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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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An Architecture model for Smart Farming 15

An Architecture model for Smart Farming

引用

15th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Triantafyllou, Anna Tsouros, Dimosthenis C. Sarigiannidis, Panagiotis Bibi, Stamatia Univ Western Macedonia Dep Informat & Telecommun Engn Kozani Greece

ISBN: (纸本)9781728105703

Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the four layers of the architecture model that are the sensor Layer, the Network Layer, the Service Layer and the Application Layer. A discussion is also conducted upon the challenges that smart farming monitoring systems face.

关键词： Wireless sensor Networks Internet of Things Precision Agriculture Smart Farming Communication technologies Cloud computing

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Towards Extensibility-Aware Scheduling of Industrial Applications on Fog Nodes 13

Towards Extensibility-Aware Scheduling of Industrial Applica...

引用

4th ieee international conference on Edge computing (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Barzegaran, Mohammadreza Karagiannis, Vasileios Avasalcai, Cosmin Pop, Paul Schulte, Stefan Dustdar, Schahram Tech Univ Denmark DTU Compute Lyngby Denmark TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781728182544

Fog computing has been identified as an enabler for many modern technologies like connected vehicles and the Industrial Internet of Things (IIoT). Such technologies are characterized by the integration of applications with different levels of criticality on shared platforms, which are referred to as mixed-criticality systems. Mixed-criticality systems typically use static scheduling for critical tasks;however, static scheduling is not suitable for scenarios where fog nodes run dynamic non-critical applications that implement, e.g., maintenance checks and data analytics. To address this challenge, in this paper, we differentiate between critical tasks that are statically allocated (called "native") and dynamic non-critical tasks that may migrate across fog nodes (called "temporary"). We propose a static scheduling approach that maximizes the number of temporary tasks that can be added at runtime, without negatively impacting the already scheduled native tasks. This approach enables fog nodes to become more suitable for IIoT environments by configuring them with extensible schedules for the native tasks. To evaluate our approach, we perform experiments considering several test cases, which show that given a number of native tasks, the generated extensible schedules enable the fog nodes to run a larger number of temporary tasks at the same time. Furthermore, the extensible schedules exhibit 7.8% less missed deadlines (on average), compared to the non-extensible schedules.

关键词： Fog computing mixed-criticality systems scheduling extensibility optimization

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Wireless multimedia sensor networks and data fusion: An overview

Wireless multimedia sensor networks and data fusion: An over...

引用

2020 ieee international conference on computing, Power and Communication Technologies, GUCON 2020

作者： Singh, Brahmjit Jindal, Poonam National Institute of Technology Kurukshetra Electronics and Communication Engg. Deptt. Kurukshetra136119 India

ISBN: (纸本)9781728150703

In the era of big data, Wireless Multimedia sensor Networks (WMSNs) have emerged as an enabling and effective technology for multimedia data sensing in physical environment. Wireless communication being resource constrained finds it difficult to support the volume and the type of big data traffic for acquisition and distribution. It poses complex technical challenges particularly in terms of computational power, transmission bandwidth, and energy consumption. In this paper, we have presented an account of important features and requirements of WMSNs for monitoring and analyzing the data recorded through multimodal sensors. Recent fusion strategies including those based on artificial intelligence for achieving improved accuracy in extracting the intelligence from big data are analyzed. Their limitations are identified and probable solutions in context with the real-field deployment of the emerging ubiquitous computing systems are outlined. © 2020 ieee.

关键词： Big data

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Quality of Service in Wireless sensor Networks using Cellular Learning Automata 3

Quality of Service in Wireless Sensor Networks using Cellula...

