Biblio

Given the COVID-19 pandemic, this paper aims at providing a full-process information system to support the detection of pathogens for a large range of populations, satisfying the requirements of light weight, low cost, high concurrency, high reliability, quick response, and high security. The project includes functional modules such as sample collection, sample transfer, sample reception, laboratory testing, test result inquiry, pandemic analysis, and monitoring. The progress and efficiency of each collection point as well as the status of sample transfer, reception, and laboratory testing are all monitored in real time, in order to support the comprehensive surveillance of the pandemic situation and support the dynamic deployment of pandemic prevention resources in a timely and effective manner. Deployed on a cloud platform, this system can satisfy ultra-high concurrent data collection requirements with 20 million collections per day and a maximum of 5 million collections per hour, due to its advantages of high concurrency, elasticity, security, and manageability. This system has also been widely used in Jiangsu, Shaanxi provinces, for the prevention and control of COVID-19 pandemic. Over 100 million NAT data have been collected nationwide, providing strong informational support for scientific and reasonable formulation and execution of COVID-19 prevention plans.

Opportunistic Situation Identification (OSI) is new paradigms for situation-aware systems, in which contexts for situation identification are sensed through sensors that happen to be available rather than pre-deployed and application-specific ones. OSI extends the application usage scale and reduces system costs. However, designing and implementing OSI module of situation-aware systems encounters several challenges, including the uncertainty of context availability, vulnerable network connectivity and privacy threat. This paper proposes a novel middleware framework to tackle such challenges, and its intuition is that it facilitates performing the situation reasoning locally on a smartphone without needing to rely on the cloud, thus reducing the dependency on the network and being more privacy-preserving. To realize such intuitions, we propose a hybrid learning approach to maximize the reasoning accuracy using limited phone's storage space, with the combination of two the-state-the-art techniques. Specifically, this paper provides a genetic algorithm based optimization approach to determine which pre-computed models will be selected for storage under the storage constraints. Validation of the approach based on an open dataset indicates that the proposed approach achieves higher accuracy with comparatively small storage cost. Further, the proposed utility function for model selection performs better than three baseline utility functions.