scholarly journals Data Aggregation and Its Impact on Performance Enhancement

2019 ◽  
Vol 9 (2S) ◽  
pp. 316-319
Keyword(s):  
Performance Improvement ◽  
Data Aggregation ◽  
Performance Metrics ◽  
Performance Enhancement ◽  
Data Availability ◽  
Quality Data ◽  
Data Organization ◽  
Data Handling ◽  
Aggregation Techniques ◽  
Important Position

Wireless sensor network incorporates an innovative aspect called as data handling technologies for big data organization. In today’s research the data aggregation occupies an important position and its emerging rapidly. Data aggregation incudes, process of accumulating the data at node, then either store or transfer further to reach out the destination. This survey depicts about the previous work on data aggregation in WSN and also its impact on the different services. There are number of data aggregation techniques available for reducing the data, processing the data and storing the data. Some of them are discussed here as a review. The data aggregation performed using certain techniques can also be aimed in having energy efficiency, time efficient, security could be in the form of confidentiality, unimpaired, authenticate, freshness, quality, data availability, access control, nonrepudiation, secrecy, secrecy. These are the relevant performance metrics to maintain the better Qos in WSNs applications. The goal of this paper is to display an overview of existing techniques for performance improvement in homogenous/ heterogenous networks.

Count Models to Represent the Impacts of Weather and Infrastructure on Flight Disruptions

2020 ◽  
Vol 2674 (5) ◽  
pp. 510-521
Author(s):  
Sabrena Jahan Ohi ◽  
Amy M. Kim
Keyword(s):  
Incomplete Data ◽  
Performance Metrics ◽  
Quantitative Model ◽  
Data Availability ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Count Models ◽  
The World ◽  
The Relationship

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

An integrated metaheuristic technique based energy aware clustering protocol for Internet of Things based smart classroom

2020 ◽  
Vol 34 (22) ◽  
pp. 2050360
Author(s):  
Jyoti Chauhan ◽  
Puneet Goswami
Keyword(s):  
Internet Of Things ◽  
Data Aggregation ◽  
Performance Metrics ◽  
Energy Aware ◽  
Local Optima ◽  
Smart Classroom ◽  
Cluster Data ◽  
Aggregation Techniques ◽  
Wide Range ◽  
Metaheuristic Techniques

With an advancement in wireless technologies, Internet of Things (IoT) are becoming popular in modern era because of its wide range of applications. In this paper, an IoT enable smart classroom is considered. However, IoT nodes are battery constrained, therefore, development and implementation of energy efficiency IoT based smart classroom turn out be more challenging issue. Recently, many clustering and inter-cluster data aggregation techniques are implemented to minimize the energy consumption of IoT devices. However, designing an energy aware IoT protocol is defined as a NP-hard problem. Therefore, many researchers have utilized various metaheuristic techniques to implement energy aware protocol. However, generally metaheuristic techniques suffer from various issues such as pre-mature convergence, stuck in local optima, poor convergence speed, etc. Therefore, in this paper, an integrated multi-objective optimization is designed for IoT based smart classroom by considering ant colony optimization and crossover operator. The proposed technique is used to improve the inter-cluster data aggregation of IoT based smart classroom. Extensive experiments are performed by considering various performance metrics. Comparative analyses show that the proposed protocol achieves remarkably significant results as compared to the competitive techniques.

scholarly journals The Diamond League athletic series: does the air quality sparkle?

2021 ◽  
Author(s):  
James R. Hodgson ◽  
Lee Chapman ◽  
Francis D. Pope
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Performance Metrics ◽  
Urban Air Pollution ◽  
Quality Data ◽  
Performance Variation ◽  
Short And Long Term ◽  
Respiratory Conditions ◽  
Race Performance

AbstractUrban air pollution can have negative short- and long-term impacts on health, including cardiovascular, neurological, immune system and developmental damage. The irritant qualities of pollutants such as ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM) can cause respiratory and cardiovascular distress, which can be heightened during physical activity and particularly so for those with respiratory conditions such as asthma. Previously, research has only examined marathon run outcomes or running under laboratory settings. This study focuses on elite 5-km athletes performing in international events at nine locations. Local meteorological and air quality data are used in conjunction with race performance metrics from the Diamond League Athletics series to determine the extent to which elite competitors are influenced during maximal sustained efforts in real-world conditions. The findings from this study suggest that local meteorological variables (temperature, wind speed and relative humidity) and air quality (ozone and particulate matter) have an impact on athletic performance. Variation between finishing times at different race locations can also be explained by the local meteorology and air quality conditions seen during races.

