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A Task Transmission Path Selection Algorithm Based Object Identification and Intent-Driven in Uncertain Environments 22

A Task Transmission Path Selection Algorithm Based Object Id...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Hu, Lei Zhang, Yang Zhang, Shukui Pan, Wubing Wang, Yingying Soochow University School of Computer Science and Technology Suzhou215006 China Suzhou Global Institute School of Information & Software Suzhou215163 China

ISBN: (纸本)9798331509712

In environments lacking infrastructure, such as the battlefield, a diverse array of intelligent devices continues to evolve. These devices face challenges in flexibly addressing resource allocation and information transmission within their communication range, necessitating exploration of business awareness and resource identification technologies. Addressing these issues hinges on solving object identification problems related to address, identity, content, service, location, and resource. To tackle these challenges, we propose a Mult-IP object identification mechanism that serves as the foundation for information exchange, task allocation, and service provisioning among heterogeneous communication entities. Additionally, we design a multi-dimensional resource and service intelligence matching mechanism based on object identification. Building upon this framework is the intent-driven control algorithm AntEdge which dynamically senses task states using information encapsulated in Mult-IP packets and optimizes task packet transmission according to business intentions. Finally, leveraging the Mult-IP object identification mechanism enables task offloading to achieve timely completion of tasks within limited timeframes. Simulation experiments demonstrate that our proposed algorithm effectively enhances task completion ratios. © 2024 IEEE.

关键词： Mobile ad hoc networks

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An Efficient Sampling-Based SpMM Kernel for Balancing Accuracy and Speed in GNN Inference 22

An Efficient Sampling-Based SpMM Kernel for Balancing Accura...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Song, Yingchen Wang, Yaobin Xiong, Chaoyu Wang, Tianhai Tang, Pingping Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China

ISBN: (纸本)9798331509712

How to coordinate the design of sampling and Sparse-dense Matrix Multiplication (SpMM) is important in Graph Neural Network (GNN) acceleration. However, existing methods have an imbalance between accuracy and speed in performing GNN inference tasks due to irrational sampling strategies. To solve this problem, we propose an adaptive edge sampling strategy SpMM kernel. It considers the relationship between the number of non-zero elements in each matrix row and the shared memory width. The edge sampling scheme is adaptively selected according to the different situations of each row. Our method reduces the graph size by adaptive edge sampling to fit into the GPU's shared memory, which decreases the computational cost and increases the data locality ultimately achieving a balance between accuracy and speed in GNN inference. We conducted experiments on NVIDIA RTX 4060 Ti GPU using representative GNN model and datasets. Experimental results show that our designed kernel outperforms the cuSPARSE SpMM kernel and GE-SpMM by up to 20.2× and 17.3× respectively, with less than 1% accuracy loss. Compared to ES-SpMM, it reduces the accuracy loss by 4.7% on average and achieves an average 1.26× speedup. © 2024 IEEE.

关键词： Graph neural networks

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Service Migration Algorithm for distributed Edge Computing in 5G/6G networks 23rd

Service Migration Algorithm for Distributed Edge Computing i...

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23rd international Conference on Next Generation Wired/Wireless networks and systems (NEW2AN) / 16th Conference on Internet of Things and Smart Spaces (RuSMART)

作者： Kuznetsov, Konstantin Kuzmina, Ekaterina Lapteva, Tatiana Volkov, Artem Muthanna, Ammar Aziz, Ahmed St Petersburg State Univ Telecommun Dept Telecommun Networks & Data Transmiss St Petersburg Russia

ISBN: (纸本)9783031609930;9783031609947

In recent years, the volumes of information processed and transmitted over the network are rapidly increasing, while the demand for speed and quality of transmitted data is also increasing. As a result, there is a need to use edge computing, which allows reducing network response time and more efficiently using bandwidth, as well as significantly improving the performance of the data transmission system. Article describes the application of edge computing in the form of transferring processes to an edge computing cluster, and presents the results of research on existing migration processes, full transfer time and downtime, identifying the optimal migration process for further implementation of the service migration automation algorithm. The following empirical research methods were used in the work: comparative evaluation method and experiment. The work considers the transfer of computational processes to an edge computing cluster;a comparative analysis of methods for automated service migration is carried out;an optimal migration strategy is identified;an algorithm for automating service migration is developed. Solutions related to automation of migration are excellent for use in the field of Internet of Things. They contribute to improving application performance and reducing response time. Automatic service migration allows increasing process efficiency and reducing system maintenance costs."

关键词： kubernetes K8s virtualization service migration edge computing

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Application of distributed network in remote indoor temperature control and energy conservation system

Application of distributed network in remote indoor temperat...

