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3rd ieee international conference on Fog and Edge computing (ICFEC)

作者： Wiener, Patrick Zehnder, Philipp Riemer, Dominik FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9781728123653

Newly arising IoT-driven use cases often require low-latency analytics to derive time-sensitive actions, where a centralized cloud approach is not applicable. An emerging computing paradigm, referred to as fog computing, shifts the focus away from the central cloud by offloading specific computational parts of analytical stream processing pipelines (SPP) towards the edge of the network, thus leveraging existing resources close to where data is generated. However, in scenarios of mobile edge nodes, the inherent context changes need to be incorporated in the underlying fog cluster management, thus accounting for the dynamics by relocating certain processing elements of these SPP. This paper presents our initial work on a conceptual architecture for context-aware and dynamic management of SPP in the fog. We provide preliminary results, showing the general feasibility of relocating processing elements according to changes in the geolocation.

关键词： fog computing distributed systems context-aware computing stream processing

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Specification-based distributed Detection of Rank-related Attacks in RPL-based Resource-Constrained Real-Time Wireless Networks 3

Specification-based Distributed Detection of Rank-related At...

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3rd ieee conference on Industrial Cyberphysical systems, ICPS 2020

作者： Althubaity, Areej Gong, Tao Raymond, Kim-Kwang Nixon, Mark Ammar, Reda Han, Song University of Connecticut United States University of Texas at San Antonio United States Emerson Automation Solutions United States

ISBN: (纸本)9781728163895

RPL, the IPv6 Routing Protocol for Low-Power and Lossy Networks (LLNs), is widely used in resource-constrained wireless sensor networks. RPL is, however, vulnerable to internal routing attacks where compromised nodes may seek to exploit the vulnerability of the Rank value that represents the node's position relative to the root in the RPL graph. The impact of such attacks can be devastating, especially for real-time wireless networks where network-wide time synchronization needs to be maintained during system operation. In this paper, we present a distributed detection system FORCE to protect the RPL topology from Rank-related attacks. In FORCE, each node locally analyzes the received control messages from its neighbors and generates alerts upon the discovery of an intrusion. We evaluate the performance of proposed IDS using extensive simulations. The findings demonstrate that it can effectively detect Rank-related attacks with a high detection rate, yet incurring only moderate computation and communication overheads. © 2020 ieee.

关键词： Intrusion detection

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Intelligent Recommendation System for E-Learning using Membership Optimized Fuzzy Logic Classifier 3

Intelligent Recommendation System for E-Learning using Membe...

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3rd ieee-Pune-Section international conference (PuneCon)

作者： Das, Anupam Al Akour, Mohammad Cotton Univ Gauhati Assam India Al Sharqyat Univ Ibra Oman

ISBN: (纸本)9781728196008

Technology-enhanced learning provides various communication and information technologies for learning and teaching. The teachers also feel comfortable with the Open Educational Resources repositories that learn the existing learning materials when a new course is developed. Yet, this exists as a non-trivial problem because the learning environments should be flexible for the students to learn on the basis of their situations and characteristics. Hence, the main aim of this paper is to provide personalized dynamic and continuous recommendations for online learning systems. This paper plans to implement the novel recommendation system for online learning using intelligent techniques. The main steps of the proposed model are (a) Data collection, (b) Feature extraction, and (c) classification. Initially, the data are collected locally from the Ekhool learning application. Then the feature extraction techniques, such as t-distributed Stochastic Neighbour Embedding (t-SNE) and Principle Component Analysis (PCA) are used for selecting the most relevant features. Further, the classifier termed as Fuzzy Logic Classifier is adopted as the recommendation system, where the improvement is made in the membership limits by optimizing it with the Rider Optimization Algorithm (ROA). The superiority of the proposed method is proved by the performance analysis in terms of various performance measures.

关键词： E-learning Recommendation system t-distributed Stochastic Neighbour Embedding Principle Component Analysis Fuzzy Logic Classifier Rider Optimization Algorithm Membership Optimization

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Special Issue on Edge-Cloud Interplay Based on SDN and NFV for Next-Generation IoT Applications

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ieee INTERNET OF THINGS JOURNAL 2020年第7期7卷 5690-5694页

作者： Garg, Sahil Guo, Song Piuri, Vincenzo Choo, Kim-Kwang Raymond Raman, Balasubramanian Ecole Technol Super Montreal PQ H3C 1K3 Canada Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Univ Milan Comp Engn I-20133 Milan Italy Univ Texas San Antonio Cloud Technol Endowed Professorship San Antonio TX 78249 USA Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