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3rd international conference on computing, Electronics and Communications Engineering (iCCECE)

作者： Alansari, Zainab Anuar, Nor Badrul Kamsin, Amirrudin Belgaum, Mohammad Riyaz Soomro, Safeeullah Majan Univ Coll Muscat Oman Univ Malaya Kuala Lumpur Malaysia Univ Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9781728163307

Wireless sensor Networks (WSNs) have different Quality of Service (QoS) parameters from those of traditional networks. Several considerations utilized for evaluating QoS include appropriate number of active nodes, network lifetime, network coverage, and resource utilization. One of the features of Cellular Learning Automata (CLA), besides its simple learning structure, is learning in distributed and multi-hop environments with limited communications and incomplete information. CLA benefit show how different problems in WSNs can be overcome. In this paper, the underlying issues of WSNs are discussed, and in order to improve the QoS parameters, efficient solutions have been proposed using CLA. The WSN 's environmental coverage issue is also addressed by turning off redundant nodes and maintaining adequate nodes to conserve resources and enhance network life. In this research, the issue of clustering of WSNs is addressed and the WSNs are clustered by using CLA to efficiently distribute energy to the network and maximize network life. All provided methods are simulated by J-Sim tools showing the overall reduce in WSN energy consumption and also for each node alone. Moreover, we demonstrate the reduce in data communication overhead and maintaining the overall network coverage. Simulation experiments indicate higher performance of the proposed methods than other associated approaches.

关键词： Wireless sensor Networks Cellular Learning Automata Network Coverage Quality of Service

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SparsePipe: Parallel Deep Learning for 3D Point Clouds 27

SparsePipe: Parallel Deep Learning for 3D Point Clouds

引用

27th ieee international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Zhai, Keke He, Pan Banerjee, Tania Rangarajan, Anand Ranka, Sanjay Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9781665422925

We propose SparsePipe, an efficient and asynchronous parallelism approach for handling 3D point clouds with multi-GPU training. SparsePipe is built to support 3D sparse data such as point clouds. It achieves this by adopting generalized convolutions with sparse tensor representation to build expressive high-dimensional convolutional neural networks. Compared to dense solutions, the new models can efficiently process irregular point clouds without densely sliding over the entire space, significantly reducing the memory requirements and allowing higher resolutions of the underlying 3D volumes for better performance. SparsePipe exploits intra-batch parallelism that partitions input data into multiple processors and further improves the training throughput with inter-batch pipelining to overlap communication and computing. Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead. Using experimental results on an eight-GPU platform, we show that SparsePipe can parallelize effectively and obtain better performance on current point cloud benchmarks for both training and inference, compared to its dense solutions.

关键词： asynchronous distributed training model parallelism load balancing sparse DNN 3D point clouds

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A Configurable distributed Data Analytics Infrastructure for the Industrial Internet of Things 15

A Configurable Distributed Data Analytics Infrastructure for...

引用

15th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Kefalakis, Nikos Roukounaki, Aikaterini Soldatos, John Athens Informat Technol IoT Grp Maroussi Greece

ISBN: (纸本)9781728105703

Executing analytics functionalities over data from highly distributed data sources and data streams is at the very core of the vast majority of Industrial Internet of Things (IIoT) applications. State of the art streaming engines provide the means for high performance analytics over high velocity IIoT streams, yet they still need significant programming and customization efforts when deployed in heterogeneous industrial environments. This paper introduces a configurable engine for distributed data analytics for IIoT applications. The engine leverages the performance of state of the art data streaming middleware platforms, which it augments with a set of digital models for configuring DDA operations. As such the introduced engine reduces the effort needed to implement and deploy distributed data analytics in IIoT environments. The engine is available as open source software and has been validated in a various real-life IIoT applications in different environments.

关键词： distributed Data Analytics BigData Industrial Internet of Things Industry4.0

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A Novel Algorithm to Segregate Suspicious node in FzMAI 2

A Novel Algorithm to Segregate Suspicious node in FzMAI

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2nd ieee international conference on Advances in computing, Communication Control and Networking, ICACCCN 2020

作者： Singh, Neha Virmani, Deepali University School of Information Communication and Technology GGSIPU Delhi India BPIT Dept.of Computer Science and Engineering Delhi India

ISBN: (纸本)9781728183374

Intrusion prevention systems form a lacuna in wireless sensor networks(WSN). This paper widens the scope of fuzzified methodology to avert intrusion method (FzMAI). FzMAI defines an extra parameter for node classification other than normal and malicious: Suspicious. The paper proposes an algorithm for segregating a suspicious node into either a normal node or malignant node. The algorithm is inspired by Locard's Exchange Principle used in forensic science. The proposed algorithm is tested on a modified WSN-DS dataset and obtains an accuracy of 87%. © 2020 ieee.

