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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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international conference on Parallel and Distributed Systems (ICPADS)

作者： Swarnava Dey Arijit Mukherjee Himadri Sekhar Paul Arpan Pal Tata Consultancy Services Innovation Labs Kolkata India

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： Servers smart phones Mobile communication Availability Sensors Internet

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smart Traffic Cloud: An Infrastructure for Traffic Applications

Smart Traffic Cloud: An Infrastructure for Traffic Applicati...

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IEEE 18th international conference on Parallel and Distributed Systems (ICPADS)

作者： Wang, Wen Qiang Zhang, Xiaoming Zhang, Jiangwei Lim, Hock Beng Nanyang Technol Univ Intelligent Syst Ctr Singapore 639798 Singapore

ISBN: (纸本)9781467345651;9780769549033

With rapid development of sensor technologies and wireless network infrastructure, research and development of traffic related applications, such as real time traffic map and on-demand travel route recommendation have attracted much more attentions than ever before. Both archived and real-time data involved in these applications could potentially be very big, depending on the number of deployed sensors. Emerging Cloud infrastructure can elastically handle such big data and conveniently providing nearly unlimited computing and storage resources to hosted applications, to carry out analysis not only for long-term planning and decision making, but also analytics for near real-time decision support. In this paper, we propose smart Traffic Cloud, a software infrastructure to enable traffic data acquisition, and manage, analyze and present the results in a flexible, scalable and secure manner using a Cloud platform. The proposed infrastructure handles distributed and parallel data management and analysis using ontology database and the popular Map-Reduce framework. We have proto-typed the infrastructure in a commercial Cloud platform and we developed a real-time traffic condition map using data collected from commuters' mobile phones.

关键词： Participatory sensing cloud computing data management software architecture data management Software architecture Cloud computing INFRASTRUCTURE CLOUDS Clouds Traffic Application hosting big data Long-term planning real-time data decision making support

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Robust Decision Engineering: Collaborative Big data and its Application to international Development/Aid

Robust Decision Engineering: <i>Collaborative Big Data and i...

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8th IEEE international conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Chan, Steve Rhodes, Wesley Atencio, Charles Kuo, Caroline Ranalli, Brent Miao, Anna Sala, Simone Serene, Stephen Helbling, Robert Rumbley, Sarah Clement, Marc Sokol, Lisa Gary, Loren MIT MIT Int Dev Initiat 77 Massachusetts Ave Cambridge MA 02139 USA Swansea Univ Inst Life Sci Swansea W Glam Wales IBM Fed Off CTO Software Grp Bethesda MD USA Harvard Kennedy Sch Ctr Publ Leadership Cambridge MA USA

ISBN: (纸本)9781467327404;9781467327404

Much of the research that goes into Big data, and specifically on Collaborative Big data, is focused upon questions, such as: . how to get more of it? (e.g., participatory mechanisms, social media, geo-coded data from personal electronic devices) and . how to handle it? (e.g., how to ingest, sort, store, and link up disparate data sets). A question that receives far less attention is that of Collaborative analysis of Big data;how can a multi-disciplinary layered analysis of Big data be used to support robust decisions, especially in a collaborative setting, and especially under time pressure? The robust Decision Engineering required can be achieved by employing an approach related to Network Science, that we call Relationship Science. In Relationship Science, our methodological framework, karassian netchain analysis (KNA), is utilized to ascertain islands of stability or positive influence dominating sets (PIDS), so that a form of annealed resiliency or latent stability is achieved, thereby mitigating against unintended consequences, elements of instability, and "perfect storm" crises lurking within the network.

