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WSN API: Application Programming Interface for Wireless Sensor Networks

WSN API: Application Programming Interface for Wireless Sens...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Jari K. Juntunen Mauri Kuorilehto Mikko Kohvakka Ville A. Kaseva Marko Hannikainen Timo D. Hamalainen Institute of Digital and Computer Systems Tampere University of Technology Tampere Finland

In this paper, an application programming interface for wireless sensor networks (WSN API) is presented. The WSN API consists of a client-side API (gateway API) and a sensor-side API (node API). The WSN API conceals the complexities of WSN communication protocols and architectures, and provides a well-defined and easy-to-use way to collect data from sensors. Also, easy expandability for new sensor components and applications is provided. The WSN API is implemented practically in TUTWSN prototype platforms

关键词： Wireless sensor networks Transducers Intelligent sensors Sensor phenomena and characterization Batteries Wireless communication Application software Middleware distributed databases Java

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A novel design for Data Processing Framework of Park-level Power System with Data Mesh concept

A novel design for Data Processing Framework of Park-level P...

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IEEE International Conference on Energy Internet (ICEI)

作者： Jin Li Shixia Cai Lei Wang Mingyang Li Jiamao Li Hui Tu Alibaba Cloud Intelligence Beijing China Alibaba Cloud Intelligence Shenzhen China

Building a new-type power system with renewable energy is the key to achieving the target of carbon peak and carbon neutrality, which has been the consensus of the clean, low-carbon, and safe energy transition. The construction of a new-type power system at the industrial park level is the first demonstration of the coordinated optimization and intelligent control of power sources, grid, load, and energy storage to make overall planning to achieve diversified development. To sustain the ‘balance, security, diversity, and low cost’ construction goals of the new-type power system at the park level, the power grid dispatching and control system is transformed to the direction of intelligence and automation, which is reflected in the rapid emergence and construction of complex systems such as digital twin, simulation, reinforcement learning, model training and prediction, and intelligent decision-making of the EMS (Energy Manage System). This brings more complex data sharing, interaction, construction, and other technical requirements than ever before. At the same time, along with the simultaneous construction of new power systems such as micro-grids, virtual power plants, and distributed photovoltaic systems, it has exacerbated the problems of data divergence from business, low data utilization, and centralized architecture brought by the data lakes and data warehouses under the previous monolithic architecture, leading to the data model hard to deploy to meet the demand for efficient collection, interaction, and sharing of scattered and heterogeneous data under the new generation energy structure and greatly increases the work pressure of the data team, making the data team a bottleneck for business applications. Therefore, this paper proposes a novel design scheme of data processing framework with the data mesh concept, supports the federal computing and analysis engine by building a distributed metadata center, changes the previous centralized data modeling method t

关键词： Automation Buildings distributed databases Computer architecture Predictive models Metadata Data processing

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distributed Dimension Reduction for distributed Massive MIMO C-RAN with Finite Fronthaul Capacity

Distributed Dimension Reduction for Distributed Massive MIMO...

引用

Asilomar Conference on Signals, Systems & Computers

作者： Fred Wiffen Woon Hau Chin Angela Doufexi University of Bristol U.K. Bristol Research & Innovation Laboratory Toshiba Europe Limited U.K. Viavi Solutions Stevenage U.K.

The use of a large excess of service antennas brings a variety of performance benefits to distributed MIMO C-RAN, but the corresponding high fronthaul data loads can be problematic in practical systems with limited fronthaul capacity. In this work we propose the use of lossy dimension reduction, applied locally at each remote radio head (RRH), to reduce this fronthaul traffic. We first consider the uplink, and the case where each RRH applies a linear dimension reduction filter to its multiantenna received signal vector. It is shown that under a joint mutual information criteria, the optimal dimension reduction filters are given by a variant of the conditional Karhunen-Loeve transform, with a stationary point found using block co-ordinate ascent. These filters are then modified such that each RRH can calculate its own dimension reduction filter in a decentralised manner, using knowledge only of its own instantaneous channel and network slow fading coefficients. We then show that in TDD systems these dimension reduction filters can be re-used as part of a two-stage reduced dimension downlink precoding scheme. Analysis and numerical results demonstrate that the proposed approach can significantly reduce both uplink and downlink fronthaul traffic whilst incurring very little loss in MIMO performance.

