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Deep Learning/PUF-based Item Identification for Supply Chain Management in a distributed Ledger Framework

Deep Learning/PUF-based Item Identification for Supply Chain...

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Blockchain computing and Applications (BCCA), international conference on

作者： Carmelo Felicetti Antonella Guzzo Giuseppe Manco Francesco Pasqua Ettore Ritacco Antonino Rullo Domenico Saccà Department of Informatics Modelling Electronics and Systems (DIMES) University of Calabria Rende Italy Institute for High Performance Computing and Networking National Research Council (ICAR-CNR) Rende Italy Open Knowledge Technologies (OKT) Relatech Group Rende Italy Computer Science Department University of Udine Udine Italy

Item identification is an important issue in modern supply chains where product items are traced by means of electronic tags that, despite the advantages they bring in terms of automation, are still subject to counterfeiting attacks such as tag modification, cloning, and re-application. To counter these threats (i) tags must be able to safely demonstrate their identity without giving attackers the ability to clone them or modify their content, and (ii) auxiliary identification techniques should be adopted in the case tags are removed from original items and reapplied to fake ones. Using Physically Unclonable Functions as tag “digital fingerprint” has proven to be a viable solution for tag authentication. On the other hand, artificial intelligence has proven its effectiveness in the field of object identification. In this paper we describe a tag architecture immune to cloning and modification attacks, and illustrate how this can be coupled with a product item in a supply chain scenario to confer a stable and durable identity for authentication, in order to identify the items and track them in a distributed ledger framework. We also propose a deep learning approach to perform anti-counterfeiting controls when tag reapplication attacks are in place.

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Chord Protocol: distributed Hash Table for Peer-to-Peer networking

Chord Protocol: Distributed Hash Table for Peer-to-Peer Netw...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Arpit Rawat Amit Gupta Richa Gupta Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Chord DHT (distributed Hash Table) is a robust solution to a basic problem encountered in peer-to-peer (P2P) applications: efficiently locating the node that stores specific data items. In this paper, we perform a comprehensive analysis of Chord's capabilities and contribute to the P2P landscape. In Chord DHT, we connect all the nodes such that they form a ring, and we explore its ability to map keys onto nodes effectively, facilitating data location within the network. Chord's simplicity helps in supporting a single operation—key mapping to a node—data management, which is quite straightforward, by associating keys with data items and storing them at corresponding nodes. Chord DHT also demonstrates remarkable adaptability to dynamic network conditions, where it seamlessly accommodates node joins and departures while remaining capable of answering queries amidst ongoing system changes. Through theoretical analysis, simulations, and experiments, we found that Chord's scalability emerges as a standout feature and is designed to make node state maintenance scaling and communication costs logarithmic with the network's size. Additionally, this paper showcases a practical implementation of Chord DHT in Java. For a better user experience, we made a sophisticated GUI, which was developed using Java AWT and Swing libraries. Our graphical interface enhances usability and interaction with the Chord DHT system. Further, we highlight its practical applicability in real-world scenarios.

关键词： Java Protocols Navigation Scalability User experience Libraries Peer-to-peer computing Maintenance Iterative methods Usability

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User-centric distributed Route Planning in Smart Cities based on Multi-objective Optimization 7

User-centric Distributed Route Planning in Smart Cities base...

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7th IEEE international conference on Smart computing (SMARTCOMP)

作者： Tiausas, Francis Talusan, Jose Paolo Ishimaki, Yu Yamana, Hayato Yamaguchi, Hirozumi Bhattacharjee, Shameek Dubey, Abhishek Yasumoto, Keiichi Das, Sajal K. NAIST Nara Japan Waseda Univ Tokyo Japan Osaka Univ Osaka Japan Western Michigan Univ Kalamazoo MI 49008 USA Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA Missouri Univ Sci & Technol Rolla MO 65409 USA

ISBN: (纸本)9781665412520

The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.

关键词： Smart cities distributed route planning Multi-objective Optimization NSGA-II Edge computing

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs

Efficient Large Scale Reverse-time Migration Imaging Computa...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Suhui Wangzhang Ruizhang Yang Rong Gu Bo Li Dingjin Liu Zhaokang Wang State Key Laboratory for Novel Software Technology Nanjing University China Sinopec Geophysical Research Institute China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee.