关键词： Decision Engineering Science Robust Decision Engineering Complexity Ceiling Selection Bias Compressed Decision Cycles Gestaltian Closure Decision-Making Faster Decisions Better Decisions intelligent Decisions High Adaptation Cycles Perfect Storm Crises smart Power Times Velocity, Volume, and Vectors of Big data Collaborative Big data Bigger data Provenanced/Pedigreed data Big Compute Layered analytics Content analytics Entity Resolution Predictive analytics Complexity Theory Social Complexity Science High Performance computing Computational Intelligence Sparse data Sparse Networks Social Influence Network Participatory Revolution Network Science Relationship Science Relationship Manager 3D SD Big Insights Common Operating Picture Aegis System Unintended Consequences Condition-Creating Cyber-Physical Supply Chain Karassian Netchain Analysis Local Community Structures Network Shapes Memes Motifs Fifth Column Dualistic Actors Insider Threats Sentiment Analysis Flash Mobs Islands of Stability Sandpile Effect Cascading Failure Positive Influence Dominating Sets Civil Society Democratic Governance Science of Development Brittleness Annealed Resiliency Latent Stability

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AN intelligent SENSOR FRAMEWORK FOR THE POWER GRID

AN INTELLIGENT SENSOR FRAMEWORK FOR THE POWER GRID

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ASME 5th international conference on Energy Sustainability

作者： Akyol, Bora Haack, Jereme Tews, Cody Carpenter, Brandon Kulkarni, Anand Craig, Philip Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)9780791854686

The number of sensors connected to the electric power system is expected to grow by several orders of magnitude by 2020. However, the information networks which will transmit and analyze the resulting data are ill-equipped to handle the resulting volume with reliable real-time delivery. Without the ability to manage and use this data, deploying sensors such as phasor measurement units in the transmission system and smart meters in the distribution system will not result in the desired improvements in the power grid. The ability to exploit the massive data being generated by new sensors would allow for more efficient flow of power and increased survivability of the grid. Additionally, the power systems of today are not capable of managing two-way power flow to accommodate distributed generation capabilities due to concerns about system stability and lack of system flexibility. The research that we are performing creates a framework to add "intelligence" to the sensors and actuators being used today in the electric power system. Sensors that use our framework will be capable of sharing information through the various layers of the electric power system to enable two-way information flow to help facilitate integration of distributed resources. Several techniques are considered including use of peer-to-peer communication as well as distributed agents. Specifically, we will have software agents operating on systems with differing levels of computing power The agents will cooperate to bring computation closer to the data. The types of computation considered are control decisions, data analysis, and demand/response. When paired with distributed autonomous controllers, the sensors form the basis of an information system that supports deployment of both micro-grids and islanding. Our efforts in the area of developing the next generation information infrastructure for sensors in the power grid form the basis of a broader strategy that enables better integration of distributed

关键词： Distributed power generation

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smart Traffic Cloud: An Infrastructure for Traffic Applications

Smart Traffic Cloud: An Infrastructure for Traffic Applicati...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Wen Qiang Wang Xiaoming Zhang Jiangwei Zhang Hock Beng Lim Intelligent Systems Centre Nanyang Technological University Singapore

With rapid development of sensor technologies and wireless network infrastructure, research and development of traffic related applications, such as real time traffic map and on-demand travel route recommendation have attracted much more attentions than ever before. Both archived and real-time data involved in these applications could potentially be very big, depending on the number of deployed sensors. Emerging Cloud infrastructure can elastically handle such big data and conveniently providing nearly unlimited computing and storage resources to hosted applications, to carry out analysis not only for long-term planning and decision making, but also analytics for near real-time decision support. In this paper, we propose smart Traffic Cloud, a software infrastructure to enable traffic data acquisition, and manage, analyze and present the results in a flexible, scalable and secure manner using a Cloud platform. The proposed infrastructure handles distributed and parallel data management and analysis using ontology database and the popular Map-Reduce framework. We have prototyped the infrastructure in a commercial Cloud platform and we developed a real-time traffic condition map using data collected from commuters' mobile phones.

关键词： Roads Global Positioning System Real-time systems Vehicles Ontologies Distributed databases smart phones

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Energy-efficient task scheduling algorithms with human intelligence based task shuffling and task relocation

Energy-efficient task scheduling algorithms with human intel...

