Abstract:Public spaces are equipped with 'public actuators', e.g., HVAC, lighting fixtures, speakers, or streaming TV channels to ensure their visitors' comfort. However, many public actuators rarely allow the visitors to adjust their operation, limiting their utility and fairness across the visitors. Also, the social bar is often too high to speak up one's preference and attempt to change an actuator's operation. Social control and use of IoT devices is an underexplored new direction of research even with its huge pot… Show more
In the context of the recent emergence of the internet of things (IoT), human users and IoT-based services are interacting via physical effects such as light and sound. Therefore, it is necessary to consider the quality of the delivery of physical effects to users by IoT devices for selecting services in IoT environments. However, traditional service-selection algorithms focus primarily on the network-level quality of service (QoS), such as latency and throughput. In this study, we improve on the visual-service effectiveness metric developed in our previous work to measure the effectiveness of the personalized delivery of physical effects of visual services to users by considering user-and application-specific factors. We evaluate the metric by conducting a user study, and the results show that the metric reflects users' perceived effectiveness with high accuracy. We also investigate the use of virtual reality (VR) to imitate physical environments for efficient evaluation of the metric. Based on this metric, we develop a dynamic effect-driven output-service selection agent (DEOSA) that selects output services dynamically by considering the effectiveness of service-effect delivery. By adopting a stateof-the-art reinforcement-learning algorithm, DEOSA can learn the optimal policy for selecting output services that can be generalized to various environments. We evaluate DEOSA in simulated IoT environments and show that it can learn the optimal policy successfully; it generally performs better than traditional greedy algorithms in terms of the visual service effectiveness metric and the replacement overhead in randomlygenerated test environments.
Public spaces, where we gather, commune, and take a rest, are the essential parts of a modern urban landscape, enriching citizen's everyday life [3]. How we share these spaces are considered an indicator of the quality of life. Public spaces thus have a responsibility to provide comfort and satisfaction to any visitors. However, in most times, the operations of the spaces are managed in rather an exclusive manner.An inherent obstruction of inclusive sharing of a public space sits in the exclusive modes of actuators' control interfaces. Existing control interfaces, structured as buttons and dials, premise the activation from a single owner, lacking ability to incorporate the various needs of visitors. Even with IoT-enabled actuators, the philosophy of one-on-one control stays the same, e.g., opening up access to authenticated owners. Therefore, while the space is open to the public, its operational details, i.e., activation of actuators, are decided and managed without reflecting occupants' preferences [1].However, it is not straightforward to realize a democratic control of public actuators. Inherent uncertainty of the public spaces imposes several challenges in a system design. We cannot predict who the visitors are. The visitors freely enter/leave public places at arbitrary moments. Such visits are often short and/or happen only once. The visitors are stranger to each other; having a synchronous discussion among them are highly unlikely to happen. Hence in the public spaces, conventional decision-making strategies (e.g.,
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