With significant and continuing advances in information and communication technologies, the Internet of Things (IoT) will play an increasingly important role in domains, such as healthcare, transportation, finance, and energy. In an IoT system, billions of devices (e.g., sensors, wearables, and smart appliances) are connected to the global network infrastructure, and one associated phenomenon is the generation of a large volume of data. Apart from data volume, the velocity, variety, and veracity of these data will pose a significant burden on conventional networking infrastructures. However, as sensor and fifth-generation (5G) cellular technologies advance, so will the pervasiveness of IoT deployment. Parallel to this trend, cloud computing has been integrated with IoT in order to address limitations in existing IoT networks (e.g., storage and computing resources), and examples include Google cloud dataflow and Amazon IoT. However, cloud-centric IoT solutions may not be suited for delay-sensitive and computationally intensive applications, for example, due to resource availability, end-to-end latency, bandwidth, etc. Increasingly, large-scale IoT deployments demand high connectivity, interoperability, and orchestration which are necessary for minimizing latency and maximizing throughput. This highlights the importance of a distributed computing platform that can support the interactions between IoT and cloud computing systems.

关键词： Special issues and sections Cloud computing Edge computing Intelligent sensors Wearable sensors Wearable computers

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GRNN++: A Parallel and distributed Version of GRNN Under Apache Spark for Big Data Regression 3rd

GRNN++: A Parallel and Distributed Version of GRNN Under Apa...

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3rd international conference on Data Management, Analytics and Innovation, ICDMAI 2019

作者： Kamaruddin, Sk. Ravi, Vadlamani Centre of Excellence in Analytics Institute for Development and Research in Banking Technology Castle Hills Road No. 1 Masab Tank Hyderabad500057 India SCIS University of Hyderabad Hyderabad500046 India

ISBN: (纸本)9789813299481

Among the neural network architectures for prediction, multi-layer perceptron (MLP), radial basis function (RBF), wavelet neural network (WNN), general regression neural network (GRNN), and group method of data handling (GMDH) are popular. Out of these architectures, GRNN is preferable because it involves single-pass learning and produces reasonably good results. Although GRNN involves single-pass learning, it cannot handle big datasets because a pattern layer is required to store all the cluster centers after clustering all the samples. Therefore, this paper proposes a hybrid architecture, GRNN++, which makes GRNN scalable for big data by invoking a parallel distributed version of K-means++, namely, K-means||, in the pattern layer of GRNN. The whole architecture is implemented in the distributed parallel computational architecture of Apache Spark with HDFS. The performance of the GRNN++ was measured on gas sensor dataset which has 613 MB of data under a ten-fold cross-validation setup. The proposed GRNN++ produces very low mean squared error (MSE). It is worthwhile to mention that the primary motivation of this article is to present a distributed and parallel version of the traditional GRNN. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multilayer neural networks

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Machine Learning Prediction of TBI from Mobility, Gait and Balance Patterns

Machine Learning Prediction of TBI from Mobility, Gait and B...

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ieee international conference on Connected Health: Applications, systems and Engineering Technologies (CHASE)

作者： Bhoomi Patel Srinarayan Srikanthan Florina Asanit Emmanuel Agu Worcester Polytechnic Institute Worcester

ISBN: (纸本)9781665439664

Traumatic Brain Injury (TBI) 1 , caused by a violent blow to the head, has become a major cause of death and disability with 56,800 deaths reported in 2014. Early detection of TBI can reduce emergency visits and save lives. Prior methods were clinical tests such as Computerized Tomography (CT) that are expensive and required hospital visits, or mobile health applications that required active involvement that is burdensome. sensor-rich smartphones have emerged as viable platforms for continuous health monitoring. In this paper, we explore using smartphone sensors for detecting TBI at the onset of injury. Mobility patterns, gait and balance are three human attributes that distinguish subjects with TBI from non-TBI controls. Our work is the first to use machine learning approaches to detect TBI using mobility, gait and balance features extracted from passively gathered smartphone sensor data. Three machine learning pipelines were investigated and compared, namely; (1a) computing hand-crafted features on raw sensor data; (1b) computing hand-crafted features on pre-processed sensor data; (2) using an auto-encoder based approach to encode raw mobility patterns that were then combined with handcrafted gait and balance features. In total, we explored 6 location features, 9 gait and 4 balance statistical features extracted from the smartphone's location and accelerometer sensor data using different segmentation methods. These features were then normalized and classified using machine learning algorithms. Of the three approaches, the best results were achieved using classification of handcrafted gait, balance and mobility features using tree-based classifiers. However, encoding mobility features using an autoencoder that were classified using the Random Forest classifier achieved the best results in terms of early detection. Hand-crafted feature extraction on raw-sensor data gave the best results on the 3rd day after injury using a 24-hour window size with XGBoost achieving S

关键词： Sensitivity Computed tomography Pipelines sensor phenomena and characterization Feature extraction Data mining Smart phones

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On The Challenges Of Executing Bioinformatic Tools Using a distributed Cloud Platform 3

On The Challenges Of Executing Bioinformatic Tools Using a D...