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2011 IEEE/ACM international conference on Green computing and Communications, GreenCom 2011

作者： Wang, Zhibo Zhang, Yan-Qing Department of Computer Science Georgia State University Atlanta GA 30302-3994 United States

ISBN: (纸本)9780769544663

Currently, more and more vendors such as Amazon, Google, IBM and Microsoft are dedicated to developing their cloud platforms for increasing large-scale data and more complex software systems. The cloud computing technique is rapidly changing the computing environment for various applications. However, a large number of cloud servers consume massive energy and produce huge pollution. The smart2020 analysis shows that cloud-based computing data center and the telecommunication network will generate emission about 7% and 5% each year in 2002 and 2020, respectively. This paper aims to develop a new green algorithm that can help multiple CPUs in the cloud network not only complete the tasks before a deadline, but also greatly reduce the energy consumption. Our new green algorithm with human intelligence can effectively make task assignments via partial task shuffling and adjust the cloud servers' speeds through smart task allocation under the time constraint. Sufficient simulation results indicate that the new green algorithm with intelligent strategies is effective compared with a traditional method and another new method. In the future, we will apply both ancient and modern human's intelligent strategies to improve green optimization algorithms. © 2011 IEEE.

关键词： Green computing

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Energy-Efficient Task Scheduling Algorithms with Human Intelligence Based Task Shuffling and Task Relocation

Energy-Efficient Task Scheduling Algorithms with Human Intel...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Zhibo Wang Yan-Qing Zhang Department of Computer Science Georgia State University Atlanta GA USA

关键词： Energy consumption Green products Optimization Scheduling algorithm Cloud computing Computers Servers

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intelligent Systems and Sustainable computing 1

引用

丛书名： smart Innovation, Systems and Technologies

1000年

作者： V. Sivakumar Reddy V. Kamakshi Prasad Jiacun Wang Naga Mallikarjuna Rao Dasari

ISBN: (数字)9789819947171

ISBN: (纸本)9789819947164;9789819947195

This book is a collection of best selected research papers presented at Second international conference on intelligent Systems and Sustainable computing (ICISSC 2022), held in School of Engineering, Malla Reddy University, Hyderabad, India, during December 16–17, 2022. The book covers recent research in intelligent systems, intelligent business systems, soft computing, swarm intelligence, artificial intelligence and neural networks, data mining and data warehousing, cloud computing, distributed computing, big data analytics, Internet of things (IoT), machine learning, speech processing, sustainable high-performance systems, VLSI and embedded systems, image and video processing and signal processing and communication.

关键词： Computational Intelligence Artificial Intelligence Cyber-physical systems, IoT Professional computing Statistics, general Signal, Image and Speech Processing

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Proceedings of international conference on intelligent computing, Information and Control Systems 1

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丛书名： Advances in intelligent Systems and computing

1000年

作者： A. Pasumpon Pandian Ram Palanisamy Klimis Ntalianis

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

This book is a collection of papers presented at the international conference on intelligent computing, Information and Control Systems (ICICCS 2020). It encompasses various research works that help to develop and advance the next-generation intelligent computing and control systems. The book integrates the computational intelligence and intelligent control systems to provide a powerful methodology for a wide range of data analytics issues in industries and societal applications. The book also presents the new algorithms and methodologies for promoting advances in common intelligent computing and control methodologies including evolutionary computation, artificial life, virtual infrastructures, fuzzy logic, artificial immune systems, neural networks and various neuro-hybrid methodologies. This book is pragmatic for researchers, academicians and students dealing with mathematically intransigent problems.

关键词： Computational Intelligence Artificial Intelligence Control, Robotics, Mechatronics

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Proceedings of international conference on Computational Intelligence and Emerging Power System 1

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丛书名： Algorithms for intelligent Systems

1000年

作者： Ramesh C. Bansal Akka Zemmari K. G. Sharma Jyoti Gajrani

ISBN: (数字)9789811641039

ISBN: (纸本)9789811641022;9789811641053

This book gathers outstanding research papers presented in the international conference on Computational Intelligence and Emerging Power System (ICCIPS 2021), held on March 9-10, 2021, at Engineering College Ajmer. ICCIPS 2021 is jointly organized by the Department of CSE and Department of EE, Engineering College Ajmer, Rajasthan, India. The topics covered in the book are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, natural language processing, renewable energy, signal processing, optimization methods for power system, smart grid, micro-grid, energy management, power system, monitoring system, load management, and distributed generation.

关键词： Computational Intelligence Artificial Intelligence Energy Systems Big data

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