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Real World Applications Using Parallel computing Techniques in Dynamic Traffic Assignment and Shortest Path Search

Real World Applications Using Parallel Computing Techniques ...

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international conference on intelligent Transportation

作者： Alessandro Attanasi Edmondo Silvestri Pietro Meschini Guido Gentile PTV-SISTeMA DICEA Sapienza University of Rome

As the range of applications for intelligent Transport Systems (ITS) grows wider, the efficiency of the underlying tools for Big data analytics becomes of crucial importance. smart Cities are able to monitor, forecast and (possibly) control the pulse of collective interactions involving networks and environment (such as traffic and pollution) by means of key performance indicators. Technology-guided solutions can proactively support the sustainable development and the optimal management of infrastructures and services, improving the quality of life for both city dwellers and commuters. This requires processing huge amounts of data, continuously streaming in from a variety of fixed sensors (e.g. loops, cameras) and mobile devices (GPS trajectories). In particular, Mobility Control Centres need effective software solutions and fast algorithms to deal with two major problems: Traffic Forecasting and Route Guidance. This paper presents real world examples of large scale applications where both tasks are addressed by implementing parallel computing algorithms, achieving high performances and allowing real time management operations and end-user services. The first test case examines the performance of a routing platform covering the entire Austria region, while the second concerns large instances of dynamic traffic assignment for real-time forecasting.

关键词： Parallel processing Instruction sets Computational modeling Performance gain Heuristic algorithms Cities and towns Real-time systems

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smartphone derived movement profiles to detect changes in health status in COPD patients - A preliminary investigation

Smartphone derived movement profiles to detect changes in he...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Daniel Kelly Seamas Donnelly Brian Caulfield School of Computing and Intelligent Systems Ulster University Northern Ireland St. Vincent University Hospital Dublin Ireland Insight Center for Data Analytics University College Dublin Ireland

Over 3.2 million people in the UK alone have the lung disease Chronic Obstructive Pulmonary Disease. Identifying when COPD patients are at risk of an exacerbation is a major problem and there is a need for smart solutions that provide us with a means of tracking patient health status. smart-phone sensor technology provides us with an opportunity to automatically monitor patients. With sensors providing the ability to measure aspects of a patient's daily life, such a motion, methods to interpret these signals and infer health related information are needed. In this work we aim to investigate the feasibility of utilizing motion sensors, built within smartphones, to measure patient movement and to infer the health related information about the patient. We perform experiments, based on 7 COPD patients using data collected over a 12 week period for each patient, and identify a measure to distinguish between periods when a patient feels well Vs periods when a patient feels unwell.

关键词： Hospitals data collection Acceleration Motion measurement Diseases data analysis

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Green building energy analytics: Challenges and opportunities

Green building energy analytics: Challenges and opportunitie...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Nirmalya Roy University of Maryland Baltimore Baltimore MD US

Green building applications need efficient and finegrained determination of power consumption pattern of a wide variety of consumer-grade appliances through non-intrusive load monitoring (NILM) techniques. Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. In practice, deploying smart plug based NILM and acquiring the low-level power measures of a large number of devices is often difficult or impossible due to the deployment complexity and varying characteristics of devices and thus must instead be employed at the circuit or house-level and inferred through the incorporation of novel usage-based measurement and probabilistic level-based disaggregation algorithm. But the challenges in deploying non-intrusive load monitoring algorithm involve disaggregating individual devices consumption from the aggregate power measurement, as well as modeling and incorporating the usage based prediction. In this talk, I will discuss on advanced machine learning and data analytics algorithms that capture the measurement based approach and circuit level NILM with the autonomous profiling and prediction logic, and the significant practical impact of intelligent use of such profiling techniques for green building applications. Our approach help improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities. The performance of our proposed algorithms on real data traces will be presented. I will conclude this talk with our ongoing research proj

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Capability Adaptive Elastic IoT Architecture

Capability Adaptive Elastic IoT Architecture

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IEEE international conference on smart City

作者： Tao Zhong Kshitij A. Doshi Zhongyan Lu Gang Deng Software and Services Group Intel

