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Smart cities are the next wave of rapid expansion of Internet of Things (IoT). A smart city is a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services, such as energy, transportation, healthcare, communications, entertainments, education, e-commerce, businesses,

Smart cities are the next wave of rapid expansion of Internet of Things (IoT). A smart city is a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services, such as energy, transportation, healthcare, communications, entertainments, education, e-commerce, businesses, city management, and utilities, to reduce resource consumption, wastage and overall costs. The overarching aim of a smart city is to enhance the quality of living for its residents and businesses, through technology. In a large ecosystem, like a smart city, many organizations and companies collaborate with the smart city government to improve the smart city. These entities may need to store and share critical data with each other. A smart city has several thousands of smart devices and sensors deployed across the city. Storing critical data in a secure and scalable manner is an important issue in a smart city. While current cloud-based services, like Splunk and ELK (Elasticsearch-Logstash-Kibana), offer a centralized view and control over the IT operations of these smart devices, it is still prone to insider attacks, data tampering, and rogue administrator problems. In this thesis, we present an approach using blockchain to recovering critical data from unauthorized modifications. We use extensive simulations based on complex adaptive system theory, for evaluation of our approach. Through mathematical proof we proved that the approach always detects an unauthorized modification of critical data.
ContributorsMishra, Vineeta (Author) / Yau, Sik-Sang (Thesis advisor) / Goul, Michael K (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2017
Description

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on dee

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.

Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).

ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31