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3rd ieee international conference on Cloud and Fog computing Technologies and Applications (ieee Cloud Summit)

作者： Thomas, Jamie Henson, Bailey Kirby, Austin Armstrong, Matthew Galloway, Michael Western Kentucky Univ Sch Engn & Appl Sci Bowling Green KY 42014 USA

ISBN: (纸本)9781728131016

The BioCloud project is an expansive and complex project involving platforms and tools from areas of file storage, containerization, and server hosting. Not the least complex of all of these systems are those used in the handing of the many bioinformatic scripts and tools needed to ensure BioCloud is a service worth the effort. This paper will dive into the design and methods that allow BioCloud to support a large number of tools, to simplify the process of adding additional tools and editing existing scripts, and to be flexible enough to support the bioinformatic script no matter its needs.

关键词： Bioinformatics Cloud computing

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ORCH: distributed Orchestration Framework using Mobile Edge Devices 3

ORCH: Distributed Orchestration Framework using Mobile Edge ...

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3rd ieee international conference on Fog and Edge computing (ICFEC)

作者： Tocze, Klervie Nadjm-Tehrani, Simin Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden

ISBN: (纸本)9781728123653

In the emerging edge computing architecture, several types of devices have computational resources available. In order to make efficient use of those resources, deciding on which device a task should execute is of great importance. Existing works on task placement in edge computing focus on a resource supply side consisting of stationary devices only. In this paper, we consider the addition of mobile edge devices. We explore how mobile and stationary edge devices can augment the original task placement problem with a second placement problem: the placement of the mobile edge devices. We propose the ORCH framework in order to solve the joint problem in a distributed manner and evaluate it in the context of a spatially-changing load. Our implementation of the combined task and edge placement algorithms shows a normalized 83% delay-sensitive task completion rate compared to a perfect edge placement strategy.

关键词： Fog/edge computing resource management edge mobility task placement edge placement

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A platform application for exploiting the Digital Reference Ontology 3

A platform application for exploiting the Digital Reference ...

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3rd ieee conference on Industrial Cyberphysical systems, ICPS 2020

作者： Sipsas, Konstantinos Zalonis, Andreas Broechler, Raimund Baumgaertel, Hartwig Ehm, Hans Intrasoft International Sa Reasearch and Innovation Development Luxembourg Luxembourg Logistics and Scm University of Applied Sciences Ulm Ulm Germany Infineon Technologies Ag Supply Chain Innovations Munich Germany

ISBN: (纸本)9781728163895

Horizontal and vertical integration rely on flexible interaction, i.e. communication and cooperation, of heterogeneous systems and subsystems on several abstraction layers. A large ontology (the Digital Reference) that describes semiconductor supply chains and supply chains containing semiconductors is needed so as to leverage the highly distributed and decentralized data sources. The Digital Reference Platform Application (DRPA) is a tool designed to explore the power of the Digital Reference Ontology, offering features for ontology exploration and data creation along with Arrowhead Framework integration. With this, the DRPA offers interoperability between arbitrary software applications and industrial cyber-physical systems-integrated into Arrowhead Local Clouds. This approach applied for a voting wristband prototype shows the potential of the DPRA. The final target is to extend the potential of DRPA beyond Ontology exploration. © 2020 ieee.

关键词： Ontology

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3-PHASE LEADER ELECTION ALGORITHM FOR distributed systems 3

3-PHASE LEADER ELECTION ALGORITHM FOR DISTRIBUTED SYSTEMS

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3rd international conference on computing Methodologies and Communication (ICCMC)

作者： Chaparala, Pushya Atmakuri, Aparna Rajesh SivaSankarRao, S. Guru Nank Inst Comp Sci & Engn Hyderabad India Visveswaraya Technol Univ Comp Sci & Engn Belgaum Karnataka India

ISBN: (纸本)9781538678084

Election plays a foremost part in distributed systems. The reason behind the Election process in distributed system is to optimize in terms of load and improving the efficiency of the system there by improving the process of group communication. Choosing a node to be a leader in a distributed environment is intricate. Over years, there are so much of exploitations in the existing techniques such as ring and bully but these algorithms could not able to provide an appropriate solution in terms of message complexity. In this paper, we proposed a novel method known as "3-Phase Leader Election Algorithm" that consists of three phases. Firstly, it identifies and filters the nodes for election. Secondly, the filtered nodes are been validated for determination of prime node via which the group communication takes place. Finally, the Prime node will be identified and accepted in the acceptance phase. Our approach is based on understanding the complexity involved behind a message and improving the efficiency of the system via following performance metrics such as Time and Communication complexity.

关键词： Ring Bully 3-PLE distributed systems

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