关键词： Dimensionality reduction Precoding distributed databases Massive MIMO Information filters Downlink Throughput

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distributed Link Heterogeneity Exploitation for Attention-Weighted Robust Federated Learning in 6G Networks

Distributed Link Heterogeneity Exploitation for Attention-We...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Qiaomei Han Xianbin Wang Weiming Shen Department of Electrical and Computer Engineering Western University London Canada State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

The rapid evolution of wireless communications is paving the way for distributed computing, enabling pervasive intelligence in 6G networks through distributed machine learning particularly federated learning (FL). Despite the dramatically enhanced connectivity expected from 6G, imperfect or hetero-geneous communication links among distributed participating devices remain as a fundamental challenge for the performance improvement of FL. In overcoming the communication con-straint, existing solutions primarily focus on reducing communication overhead through techniques like FL model compression or sparsification. However, these approaches often ignore the impact of link heterogeneity among distributed devices on FL performance. To bridge this gap, we propose a heterogeneous link attention-weighted FL framework in 6G networks through the characterization of link heterogeneity and the design of an attention mechanism-driven model aggregation method. Specifically, leveraging prior knowledge about distributed communication links and their performance metrics such as latency, reliability, and data rate, the FL server calculates the joint performance dissimilarities among these links, thereby characterizing their heterogeneity relations as a binary probabilistic matrix. Sub-sequently, an attention mechanism is employed for FL model aggregation, where the generated attention weights represent the degree to which each link is influenced by other links. Therefore, the obtained global FL performance can be ensured. Experimental results further demonstrate that our proposed FL framework and model aggregation approach robustly handle FL under the impact of link heterogeneity and optimize the learning performance of FL.

关键词： 6G mobile communication Performance evaluation Wireless communication Federated learning Computational modeling distributed databases Probabilistic logic

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DIDA: distributed In-Network Defense Architecture Against Amplified Reflection DDoS Attacks

DIDA: Distributed In-Network Defense Architecture Against Am...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Xin Zhe Khooi Levente Csikor Dinil Mon Divakaran Min Suk Kang National University of Singapore Trustwave

ISBN: (数字)9781728156842

ISBN: (纸本)9781728156859

With each new DDoS attack potentially becoming a higher intensity attack than the previous ones, current ISP measures of over-provisioning or employing a scrubbing service are becoming ineffective and inefficient. We argue that we need an in-network solution (i.e., entirely in the data plane), to detect DDoS attacks, identify the corresponding traffic and mitigate promptly. In this paper, we propose the first distributed in-network defense architecture, DIDA, to cope with the sophisticated amplified reflection DDoS (AR-DDoS) attacks. We leverage programmable stateful data planes and efficient data structures and show that it is possible to keep track of per-user connections in an automated and distributed manner without overwhelming the network controller. Building on top of this data, DIDA can easily detect if unsolicited attack packets are sent towards a victim within an ISP network. Once an attack is detected, the routers at the network edge automatically block the malicious sources. We prototype DIDA in P4. Our preliminary experiments show that DIDA can detect and mitigate 99.8% of amplification attacks containing 7, 000 different sources while requiring less than 1% of the memory of current programmable switches.

关键词： Servers Computer crime distributed databases Memory management Protocols Materials handling IP networks

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Applying Twister to Scientific Applications

Applying Twister to Scientific Applications

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Bingjing Zhang Yang Ruan Tak-Lon Wu Judy Qiu Adam Hughes Geoffrey Fox Pervasive Technology Institute Indiana University Bloomington USA Indiana University Bloomington Bloomington IN US

Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, which not only supports the traditional MapReduce programming model but also extends it by allowing iterations, addresses these problems. This paper describes how Twister is applied to several kinds of scientific applications such as BLAST, MDS Interpolation and GTM Interpolation in a non-iterative style and to MDS without interpolation in an iterative style. The results show the applicability of Twister to data parallel and EM algorithms with small overhead and increased efficiency.

关键词： Interpolation distributed databases Programming Algorithm design and analysis Computer architecture Cloud computing

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Cross-Source ML Model Training

Cross-Source ML Model Training

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International Conference on Data Engineering

作者： Wenbo Sun Rihan Hai Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Machine learning (ML) often operates on data fragmented across silos through two paradigms: distributed or centralized. This study illuminates the underexplored signifi-cance of data integration (DI) metadata in both methodologies. Our contribution is threefold. First, we formalize the complex relationships of data sources with DI metadata. Second, we propose an approach that transforms DI metadata into matrix representations, and streamlines data transformation and linear algebra operations over source datasets. Third, we present an optimization method, effectively deciding between factorization versus materialization. By leveraging logic-based pruning rules and an ML-based cost estimator, our approach outperforms state-of-the-art baselines and makes the trade-off of factorization and materialization with up to 90.5% accuracy.

