A High-Performance Data Accessing and Processing System for Campus Real-time Power Usage

With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirements. Numerous power data would be accumulated to even Tera-byte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. Based on the integration of multiple software subsystems in a layered manner, the proposed power-monitoring platform has been established and is composed of Ubuntu (as operating system), Hadoop (as storage subsystem), Hive (as data warehouse), and the Spark MLlib (as data analytics) from bottom to top. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single-record query, these software modules: HiveQL and Impala SQL had been tested in terms of query-response efficiency. And for the performance exploration on the full-table query function. The relevant experiments have been conducted on the same software modules as well. The kernel contributions of this research work can be highlighted by two parts: the details of building an efficient real-time power-monitoring platform, and the relevant query-response efficiency for reference

Dynamic partitioning of loop iterations on heterogeneous PC clusters

[[abstract]]Loop partitioning on parallel and distributed systems has been a critical problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous PC cluster environments. In the past, some loop self-scheduling schemes have been proposed to be applicable to heterogeneous cluster environments. In this paper, we propose a performance-based approach, which partitions loop iterations according to the performance ratio of cluster nodes. To verify the proposed approach, a heterogeneous cluster is built, and three types of application programs are implemented to be executed in this testbed. Experimental results show that the proposed approach performs better than traditional schemes. © 2007 Springer Science+Business Media, LLC

On utilization of the grid computing technology for video conversion and 3D rendering

[[abstract]]In this paper, we investigate the recent popular computing technique called grid computing, and use video conversion and 3D rendering applications to demonstrate this technology's effectiveness and high performance. We also report on developing a resource broker called Phantom that runs on our grid computing testbed and whose main function is querying nodes in grid computing environments and showing their system information to aid in selecting the best nodes for job assignments to have the jobs executed in the least amount of time. (C) 2009 Elsevier B.V. All rights reserved

Dynamic partitioning of loop iterations on heterogeneous PC clusters

[[abstract]]Loop partitioning on parallel and distributed systems has been a critical problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous PC cluster environments. In the past, some loop self-scheduling schemes have been proposed to be applicable to heterogeneous cluster environments. In this paper, we propose a performance-based approach, which partitions loop iterations according to the performance ratio of cluster nodes. To verify the proposed approach, a heterogeneous cluster is built, and three types of application programs are implemented to be executed in this testbed. Experimental results show that the proposed approach performs better than traditional schemes

Implementation of Intelligent Green Energy Management System

In this work, we mainly apply the cloud infrastructure (IaaS) and virtualization technology to provide the construction services of a green energy management system. First of all, we used MySQL Cluster database technology to build a data storage system which can solve the challenge of large demand. Digital electricity meter data and environmental information are collected efficiently and quickly in the proposed green energy management system. Next, a virtualized user-interface is provided by graphical presentation to facilitate data analysis. Finally, we control the electricity equipment to reduce Power Usage Effectiveness (PUE) and the overall power consumption target-based on this virtualized user-interface of the data analysis

Square Key Matrix Management Scheme in Wireless Sensor Networks

In this paper we propose a symmetric cryptographic approach named Square Key Matrix Management Scheme (SKMaS) in which a sensor node named Key Distribution Server (KDS) is responsible for the security of key management. When the system starts up, the KDS sends its individual key and two sets of keys to sensor nodes. With the IDs, any two valid sensor nodes, e.g. i and j, can individually identify the corresponding communication keys (CKs) to derive a dynamic shared key (DSK) for encrypting/decrypting messages transmitted between them. When i leaves the underlying network, the CKs and the individually keys currently utilized by i can be reused by a newly joining sensor, e.g. h. However, when h joins the network, if no such previously-used IDs are available, h will be given a new ID, CKs and the individually key by the KDS. The KDS encrypts the CKs, with which an existing node q can communicate with h, with individual key so that only q rather than h can correctly decrypt the CKs. The lemmas and security analyses provided in this paper prove that the proposed system can protect at least three common attacks

Indoor CO2 monitoring in a surgical intensive care unit under visitation restrictions during the COVID-19 pandemic