ISBN: (纸本)9781509018949

IoT networks, together with the data that flows between inter-networked smart devices are growing at unprecedented rates. Storing the data arriving in high volume at IoT gateways or transferring it to back-end clusters for timely processing and real-time response generation is not always feasible, and generally requires considerable overprovisioning of resources. In typical configurations, a computationally capable front-end gateway is available for coordinating the operation of smart devices and for linking them to computation and communication rich back-end infrastructure. It is attractive to harness the computational capabilities in front-end gateways to achieve elasticity of deployment so that the total throughput and response time of IoT solutions can be optimized by intelligent division of labor between devices, front-end gateways, and back-end servers. This paper proposes an Elastic computing Framework (ECF), an architecture in which programmability of front-end machines is drawn upon to dynamically divide data processing tasks into those that see all of the data but perform simple operations on it, and those that see a transformed subset of the data and extract deep insights from it. This results in lifting the pressure on the network that links the front- and back-ends, and achieves capability adaptive deployment of IoT solutions. In experimental evaluation with one production usage scenario, this approach achieves between six fold and fifty fold reduction in data transferred to back-end clusters, and improves responsiveness by roughly sixteen fold.

关键词： Internet Of Things (IoT) IoT Architecture Machine-to-Machine Interoperability Real-time analytics

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Research on information security issues facing the era of big data 3

Research on information security issues facing the era of bi...

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3rd international conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Ma, Xiaoxing Wu, Di Tianjin Univ Finance & Econ Tianjin Peoples R China

ISBN: (纸本)9783038352679

Today's society, IT technology has rapid updates, data is growing at an alarming rate of accumulation, the era of big data had come. The intelligent terminals are the main source of data sensor data big data era. Big data to people's lives is brings a lot of convenience, to the enterprise is provide more business opportunities. On the other hand, big data increases the risk of leaks important information, information security technology at this stage cannot meet big data information security needs, how to provide information security protection, strengthen information security, construction-related law is also the attendant problems. IT technologies has rapid innovation in today's society, under the influence of emerging technologies such as cloud computing and IOT, data is growing at an alarming speed explosive, massive data marked the era of big data had come. The advent of smart phones, tablet computers, personal computers and other intelligent terminals, as well as data sensors around the world, have become the main source of data in big data era. [1] Market research firm IDC's report released in 2013, the domestic active Android and iOS devices has reached 370 million, the intelligent end-user of network transmission, interactive social networking generating a large amount of data at any time. Statistics show that, there are an average of two million users using the Google search every second, Facebook's users to share over 4 billion copies files a day, Twitter processing daily data capacity of more than 500TB. Baidu cloud computing center's data storage capacity will exceed 4000PB, it can store information equal to the amount more than 20 million National Library. 2013 global total data storage reached 2.7ZB, while in 2015 is expected to reach 8ZB. The amount of China produced more than 0.8ZB (equal to 800 million TB) data in 2013, the amount of data generated in 2020, China will generate more than 10 times the amount of data in 2013, more than 8.5ZB[2]. "Big

关键词： Big data information leakage Information Security

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smart Car Parking: Temporal Clustering and Anomaly Detection in Urban Car Parking

Smart Car Parking: Temporal Clustering and Anomaly Detection...

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international conference on intelligent Sensors, Sensor Networks and Information Processing

作者： Yanxu Zheng Sutharshan Rajasegarar Christopher Leckie Marimuthu Palaniswami Dept. of Computing and Information Systems The University of Melbourne Dept. of Electrical and Electronic Eng. The University of Melbourne

ISBN: (纸本)9781479928446

A major challenge for modern cities is how to maximise the productivity and reliability of urban infrastructure, such as minimising road congestion by making better use of the limited car parking facilities that are available. To achieve this goal, there is growing interest in the capabilities of the emerging Internet of Things (IoT), which enables a wide range of physical objects and environments to be monitored in fine detail by using low-cost, low-power sensing and communication technologies. While there has been growing interest in the IoT for smart cities, there have been few systematic studies that can demonstrate whether practical insights can be extracted from real-life IoT data using advanced data analytics techniques. In this work, we consider a smart car parking scenario based on real-time car parking information that has been collected and disseminated by the City of San Francisco. We investigate whether useful trends and patterns can be automatically extracted from this rich and complex data set. We demonstrate that by using automated clustering and anomaly detection techniques we can identify potentially interesting trends and events in the data. To the best of our knowledge, we provide the first such analysis of the scope for clustering and anomaly detection on real-time car parking data in a major urban city.

关键词： Vectors Cities and towns Support vector machines Clustering algorithms Availability Sensors Standards

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Multi-perspective Process Variability: A Case for smart Green Buildings

Multi-perspective Process Variability: A Case for Smart Gree...