关键词： Training Soft sensors Optimization methods distributed databases Data integration Transforms Machine learning

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Design and Verification of Logic Block Circuit in an SOI-Based FPGA

Design and Verification of Logic Block Circuit in an SOI-Bas...

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2010 10th IEEE International Conference on Solid-State and Integrated Circuit Technology(第十届固态和集成电路技术国际会议 ICSICT-2010)

作者： Xiaowei Han Stanley L. Chen Lihua Wu Yan Zhao Yan Li Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 P.R. China

A novel logic block circuit consisting of two multimode logic cells is proposed for the design of a tile-based FPGA fabricated with a 0.5um SOI-CMOS logic process. Each logic cell contains two 3-LUTs. The proposed 3-LUT based logic cell circuit increases logic density by about 12％ compared with a traditional 4-LLJT implementation. The logic block can be used in two functional modes: LUT mode and distributed RAM mode, the latter of which can be configured in two modes: Single-Port RAM and DualPort RAM. Comparing with the published data on the CLB in Xilinx Spartan FPGA, the maximum LUT logic propagation delay has about 20％ improvement and the distributed RAM average access time has about 21％ improvement.

关键词： Random access memory Field programmable gate arrays Table lookup Routing distributed databases Logic gates

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Scalable Image Processing with Apache Spark and Convolutional Neural Networks

Scalable Image Processing with Apache Spark and Convolutiona...

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Deepika Sharma Ruchira Rawat Zaid Alsalami Narender Chinthamu Neeraj Varshney Manish Saraswat Department of Computer Science and IT Noida International University U. P Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Enterprise Architect MIT CTO Dallas Texas USA Department of Computer Engineering and Applications GLA University Mathura Lloyd Institute of Engineering & Technology Greater Noida

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

This research presents a new framework called Spark-CNN that incorporates Apache Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for images work processing. The results of experimental evaluation show that the proposed framework is indeed effective in handling large-scale image datasets, wherein substantial improvements have been noted regarding reduced processing time, scalability and accuracy. In Trial 1, Spark-CNN outperformed standalone –Spark (180 minutes), and all CNNs (240minutes) with an estimated processing season of 120 mins. Scale2 demonstrated the scalability of analyzing 2, sustaining constant processing times despite an increase in image dataset size from 1 million to 10 million. Further, in the third examination, Spark-CNN got an accuracy of 89%, surpassing standalone CNNs with a percentage score of 85%. Comparing the Trial 4 with Horovod-CNN, Spark-CNN showed a cutthroat processing time (120 minutes) and enhanced accuracy (89% vs., Trial 5 presented the influence of cluster setup on Spark-CNN revealing reduced execution times and better precision with growing numbers of executors beneficiaries per agent. These results establish Spark-CNN as a robust and effective solution to address scalable image processing challenges across different areas.

关键词： Deep learning Accuracy Image analysis Scalability distributed databases Cluster computing Big Data

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Blockchain Smart Contract for Scalable Data Sharing in IoT: A Case Study of Smart Agriculture

Blockchain Smart Contract for Scalable Data Sharing in IoT: ...

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Mohsin Ur Rahman Fabrizio Baiardi Laura Ricci University of Pisa Pisa Italy Linguistics University of Pisa Pisa Italy

ISBN: (数字)9781728184203

ISBN: (纸本)9781728184210

The emerging Smart Agriculture based on Internet of Things (IoT) is facing major challenges like data sharing, storage, and monitoring, primarily due to the distributed nature of IoT and massive scale. We performed a review of the literature and found that blockchain performance, scalability, cost, and throughput are the major challenges in adopting blockchain for smart agriculture. To overcome these challenges, this paper proposes a scalable and distributed data sharing system integrating access control for smart agriculture. We demonstrate our approach in a smart agriculture setting, which consists of four tiers that are: smart agriculture, smart contract, Interplanetary File System (IPFS) and agriculture stakeholders (remote users). This paper explains in detail the different components of our proposed architecture. Our approach uses anonymous identities to ensure users' privacy. Our approach is fully scalable because a large number of resource owners can use their data sharing smart contracts to create, update or delete data sharing policies. In addition, our approach does not require transaction fees when the smart contract receives a large number of policy evaluation requests. For the sake of simplicity, we publish and test a single data sharing smart contract. However, in practice, multiple smart contracts need to be deployed to allow each resource owner to securely share agriculture data with stakeholders. Finally, we evaluate the performance of our proposed system on the EOS blockchain to show that the resource consumption (in terms of computing power and network bandwidth) introduced by our framework are insignificant compared to its scalability, cost and security benefits.

关键词： Scalability Smart contracts distributed databases Blockchain Agriculture Internet of Things Stakeholders

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