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Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google." However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.

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Cognitive radio sensor network (CRSN) is an intelligent and reasonable combination of cognitive radio technology and wireless sensor networks. It poses significant challenges to the design of topology maintenance techniques due to dynamic primary-user activities, which in turn decreases the data delivery performance of the network as well as it's lifetime. This paper aims to provide a solution to the CRSN clustering and routing problem using an energy aware event-driven routing protocol (ERP) for CRSN. Upon detection of an event, the ERP determines eligible nodes for clustering according to local positions of CRSN nodes between the event and the sink and their residual energy levels. Cluster-heads are selected from the eligible nodes according to their residual energy values, available channels, neighbors and distance to the sink. In ERP, cluster formation is based on relative spectrum awareness such that channels with lower primary user appearance probability are selected as common data channels for clusters. For data routing, ERP employs hopby-hop data forwarding approach through the CHs and primary/secondary gateways towards the sink. Through extensive simulations, we demonstrate that the proposed ERP provides with better network performances compared to those of the state-of-the-art protocols under a dynamic spectrum-aware data transmission environment.

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