关键词： sensor nodes

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Evolutionary Search for Energy-Efficient distributed Cooperative Spectrum Sensing

Evolutionary Search for Energy-Efficient Distributed Coopera...

引用

2020 international conference on computing, Networking and Communications, ICNC 2020

作者： Jiang, He Li, Lusi He, Haibo Liu, Lingjia University of Rhode Department of Electrical Computer and Biomedical Engineering Island KingstonRI02881 United States Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburVA24060 United States

ISBN: (纸本)9781728149059

This paper concerns the energy efficiencyoptimization for distributed cooperative spectrum sensing. In the considered distributed spectrum sensing system, each sensor measures the local test statistic for the target spectrum bands and these measurements will be combined together through a weighted consensus protocol. In this way, all the sensors are able to make contributions to the improvement of the spectrum sensing performance. However, the significance of a sensor's contribution depends on its local signal to noise ratio. From energy efficiency perspective, it is not reasonable to invest energy on the sensors that bring little benefits. In this paper, we formulate an energy efficiency optimization framework for distributed spectrum sensing. Our objective is to achieve the target spectrum sensing performance with as few sensors as possible. Accordingly, a genetic algorithm based approach and a particle swarm optimization based approach are proposed for this problem. © 2020 ieee.

关键词： Energy efficiency

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Review of Energy Harvesting in LoRa based Wireless sensor Network 2

Review of Energy Harvesting in LoRa based Wireless Sensor Ne...

引用

2nd ieee international conference on Advances in computing, Communication Control and Networking, ICACCCN 2020

作者： Philip, Merin Susan Singh, Poonam National Institute of Technology Electronics and Communication Department Rourkela Odisha769008 India

ISBN: (纸本)9781728183374

Real-Time monitoring systems such as environmental monitoring, smart farming, etc. uses a wireless sensor network(WSN) that consist of nodes and gateways. The sensor nodes in WSN are usually powered by batteries that have only a limited lifetime. These nodes are deployed in fields and expected to operate for a long time. Due to extensive usage, the battery will drain up soon resulting in energy scarcity. To overcome the challenge, many energy harvesting techniques are used. Several studies have shown that harvested energy is not sufficient to meet the energy requirements of the system in a WSN. LoRa, as low power, long range technology can be a solution to this problem which consumes less energy compared to other technologies. This paper deals with the studies which have done in the area of energy harvesting (EH) in LoRa based systems to minimize the mismatch between the energy harvested and consumed. © 2020 ieee.

关键词： Energy harvesting

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Ensuring the Blind Signature for the Electoral System in a distributed Environment

Ensuring the Blind Signature for the Electoral System in a D...

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2020 ieee international conference on Power, Intelligent computing and systems, ICPICS 2020

作者： Toapanta Toapanta, Segundo Moisés Huilcapi Subia, Darío Fernando Cepeda Aveiga, Milton Andrés Mafla Gallegos, Luis Enrique Universidad Politécnica Salesiana Ecuador Computer Science Department Guayas Guayaquil Ecuador Escuela Politécnica Nacional Faculty of Engineering Systems Quito Pichincha Ecuador

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198736

We analyze several alternatives of blind digital signatures, for an electronic electoral system in a distributed architecture environment, in view of the persistence of blind signature problems in electoral processes. The objective is to recognize and propose an alternative of a blind signature scheme to improve privacy, confidentiality for the voter and in this way reduce any type of vulnerability with respect to the information of the same. The deductive method is used to summarize the information of the scientific articles studied. It results in a blind signature prototype based on algorithms to mitigate the vulnerabilities of voter information. It is concluded that, in order to guarantee privacy and confidentiality, several blind signature schemes must be implemented together to obtain the benefits of each of them, thus reducing the risks to which the voter's information is exposed in this process. © 2020 ieee.

关键词： Authentication

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