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Extending TWJ for Triangles Detection in Large Graphs

Extending TWJ for Triangles Detection in Large Graphs

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international conference on Embedded & distributed Systems (EDiS)

作者： Sarah Balbouzi Lougmiri Zekri Kheira Mechach Department of computer science Ahmed Ben Bella Oran1 University Oran Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

In light of social networks' expansion and the ever-increasing number of subscribers who daily sign up to them. Detecting and counting triangles of subscribers in these networks are the basis of community detection. This detection is expressed as the computation of intersections between neighbors' subscribers lists. However, it remains very cumbersome and time-consuming to perform intersection operations. The significant detection cost is due to the fact that, until now, the treatment of intersections has been approached naively. Indeed, each element in the sorted neighborhood list of two neighboring vertices (listel and list2) in the network is compared one by one until one of the lists is exhausted. Given large-scale context, this results in a very high complexity of |liste1| + |liste2|. In this paper, we introduce a new technique for triangle detection. This approach aims to optimize intersection computing through minimizing the number of comparisons skipping the neighbors of one list that have no chance appearing in the second one, and thus making the triangle detection in large-scale networks more efficient.

关键词： Costs Social networking (online) Image edge detection Clustering algorithms Routing Computational efficiency Complexity theory Sparks

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Weather Prediction from Historical Data using Big Data Technologies

Weather Prediction from Historical Data using Big Data Techn...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), international conference on

作者： Shaurya Choudha Aniruddh Singh Aman Kumar Gouthaman. P Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Weather forecasting is an important function in a variety of activities such as agriculture, environment monitoring, and disaster management among others. Today with the development of technology, vast amounts of meteorological data are able to be collected and analyzed with the intention of improving prediction accuracy. The purpose of this work is to analyze the contribution of Big Data frameworks such as, Hadoop, MapReduce and Apache Spark in effective weather prediction processes. In a distributed system, historical weather data, satellite images and sensor readings are kept and manipulated through the HDFS storage. For effective data manipulation, MapReduce and Spark are used along with Hive and Pig for searching large datasets. Predictive analytics are performed using machine learning algorithms included in the MLlib package of Spark. These results are presented using the Tableau application, which shows the speed and accuracy of this Big Data approach in comparison to classical ones. The proposed work ends with a look into the prospects of Big Data in the field of weather forecasting and relevant problems in this area.

关键词： Accuracy Machine learning algorithms Weather forecasting distributed databases Big Data Satellite images Sparks Predictive analytics Intelligent systems Monitoring

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Dynamic Clustering and Power Control for Two-Tier Over-the-Air Federated Learning

Dynamic Clustering and Power Control for Two-Tier Over-the-A...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Guo, Wei Huang, Chuan Qin, Xiaoqi Yan, Lian Zhang, Wei Chinese Univ Hong Kong Sch Sci & Engn SSE Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst FNii Shenzhen 518172 Peoples R China Peng Cheng Lab Shenzhen 518006 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Elect Sci & Technol China Informat & Commun Engn Coll Chengdu 611731 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge. However, direct computing based on the information from the devices may not be resource efficient due to the relatively large communication cost between the devices and parameter server (PS). This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

关键词： Wireless communication Minimization methods Upper bound Distance learning Clustering methods Power control Lead

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Parallel Implementation of SHA256 on Multizone Heterogeneous Systems

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yongtao Luo Jie Liu Tiaojie Xiao Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing program for Frontier Equipment National University of Defense Technology Changsha China National Supercomputer Center in Tianjin Tianjin China

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability.

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distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning 17

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Hu, Shucheng Ren, Tao Niu, Jianwei Hu, Zheyuan Xing, Guoliang Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou 450001 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781665406680

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the 'curse of dimensionality' in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： task offloading mobile edge computing multi-agent deep reinforcement learning

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Teaching Toy Car to Self-drive in a Game Environment using Reinforcement Learning

Teaching Toy Car to Self-drive in a Game Environment using R...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Purna Jain Saumya Tiwari Saud Hashmi Gaurav Sharma Department of Computer Science Engineering Medi-Caps University Indore India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

This research paper explores the use of reinforcement learning (RL) to teach a toy car to self-drive in a game environment. The study focuses on the development of an RL-based algorithm that enables the toy car to learn the optimal driving behavior through trial-and-error experience. The game environment provides a simulated environment for the toy car to navigate, and the RL algorithm rewards the car for taking the right actions while penalizing it for taking the wrong ones. The study also examines the effects of different reward structures and hyperparameters on the performance of the RL algorithm. The results of the study demonstrate that the toy car can learn to drive autonomously in the game environment and that the performance of the RL algorithm improves with appropriate reward structures and hyperparameters. The outcomes of this study’s conclusions may have an impact on the creation of autonomous driving systems and robotics.

关键词： Training Visualization Toy manufacturing industry Supervised learning distributed databases Stochastic processes Games

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