BackgroundIndoor CO2 concentration is an important metric of indoor air quality (IAQ). The dynamic temporal pattern of CO2 levels in intensive care units (ICUs), where healthcare providers experience high cognitive load and occupant numbers are frequently changing, has not been comprehensively characterized.ObjectiveWe attempted to describe the dynamic change in CO2 levels in the ICU using an Internet of Things-based (IoT-based) monitoring system. Specifically, given that the COVID-19 pandemic makes hospital visitation restrictions necessary worldwide, this study aimed to appraise the impact of visitation restrictions on CO2 levels in the ICU.MethodsSince February 2020, an IoT-based intelligent indoor environment monitoring system has been implemented in a 24-bed university hospital ICU, which is symmetrically divided into areas A and B. One sensor was placed at the workstation of each area for continuous monitoring. The data of CO2 and other pollutants (e.g., PM2.5) measured under standard and restricted visitation policies during the COVID-19 pandemic were retrieved for analysis. Additionally, the CO2 levels were compared between workdays and non-working days and between areas A and B.ResultsThe median CO2 level (interquartile range [IQR]) was 616 (524–682) ppm, and only 979 (0.34%) data points obtained in area A during standard visitation were ≥ 1,000 ppm. The CO2 concentrations were significantly lower during restricted visitation (median [IQR]: 576 [556–596] ppm) than during standard visitation (628 [602–663] ppm; p < 0.001). The PM2.5 concentrations were significantly lower during restricted visitation (median [IQR]: 1 [0–1] μg/m3) than during standard visitation (2 [1–3] μg/m3; p < 0.001). The daily CO2 and PM2.5 levels were relatively low at night and elevated as the occupant number increased during clinical handover and visitation. The CO2 concentrations were significantly higher in area A (median [IQR]: 681 [653–712] ppm) than in area B (524 [504–547] ppm; p < 0.001). The CO2 concentrations were significantly lower on non-working days (median [IQR]: 606 [587–671] ppm) than on workdays (583 [573–600] ppm; p < 0.001).ConclusionOur study suggests that visitation restrictions during the COVID-19 pandemic may affect CO2 levels in the ICU. Implantation of the IoT-based IAQ sensing network system may facilitate the monitoring of indoor CO2 levels

Indomethacin protects rats from neuronal damage induced by traumatic brain injury and suppresses hippocampal IL-1β release through the inhibition of Nogo-A expression

BACKGROUND: Nogo-A is a member of the reticulon family of membrane-associated proteins and plays an important role in axonal remodeling. The present study aimed to investigate alterations in Nogo-A expression following traumatic brain injury (TBI)-induced inflammation and neuronal damage. METHODS: A weight-drop device was used to deliver a standard traumatic impact to rats. Western blot, RT-PCR and ELISA were used to analyze the expression of Nogo-A and IL-1β. Nogo-A antisense, and an irrelevant control oligonucleotide was intracerebroventricularly infused. We also performed H & E staining and luxol fast blue staining to evaluate the neuronal damage and demyelination resulting from TBI and various treatments. RESULTS: Based on RT-PCR and western blot analyses, the expression of Nogo-A was found to be significantly upregulated in the hippocampus beginning eight hours after TBI. In addition, TBI caused an apparent elevation in IL-1β levels and severe neuronal damage and demyelination in the tested animals. All of the TBI-associated molecular and cellular consequences could be effectively reversed by treating the animals with the anti-inflammatory drug indomethacin. More importantly, the TBI-associated stimulation in the levels of both Nogo-A and IL-1β could be effectively inhibited by a specific Nogo-A antisense oligonucleotide. CONCLUSIONS: Our findings suggest that the suppression of Nogo-A expression appears to be an early response conferred by indomethacin, which then leads to decreases in the levels of IL-1β and TBI-induced neuron damage

NetFlow Monitoring and Cyberattack Detection Using Deep Learning With Ceph

Figuring the network's hidden abnormal behavior can reduce network vulnerability. This paper presents a detailed architecture in which the collected log data of the network can be processed and analyzed. We process and integrate on-campus network information from every router and store the integrated NetFlow log data. Ceph is used as an open-source distributed storage platform that offers high efficiency, high reliability, scalability, and preliminary preprocessing of raw data with Python, removing redundant areas and unification. In the subanalysis, we discover the anomaly event and absolute flow by three times of standard deviation rule. Keras has been used to classify in-time data collected via a cyber-attack and to construct an automatic identifier template through the Recurring Neural Network (RNN) test. The identification accuracy of the optimization model is around 98% in attack detection. Finally, in the MySQL server, the results of the real-time evaluation can be obtained, and the results of the assessment can be displayed via ECharts