, cyber-physical systems are integrations of computation, networking, and physical dynamics, in which embedded devices are massively networked to sense, monitor and control the physical world. CPS has been regarded as the next computing revolution. This revolution will be featured by the envisioned transform that CPS will make on how we interact with the physical world. To facilitate unprecedented interactions between human beings and the physical world, sensor networks will become a crucial ingredient of CPS due to the need for coupling geographically distributed computing devices with physical elements. The proliferation of affordable sensor network technologies has significantly contributed to recent progress in CPS. On the other hand, the unique features of CPS (e.g. cyber-physical coupling driven by new demands and applications) give rise to a lot of open challenges for design and deployment of sensor networks in such systems. In particular, high-confidence CPS requires the employed sensor networks to support real-time, dependable, safe, secure, and efficient operations. In this issue we are focused on latest research results in wireless sensor networks (WSNs) that address key issues related to high-confidence cyber-physical systems and applications. In response to our call-for-papers, we have received 36 submissions, out of which 15 papers are finally accepted as a result of thorough review process by international experts in respective areas. The selection provides a glimpse of the state-of-the-art research in the field. In the paper "A Game Theoretic Approach for Interuser Interference Reduction in Body Sensor Networks", Wu et al present a decentralized inter-user interference reduction scheme with non-cooperative game for body sensor networks (BSNs). A no-regret learning algorithm for reducing the effect of the inter-user interference with low power consumption is proposed. The correctness and effectiveness of the proposed scheme are theoretically proved, and experimental results demonstrate that the effect of inter-user interference can be reduced effectively with low power consumption.

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