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Requirements for implementing gossip based schemes for information dissemination in future power systems

Requirements for implementing gossip based schemes for infor...

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IEEE PES Innovative Smart grid Technologies conference Europe (ISGT Europe)

作者： A. Krkoleva V. Borozan A. Dimeas N. Hatziargyriou Faculty of Electrical Engineering and Information Technologies Ss Cyril and Methodius University Skopje Macedonia National Technical University of Athens Athens Greece

The future power systems will develop in conjunction with the ICT technologies and infrastructure, enabling implementation of novel technologies and concepts, where information sharing and making decisions on local level will play a key role in coordination actions among different actors. The general objective of the paper is to introduce a class of distributed algorithms which can be implemented in the future power systems by making use of the innovative ICT solutions and to investigate the characteristics and requirements of the ICT infrastructure that will enable actual implementation of these algorithms.

关键词： Peer to peer computing Logic gates Network topology Sensors Topology Smart grids

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Delay Optimization of Power Internet of Things based on Edge-cloud Collaboration

Delay Optimization of Power Internet of Things based on Edge...

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international conference on Communication Technology (ICCT)

作者： Julaiti Abulizi Qingsheng Hu Wei Wang State Grid Electronic Power Research Institute Urumqi China School of Information Science and Engineering Southeast University Nanjing China Nanjing Jiruizhidian Technology Co. Ltd. Nanjing China

With the continuous development of the power Internet of Things and the emergence of multi service scenarios, real-time and high-speed process in power data services face more and more challenges. This paper presents an SDN based edge-cloud collaborative method for delay optimization. First, the delay optimization problem is formulated and transformed into a convex optimization problem further. Then a continuously variable coefficient is introduced and its optimal solution is found. Finally a valid task partition strategy that closet to the optimal solution can be found, and the system delay is minimized. Simulation results show that the reasonable loading of edge node tasks to the cloud can effectively reduce the total system delay of power business and alleviate network congestion. Compared with other conventional methods, this edge-cloud collaboration method not only has low system delay, but also makes full use of the control mechanism of SDN to coordinate distributed environment.

关键词： cloud computing Simulation Collaboration Optimization methods parallel processing Real-time systems Delays

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An Overview of Experimental Load Tracking, Billing and Energy Consumption Monitoring Device using IoT

An Overview of Experimental Load Tracking, Billing and Energ...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Linda Cecilia Tracy Maule K. Raj Thilak B. Gayathri Devi P. Anbarasu Avvaru Nohit Venkata Seetha Sai Balaji N. Franklin Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore India Goldwin Industries Coimbatore India

In an electricity network, a smart grid's aim is to provide electronic power conditioning and production control and twitches in energy usage. These issues have been severely recognized in developing countries such as India in which there exists a large monotonous gap between required and available energy bringing the need for load-shedding. To promote electrical savings, smart grids have now been known to employ Demand Side Management (DSM). Future DSM systems should be designed such that it exploit's technologies such as Web of Things (WOT) and utility computing. This research study provides a planning structure, discovery and integration of a WOT and utility-based Electricity Monitoring System (EMS), with the ability to generate a fill profile of the end user. The designed EMS is easily installed at the premises of the consumer where it performs tasks like monitoring the consumption of the end user and uploads the sensed data to Think-Speak cloud over Information Transfer Protocol.

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Comparing the Supersonic cloud computing Model to Enhance the Networking and Security in Traditional Data Centers

Comparing the Supersonic Cloud Computing Model to Enhance th...

