In recent years, human-cyber-physical systems (HCPSs) have become increasingly complex due to the widespread adoption of environmental sensing and behavioral adaption. Apart from the tight coupling between application logic and sensing-adaptation modules, such applications are mainly constrained by erroneous sensing and abnormal adaptation issues, often resulting in misjudgment of scenarios or adaptation behaviors that deviate from intended goals. Reliability in constructing and maintaining such application systems faces significant challenges, especially as human-cyber-physical scenarios exhibit dynamic uncertainties and evolving requirements, further exacerbating the development difficulty. To address these challenges, we design and implement SEPAL, a consistency-driven programming framework and runtime support for HCPSs with reliable environmental sensing and dynamic adaptation. SEPAL simplifies the design of environmental sensing and behavioral adaption in HCPSs through a unified programming framework, and transparently manages the reliability of sensing and the unbiasedness of adaptation through its two built-in consistency-based services. SEPAL also provides a flexible browser-based management interface and a customizable interface design language for ease of usage. Case studies and evaluations demonstrate SEPAL’s facilitation of reliable support for various HCPSs, as well as the effectiveness and efficiency of environmental sensing and behavioral adaption capabilities.

Context-aware systems (a.k.a. CASs) integrate cyber and physical space to provide adaptive functionalities in response to changes in context. Building context-aware systems is challenging due to the uncertain running environment. Therefore, many input validation approaches have been proposed to protect context-aware systems from uncertainty and keep them executing safely. However, in contrast to context-aware systems' prevailing in physical environments, most of those academic solutions (83%) are purely evaluated in simulated environments. In this article, we study whether this evaluation setting could lead to biased conclusions. We build a testing platform, RM-Testing, based on DJI RoboMaster robot car, to conduct the physical-environment based experiments. We select three up-to-date input validation approaches, and compare their performance in the simulated environment and in the physical environment. The experimental results show that all three approaches' performance in simulated environments (improving task success rate by 82% compared with the system without the support of input validation) does differ from their performance in a physical environment (improving the task success rate by 50%). We also recognize three factors (scenario setting, physical platform and environmental model) that affect the performance of input validation approaches, based on an execution model of the context-aware system.