scholarly journals Efficient data retrieval for large-scale smart city applications through applied Bayesian inference

2015 ◽  
Author(s):  
Jin Ming Koh ◽  
Marcus Sak ◽  
Hwee-Xian Tan ◽  
Huiguang Liang ◽  
Fachmin Folianto ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Smart City ◽  
Large Scale ◽  
Data Retrieval ◽  
Efficient Data

Social and Medical Factors in the use and Effectiveness of IUDs

1973 ◽  
Vol 5 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Robert Snowden ◽  
Peter Eckstein ◽  
Denis Hawkins
Keyword(s):  
Large Scale ◽  
Data Retrieval ◽  
Work Load ◽  
Sufficient Evidence ◽  
Contraceptive Device ◽  
Psychological Variables ◽  
The United Kingdom ◽  
Intrauterine Contraceptive ◽  
Efficient Data ◽  
The Difference

The possible influence of psycho-social factors on the use and effectiveness of the intrauterine contraceptive device (IUD) has not been seriously examined until recently. Inquiry into these factors has become feasible as large-scale projects—primarily designed to consider medical factors in IUD use—have been developed. Data obtained from a number of centres within and outside the United Kingdom indicate the existence of variation in the clinical efficacy of the same type of device. This variability suggests that success or failure of a given model does not necessarily depend on the device alone. Factors other than those generally considered ‘medical’ may play a significant role in IUD use and effectiveness.The procedures involved in organizing large-scale multi-clinic IUD trials and including efficient data-retrieval systems are described in detail.In addition, findings are reported on the trial of a stainless steel type of device, the M 213, which has been fitted in sufficient numbers in several UK clinics to permit analysis of variables associated with both the clinic and the doctor responsible for the IUD fitting. Evaluation of the data collected from two single-doctor clinics in two adjacent towns in the southwest of England using the M 213 and served by the same doctor, has revealed significant differences in the net cumulative pregnancy rate associated with the device. Age and parity of the IUD acceptors (and the interaction of age and parity) did not appear to be responsible for the difference. Although the general atmosphere and work load within the clinic setting may have differed it is not thought that this could fully account for the observed variations in the pregnancy rates between the two clinics.There appears to be sufficient evidence to suggest that the traditional purely ’medical’ approach to the examination of IUD use—effectiveness should be extended to include the study of social and psychological variables associated with both the providers and the acceptors of the IUD service.

scholarly journals ScienceEarth: A Big Data Platform for Remote Sensing Data Processing

2020 ◽  
Vol 12 (4) ◽  
pp. 607 ◽  
Author(s):  
Chen Xu ◽  
Xiaoping Du ◽  
Zhenzhen Yan ◽  
Xiangtao Fan
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Large Scale ◽  
Cluster Computing ◽  
Remote Sensing Data ◽  
Data Retrieval ◽  
Hilbert Curve ◽  
Sensing Data ◽  
System A ◽  
Efficient Data

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading

2019 ◽  
Vol 6 (3) ◽  
pp. 4176-4187 ◽  
Author(s):  
Guorui Li ◽  
Jingsha He ◽  
Sancheng Peng ◽  
Weijia Jia ◽  
Cong Wang ◽  
...  
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Large Scale ◽  
Computation Offloading ◽  
Efficient Data ◽  
Efficient Data Collection

scholarly journals A comprehensive evaluation of cache utilization characteristics in large scale WSN considering network driven cache replacement techniques

2018 ◽  
Vol 188 ◽  
pp. 05004
Author(s):  
Christos Panagiotou ◽  
Christos Antonopoulos ◽  
Stavros Koubias
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Resource Conservation ◽  
Data Sources ◽  
Data Cache ◽  
Cache Replacement ◽  
Large Scale Networks ◽  
Result Analysis

WSNs as adopted in current smart city deployments, must address demanding traffic factors and resilience in failures. Furthermore, caching of data in WSN can significantly benefit resource conservation and network performance. However, data sources generate data volumes that could not fit in the restricted data cache resources of the caching nodes. This unavoidably leads to data items been evicted and replaced. This paper aims to experimentally evaluate the prominent caching techniques in large scale networks that resemble the Smart city paradigm regarding network performance with respect to critical application and network parameters. Through respective result analysis valuable insights are provided concerning the behaviour of caching in typical large scale WSN scenarios.

Sign in / Sign up

Export Citation Format

Share Document