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Disruptive Technologies (ICDT), international conference on

作者： Ranadeep Reddy Palle Vinay Mallikarjunaradhya Haritha Yennapusa K. Suganyadevi Nidhi Gupta Department of Computer Science University of Houston Austin Texas USA Legal Tech Platform Thomson Reuters Toronto Canada Department of Computer Science University of central Missouri Austin Texas USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India

The Comparing the supersonic cloud computing model to enhance the networking and security in Traditional Data Centers study aimed to analyse the differences between traditional data centers and cloud computing models. The researchers' findings demonstrate that the supersonic cloud model consists of a number of services and functions that can improve network and security performance across all levels of the data center. Furthermore, the supersonic model offers more options for load balancing through routing protocols and more secure network access. In addition, the supersonic model allows for more flexibility in the resources used, such as virtual servers and storage systems. Lastly, the supersonic model allows for greater scalability through the use of distributed simultaneous networks. These benefits pave the way to the eventual goal of improving existing data center operation and security. The objective of this study was to assess the potential of supersonic cloud computing model (SCC) to improve the networking and security in traditional data centers. The findings indicated that SCC improved the network efficiency by 10.4%, reduced network latency by 11 %, and improved packet delivery rates by 8.6%. The security was also improved, with overall firewall rules increased by 12 %, and 5% fewer security violations. Overall, SCC showed promising performance in improving the networking and security of traditional data centers.

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grid resource allocation based on particle swarm optimization

Grid resource allocation based on particle swarm optimizatio...

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Workshop on Power Electronics and Intelligent Transportation System, PEITS

作者： Zhijie Li Xiangdong Liu Xiaodong Duan Jian Yun School of Computer Science and Engineering Dalian Nationalities of University Dalian Liaoning China

Resource allocation in grid environment is a complex undertaking due to the heterogeneity and dynamic nature aroused by wide area sharing. Most of the existing studies for this problem have been found to be performance deficiency or slow convergence. To address the heterogeneous and computationally intractable problem of resource allocation optimization in grid, this paper presents an allocation algorithm for parallel tasks based on particle swarm optimization. The heterogeneity of grid user is tackled by introducing a universal utility function. And that computational intractability is solved using iterative searching of particle swarm. Experimental results show that the proposed algorithm is convergent and performs better than genetic algorithm.

关键词： Resource management Particle swarm optimization Genetic algorithms grid computing Intelligent transportation systems Convergence Iterative algorithms Costs Computational modeling Heuristic algorithms

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A pruning based approach for scalable entity coreference

A pruning based approach for scalable entity coreference

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25th international Florida Artificial Intelligence Research Society conference, FLAIRS-25

作者： Song, Dezhao Heflin, Jeff Department of Computer Science and Engineering Lehigh University 19 Memorial Drive West Bethlehem PA 18015 United States

ISBN: (纸本)9781577355588

Entity coreference is the process to decide which identifiers (e.g., person names, locations, ontology instances, etc.) refer to the same real world entity. In the Semantic Web, entity coreference can be used to detect equivalence relationships between heterogeneous Semantic Web datasets to explicitly link coreferent ontology instances via the owl:sameAs property. Due to the large scale of Semantic Web data today, we propose two pruning techniques for scalably detecting owl:sameAs links between ontology instances by comparing the similarity of their context graphs. First, a sampling based technique is designed to estimate the potential contribution of each RDF node in the context graph and prune insignificant context. Furthermore, a utility function is defined to reduce the cost of performing such estimations. We evaluate our pruning techniques on three Semantic Web instance categories. We show that the pruning techniques enable the entity coreference system to run 10 to 35 times faster than without them while still maintaining comparably good F1-scores. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Ontology

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Trusted D2D Offloading with Energy Optimization for Workflow Applications in Mobile Edge computing

Trusted D2D Offloading with Energy Optimization for Workflow...

