ABSTRACT
Purpose: Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully. Methods: This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing. Results: Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities. Conclusions: This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.
ABSTRACT
In total, 18 simple sequence repeat (SSR) markers were used to analyze the genetic diversity and population structure of 59 accessions of cultivated strawberry (Fragaria x ananassa Duch.) from Korea, Germany, United States, United Kingdom, and Japan. In total, 101 alleles were detected with an average of 5.6 per locus and 21 specific alleles were identified. Notably, one genotype (Blonoli from Germany) possessed a maximum of 10 different unique alleles specific to each genotype. The gene diversity varied from 0.027 (EMPaEKO1B) to 0.791 (CFACT110), with an average value of 0.509. PIC values ranged from 0.026 to 0.762 (average 0.454). A model-based structure analysis revealed the presence of two populations. The accessions that were clearly assigned to a single population in which > 70 percent of their inferred ancestry was derived from one of the model-based populations. However, two accessions (3.4 percent) in the sample were categorized as having admixed ancestry. Here, we report detailed information on commercially grown strawberry accessions from five different origins using SSR markers. These results couldbe used for broadening the genetic base of commercially grown varieties.