Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time

Rac1 Is Required for Pathogenicity and Chm1-Dependent Conidiogenesis in Rice Fungal Pathogen Magnaporthe grisea

Rac1 is a small GTPase involved in actin cytoskeleton organization and polarized cell growth in many organisms. In this study, we investigate the biological function of MgRac1, a Rac1 homolog in Magnaporthe grisea. The Mgrac1 deletion mutants are defective in conidial production. Among the few conidia generated, they are malformed and defective in appressorial formation and consequently lose pathogenicity. Genetic complementation with native MgRac1 fully recovers all these defective phenotypes. Consistently, expression of a dominant negative allele of MgRac1 exhibits the same defect as the deletion mutants, while expression of a constitutively active allele of MgRac1 can induce abnormally large conidia with defects in infection-related growth. Furthermore, we show the interactions between MgRac1 and its effectors, including the PAK kinase Chm1 and NADPH oxidases (Nox1 and Nox2), by the yeast two-hybrid assay. While the Nox proteins are important for pathogenicity, the MgRac1-Chm1 interaction is responsible for conidiogenesis. A constitutively active chm1 mutant, in which the Rac1-binding PBD domain is removed, fully restores conidiation of the Mgrac1 deletion mutants, but these conidia do not develop appressoria normally and are not pathogenic to rice plants. Our data suggest that the MgRac1-Chm1 pathway is responsible for conidiogenesis, but additional pathways, including the Nox pathway, are necessary for appressorial formation and pathogenicity

Techniques and tools for model-based design and analysis of embedded real-time software.

As Embedded Real-Time (ERT) systems become more complex and safety-critical, there is a trend to raise software development level of abstraction from programming languages to models. We have developed a suite of techniques and tools to improve industry acceptance of model-driven development of ERT software. As a result of collaboration among multiple institutions, an end-to-end tool-chain has been developed for the design and analysis of ERT software, with Avionics Mission Computing (AMC) as the main target application. As part of the tool-chain, we have developed a tool called AIRES for model-level static analysis. Compared to traditional static analysis techniques that work at the level of programming languages, AIRES works at a higher level of abstraction, and provides valuable dependency and timing information to the engineer at an early stage of the design cycle. AIRES mainly focuses on the static structural aspects while largely ignoring the dynamic behavior of component interactions. We use model-checking to formalize the natural language description of the dynamic behavior of the AMC software, and verify safety and liveness properties. We also present several techniques to improve scalability of model-checking by exploiting application-level domain semantics. To bridge the gap between logical models and implementation on the physical execution platform, many UML tools come with automatic code generators that translate models into code in a programming language. However, current code generation technology generates functional code without considering non-functional and real-time issues. We have adapted the schedulability analysis algorithm by Harbour, Klein and Lehoczky to fit the native runtime model of UML-RT, a UML profile widely used in the telecom domain. This algorithm can be used during state-space exploration to synthesize an implementation architecture for a logical UML-RT model that satisfies timing constraints. In summary, the techniques and tools developed in this thesis address multiple aspects of model-driven development of ERT software, in order to shift the focus of the software development process from programming language-level to the model-level, and reduce the overall system development cost.Ph.D.Applied SciencesComputer scienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124418/2/3138164.pd

Partitioned Multiprocessor Scheduling of Mixed-Criticality Parallel Jobs

Motivated by the increasing trend in embedded systems towards platform integration, there has been an increasing research interest in scheduling mixed-criticality systems. However, most existing efforts have concentrated on scheduling sequential tasks and ignored intra-task parallelism. In this paper, we study the scheduling of mixed-criticality parallel jobs on multiprocessor platforms. We propose a synchronous mixedcriticality job model, where each job consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. A novel MinLoad algorithm is developed to decompose mixed-criticality parallel jobs into mixed-criticality sequential jobs. This decomposition enables us to leverage existing mixed-criticality scheduling algorithms and schedulability analysis to the multiprocessor scheduling of mixed-criticality parallel jobs. In addition, our MinLoad job decomposition algorithm is designed to make the decomposed mixed-criticality sequential tasks easier to schedule, and thus requires smaller-sized multiprocessor platforms for the mixedcriticality systems