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cloud computing, Big Data Applications and Software engineering (CBASE), international conference on

作者： Jiabao Fan Cheng Qian Zheng Gong Haoyu Luo School of Computer Science South China Normal University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

Device-to-Device (D2D) offloading is a promising complement to the Device-Edge-cloud hierarchical offloading paradigm, which enables end devices to offload their computational tasks to nearby devices with idle resources. However, since the end devices are owned by individuals, people may not be willing to share resources with devices they do not trust. Excessive battery consumption can also dampen their enthusiasm for participation. Unfortunately, existing works on D2D offloading rarely jointly consider trust relationships and energy consumption, especially for the offloading of workflow applications where interdependent tasks are assigned to different individual devices. In this paper, we propose a novel D2D collaborative offloading approach for workflow applications in mobile edge computing environment, aiming to minimize the energy consumption of workflow execution while meeting the on-time response requirement of workflow requests and trust constraint of end devices. Specifically, we first partition the workflow into branches to meet the performance of single device and reduce data transfer among different devices. To achieve effective matching between the workflow branches and end devices, we introduce the concept of multi-hop trust, which helps to build a larger collaboration network with more candidate collaborators. Subsequently, we propose a distributed stable marriage matching algorithm to make the offloading decision based on the real-time information of the devices in the collaboration network, including their idle time, computing and communication capacities. We evaluate the proposed approach by conducting comparative and ablation experiments, and the simulation results demonstrate that our approach achieves the lowest energy consumption while ensuring reasonable response time of workflow requests.

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Factors influencing cloud technology adoption in Australian organisations

Factors influencing cloud technology adoption in Australian ...

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international conference on Information Technology (INCIT)

作者： L N Prasanna Balaaooriya Santoso Wibowo Marilyn Wells Central Queensland University Rockhampton QLD AU School of Engineering & Technology Central Queensland University Melbourne Australia

ISBN: (纸本)9781538614327

The purpose of this paper is to investigate the relevant factors including security, perceived usefulness, and perceived ease of use affect employee's behavioral and adoption intentions of cloud technology in organisations. This study was carried out using the quantitative research methodology based on a survey distributed to information technology (IT) professionals in Australia. The survey questionnaire was developed based 6 hypotheses and 13 indicator variables. Around 220 valid responses were received and analyzed by adopting a two-tier approach consisting of (1) confirmatory factor analysis to confirm the reliability and validity of the latent variables, and (2) analysis of the structural model to confirm or reject the hypothesis. The finding of this study indicates that the hypotheses that have been developed are valid and can be adopted in an organisation. This study has found that cyber attacks and cloud reliability factors are insignificant and have no affects in the cloud adoption. Furthermore, this study suggests that approximately 37 % of other factors which are not covered in this study need to be considered during its adoption.

关键词： cloud computing Security Information technology Data privacy Australia Atmospheric measurements Particle measurements

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power grids: Millisecond Level Precise distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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A double-stage hierarchical hybrid PSO-ANN model for short-term wind power prediction

A double-stage hierarchical hybrid PSO-ANN model for short-t...

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IEEE international conference on cloud computing and Big Data Analysis (ICCCBDA)

作者： Abinet Tesfaye Eseye Jianhua Zhang Dehua Zheng Han Li Gan Jingfu School of Electrical and Electronic Engineering Goldwind Science and Etechwin Electric Co. Ltd Beijing China Microgrid Platform R&D Department Goldwind Science and Etechwin Electric Co. Ltd. Beijing China School of Electrical and Electronic Engineering North China Electric Power University Beijing China State Grid Energy Conservation Service Tanshan Electric Power Utility China State Grid Co. Ltd. Beijing China

ISBN: (纸本)9781509045006

Power output of wind generators is always associated with some uncertainties due to wind speed and other weather parameters alteration, and precise short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical particle swarm optimization trained artificial neural network (double-stage hybrid PSO-ANN) model for short-term wind power prediction of a microgrid wind farm in Beijing, China. The model has two hierarchical stages. The first PSO-ANN stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day's wind speed by the first stage is applied to the second stage to forecast next day's wind power. The proposed approach has attained significant prediction accuracy improvements. The performance of the proposed model is compared with other two prediction approaches and showed best accuracy improvement than both methods.

关键词： Predictive models Generators Microgrids Computational modeling Atmospheric measurements Particle measurements Springs

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