Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.Comment: 11 pages, 7 figure

A Pervasive Middleware for Activity Recognition with Smartphones

Title from PDF of title page, viewed on August 28, 2015Thesis advisor: Yugyung LeeVitaIncludes bibliographic references (pages 61-67)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015Activity Recognition (AR) is an important research topic in pervasive computing. With the rapid increase in the use of pervasive devices, huge sensor data is generated from diverse devices on a daily basis. Analysis of the sensor data is a significant area of research for AR. There are several devices and techniques available for AR, but the increasing number of sensor devices and data demands new approaches for adaptive, lightweight and accurate AR. We propose a new middleware called the Pervasive Middleware for Activity Recognition (PEMAR) to address these problems. We implemented PEMAR on a Big Data platform incorporating machine-learning techniques to make it adaptive and accurate for the AR of sensor data. The middleware is composed of the following: (1) Filtering and Segmentation to detect different activities; (2) A human centered adaptive approach to create accurate personal models, leveraging on the existing impersonal models; (3) An activity library to serve different mobile applications; and (4) Activity Recognition services to accurately perform AR. We evaluated recognition accuracy of PEMAR using a generated dataset (15 activities, 50 subjects) and USC-Human Activity Dataset (12 activities, 14 subjects) and observed a better accuracy for personal trained AR compared to impersonal trained AR. We tested the applicability and adaptivity of PEMAR by using several motion based applications.Introduction -- Related work -- Middleware for gesture recognition -- Implementation and applications -- Results and evaluation -- Conclusion and future wor

Ab Initio Structural Studies of Cyclobutylmethyl Cations: Effect of Fluoroalkyl Groups on the Relative Stability of the Carbocations

Ab initio calculations at MP2/cc-pVTZ level show that the trifluoromethyl group has a strong destabilizing effect on the nonclassical, σ-bridged cyclobutylmethyl cations. The GIAO-MP2 derived 13C NMR chemical shifts indicate substantial charge delocalization from the neighboring cyclobutyl ring for carbocations with an α-fluorolkyl group as compared to the 1-cyclobutylethyl cation, and this enhanced charge delocalization in case of the α-(trifluoromethyl)cyclobutylmethyl cation would lead to the ring-opening rearrangement to form the relatively more stable nonclassical primary cyclobutylmethyl cation, in which the carbocation center is farthest from the strongly electron-withdrawing trifluoromethyl group

Significance of abdominal manifestations in dengue fever

Background: Dengue Fever is an Infectious condition caused by flavo virus. It is an epidemic since 4 years and its prevalence is increased in the recent years in India. The increase in India is due to rapid urbanization, population growth, increased international travel and global warming. But dengue fever is now being reported from rural backgrounds due to poor sanitation and stagnant water sources.Methods: This is an institutional cross sectional study in which we took patients presenting with fever and various other complaints related to viral fevers for 9months from 2016 June to March 2017 at Rajiv Gandhi Institute of Medical Sciences, Ongole. In this study we included patients who are NS1 Ag positive and dengue ELISA positive only. We excluded whose NS1 Ag test positive but their dengue IgM ELISA report is negative.Results: In this study we have included 94 patients of all age groups who are diagnosed with dengue fever. Next in the list are nausea/vomiting (43.6%) and diarrhea (40.4%) respectively. We highlighted this in conclusion to consider abdominal manifestations association while evaluating pyrexia patients.Conclusions: As usually fluid management and regular monitoring is the main role in the management of dengue cases than platelet or blood transfusions and antibiotics. We concluded that there is significant association between abdominal manifestations and dengue fever. So abdominal manifestations should be considered while evaluating pyrexia patients to rule out dengue association in those patients and prognosis of dengue fever