Design of Power Quality Management Software Based on Virtual Instrument and Oracle

2013 ◽  
Vol 427-429 ◽  
pp. 2441-2444
Author(s):  
Wei Chen ◽  
Long Chen ◽  
Ming Li
Keyword(s):  
Power Quality ◽  
Software Design ◽  
Virtual Instrument ◽  
Quality Analysis ◽  
Quality Data ◽  
Data Handling ◽  
Data Display ◽  
Report Generation ◽  
Management Capacity ◽  
Power Quality Analysis

This paper presents a software design useful for power quality analysis and data management. The software was programmed in LabVIEW and Oracle, running on Windows in a regular PC. LabVIEW acquires data continuously from the lower machine via TCP/IP. Using its database connection toolkit, LabVIEW accesses to Oracle to stores and retrieve the power quality data according to different indicators. A friendly GUI was built for data display and user operation, taking advantage of the powerful data-handling capacity of LabVIEW and its rich controls. Moreover, Excel reports can be exported using report generation toolkit in LabVIEW. The software greatly improves the data analysis and management capacity.

scholarly journals Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8009
Author(s):  
Abdulmajid Murad ◽  
Frank Alexander Kraemer ◽  
Kerstin Bach ◽  
Gavin Taylor
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Current Data ◽  
Data Driven ◽  
Quality Data ◽  
Empirical Performance ◽  
Adversarial Training ◽  
Scalable Methods

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

scholarly journals Managing contamination delay to improve Timing Speculation architectures

2016 ◽  
Vol 2 ◽  
pp. e79 ◽  
Author(s):  
Naga Durga Prasad Avirneni ◽  
Prem Kumar Ramesh ◽  
Arun K. Somani
Keyword(s):  
Performance Improvement ◽  
Short Path ◽  
Performance Enhancement ◽  
Propagation Delay ◽  
Performance Impact ◽  
Worst Case ◽  
Circuit Delay ◽  
Timing Speculation ◽  
Better Than

Timing Speculation (TS) is a widely known method for realizing better-than-worst-case systems. Aggressive clocking, realizable by TS, enable systems to operate beyond specified safe frequency limits to effectively exploit the data dependent circuit delay. However, the range of aggressive clocking for performance enhancement under TS is restricted by short paths. In this paper, we show that increasing the lengths of short paths of the circuit increases the effectiveness of TS, leading to performance improvement. Also, we propose an algorithm to efficiently add delay buffers to selected short paths while keeping down the area penalty. We present our algorithm results for ISCAS-85 suite and show that it is possible to increase the circuit contamination delay by up to 30% without affecting the propagation delay. We also explore the possibility of increasing short path delays further by relaxing the constraint on propagation delay and analyze the performance impact.

Efficient clustering-based data aggregation techniques for wireless sensor networks

2011 ◽  
Vol 17 (5) ◽  
pp. 1387-1400 ◽  
Author(s):  
Woo-Sung Jung ◽  
Keun-Woo Lim ◽  
Young-Bae Ko ◽  
Sang-Joon Park
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Aggregation Techniques

ICT Based Performance Measurement and Benchmarking Methodology

2020 ◽  
pp. 188-196
Author(s):  
Nadja Yang Meng ◽  
Karthikeyan K
Keyword(s):  
Best Practices ◽  
Performance Measurement ◽  
Performance Metrics ◽  
Performance Enhancement ◽  
Performance Measurements ◽  
Know How ◽  
Performance Benchmarking ◽  
Measurement Framework ◽  
And Performance ◽  
Made In

Performance benchmarking and performance measurement are the fundamental principles of performance enhancement in the business sector. For businesses to enhance their performance in the modern competitive world, it is fundamental to know how to measure the performance level in business that also incorporates telling how they will performance after a change has been made. In case a business improvement has been made, the performance processes have to be evaluated. Performance measurements are also fundamental in the process of doing comparisons of performance levels between corporations. The best practices within the industry are evaluated by the businesses with desirable levels of the kind of performance measures being conducted. In that regard, it is fundamental if similar businesses applied the same collection of performance metrics. In this paper, the NETIAS performance measurement framework will be applied to accomplish the mission of evaluating performances in business by producing generic collection of performance metrics, which businesses can utilize to compare and measure their organizational activities.

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