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6th IEEE international conference on Service-Oriented computing and Applications (SOCA)

作者： Murguzur, Aitor Hong-Linh Truong Dustdar, Schahram Information Technologies Area IK4-Ikerlan Research Center Spain Distributed Systems Group Vienna University of Technology Austria

ISBN: (纸本)9781479927012

The variability scale in large-scale Cyber-Physical Systems (CPSs) is high and complex due to the voluminousness, dynamicity and diversity of available computing resources (people, things and software services), domain-specific processes, domain-specific elements (stakeholders, assets and contracts), and their relationships. This requires us to go beyond current variability modeling and management techniques which neglect the complexity and the diversity of relevant stakeholders, data and assets, and thus cannot cope with intelligent business and analytics requirements in dynamic environments, such as smart city management. In this paper, we present a comprehensive analysis for understanding the multi-perspective variability in processes atop people, data and things in CPSs, particularly, for the sustainability governance of smart Green Buildings (SGBs). We examine domain-specific processes and domain-specific elements and their relationships to derive a multiple-perspective variability management for SGBs. On the basis of this, we conceptualize a novel model for the multi-perspective process variability representation.

关键词： smart city smart building variability process configuration

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Advanced analytics methods and intelligent applications in education

Advanced analytics methods and intelligent applications in e...

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2013 IEEE 7th international conference on intelligent data Acquisition and Advanced computing Systems, IDAACS 2013

作者： Jotsov, Vladimir S. P.O. Box 161 Sofia 1113 Bulgaria

ISBN: (纸本)9781479914265

A set of advanced analytics methods is investigated aiming at easier and higher quality e-/m-learning or blended tutoring. Security systems (SS) have been considered as an example of difficult-to-learn area. The topic of the presented investigation are contemporary education threats that will lead to big problems in the nearest future. It is shown that today the prevention of such threats is almost impossible without applications of intelligent technologies. Even more, the contemporary education system should possess some features of intelligent technologies or smart systems and advanced analytics elements. Original results are presented on how to build more effective classical applications by using novel puzzle methods. It is shown that all the demonstrated advantages may be successfully combined with other known methods from e-learning, m-learning or other education fields. Also it is shown how the considered applications enhance the quality of teaching, improve the emotional connection between the teacher and students and hence serve their sustainable learning and development. It has been discussed that qualitative forms of education may not require the usage of expensive hardware © 2013 IEEE.

关键词： intelligent systems

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Challenges of Using Edge Devices in IoT Computation Grids

Challenges of Using Edge Devices in IoT Computation Grids

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19th IEEE international conference on Parallel and Distributed Systems (ICPADS)

作者： Dey, Swarnava Mukherjee, Arijit Paul, Himadri Sekhar Pal, Arpan Tata Consultancy Serv Innovat Labs Kolkata India

ISBN: (纸本)9781479920815

Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.

关键词： cyber-physical ubiquitous systems analytics internet of things iot prediction mobile grid

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Prescription for Poland: Disruptive innovative e-Health ecosystem for regenerative medicine in Poland

Prescription for Poland: Disruptive innovative e-Health ecos...

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2013 IEEE 7th international conference on intelligent data Acquisition and Advanced computing Systems, IDAACS 2013

作者： Rucinski, Andrzej Miller, Miroslaw Girek, Marek University of New Hampshire Durham 03824 United States International Laboratory of High Magnetic Fields and Low Temperatures Gajowicka 95 53-421 Wroclaw Poland Data Techno Park Ltd. Kutnowska 1/3 53-135 Wroclaw Poland

ISBN: (纸本)9781479914265

Over the last several years, Poland has created a new generation of innovators in emerging industries, such as biomedicine and e-Health, in order to compete globally. The country is now ranked third in the world in production of quality ICT specialists and regenerative medicine initiatives. In order to promote Poland's innovations globally, next generation infrastructure improvements are required that must address state and international concerns in the health care industry. This is the goal of 'Prescription for Poland' program initiated by data Techno Park Ltd., Wroclaw company established for development e-Health infrastructure and technologies. e-Health technologies understood as new medical diagnostic tools and procedures and as advanced patient monitoring systems using sensors and data transmission can become Polish 'smart specialization' in new European financing perspective 2014-2020. Identification of smart specialization nationally and regionally as well as focus of support on them will be an ex ante condition for all European countries if Cohesion Fund will be spend for innovation and development programmes. © 2013 IEEE.

关键词： Diagnosis

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