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2023 international Conference on Intelligent systems, Communications, and computer networks, ISCCN 2023

作者： Jin, Sheng-Lang Que, Fei Tourism College Huangshan University Anhui Huangshan245041 China Zhejiang Institute of Economics and Trade Hangzhou310018 China

ISBN: (纸本)9781510666238

In the present work, a distributed network-based remote indoor temperature control and energy conservation system is proposed. The system provides such functions as remote data transfer and remote control, as well as a distributed information sharing and communication platform. Meanwhile, the system integrates an intelligent control system and a database management system, on which the data obtained from the Internet are first assessed by the expert panel and then processed by the intelligent control system. In this way, real-Time intelligent control is achieved to allow the user to control the indoor temperature and production conditions of the greenhouse through the network. During the design of the system, a wireless monitoring system is developed as per the specific location and geographic conditions for remote surveillance. The field test shows that compared with the control plot, the experiment plot reduced the gas consumption by 2802.4 m3, reduced energy consumption by 24935.0 kW h. The work is expected to provide a solution for development of remote indoor temperature control and energy conservation systems in real-world scenarios. © 2023 SPIE. All rights reserved.

关键词： Energy conservation

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Framework for Optimizing Distribution of Deep Neural Network Inference Tasks across Computing networks

Framework for Optimizing Distribution of Deep Neural Network...

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2024 international symposium on networks, computers and Communications, ISNCC 2024

作者： Preetha Evangeline, D. Sahu, Atma R. Anandhakumar, P. Siva Subramaniya Nadar College of Engineering Dept. of Information Technology Tamil Nadu Kalavakkam India Coppin State University Dept. of Mathematics and Computer Science BaltimoreMD United States Madras Institute of Technology Anna University Dept. Of Information Technology Tamil Nadu India

ISBN: (纸本)9798350364910

Ubiquitous artificial intelligence (AI) is considered one of the key services in 6G systems. AI services typically rely on deep neural networks (DNN) requiring heavy computation. Hence, to support ubiquitous AI, it is crucial to provide a solution for offloading or distributing computational burden due to DNN, especially at end devices with limited resources. This paper proposes an optimization framework for assigning the tasks of DNN layer computations to computing resources in the network, to reduce the inference latency. To this end, we propose a layered graph model with which simple conventional routing jointly solves the problem of selecting nodes for computation and paths for data transfer between nodes. The proposed framework is applied to derive algorithms for minimizing the end-to-end inference latency. The numerical evaluations show that our new formulation can find a solution for DNN inference job distribution much faster than the existing formulation and that our algorithms can select computing nodes and data paths adaptively to the computational attributes of given DNN inference jobs. © 2024 IEEE.

关键词： Computation offloading

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Envision: Application Level Fairness for Cooperative Edge-AI Services with Deep Reinforcement Learning 22

Envision: Application Level Fairness for Cooperative Edge-AI...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Huang, Jiale Chen, Long Zheng, Shaojie Wu, Jigang Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China

ISBN: (纸本)9798331509712

Edge artificial intelligence (Edge-AI) is emerging with the proliferation of both multi-access edge computing (MEC) and AI. Cooperative Edge-AI can not only increase the computing resource utilization ratio with edge-edge collaboration, but also improve the big data processing efficiency of mobile end devices through computation offloading to a group of edge servers. Existing paradigms for cooperative edge-AI applications are not tailored for heterogeneous types of applications, thus harming the quality of experience (QoE) of different application users or operators in the network. This paper thus fills the gap by firstly defining the fairness index as service completion ratio, and then formulating the max-min fairness problem subject to edge server's storage, computation, deadline constraints and so on. The problem is proven to be NP-hard through reduction from a well-known NP-complete problem, the multi-knapsack problem. To tackle the dynamics of both computing resources and channel fading conditions, a deep reinforcement learning algorithm is invented on the basis of buffer replay and evaluation-target networks, to derive the joint service deployment and computation offloading strategy. Extensive experimental results demonstrate that the proposed scheme named as Envision is at least 17× faster than the existing ORA algorithm. © 2024 IEEE.

关键词： Deep reinforcement learning

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Accumulating Priority Queue for Charging of Unmanned Aerial Vehicles in Cognitive Radio networks 6

Accumulating Priority Queue for Charging of Unmanned Aerial ...

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6th international symposium on Autonomous systems, ISAS 2023

作者： Li, Yuran Zhai, Xiangping Bryce Wang, Ran Zhu, Jing Yao, Chenghua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9798350316155

The combination of cognitive radio networks and unmanned aerial vehicles (UAVs) can overcome the scarcity of spectrum for wireless networks and provide important benefits for large-scale deployment of UAVs. However, energy constraint is an inevitable challenge for UAV communication. In order to prolong the lifetime of UAVs, we apply the accumulating priority queue (APQ) discipline for the first time to the queuing charging process of UAVs in cognitive radio networks. The APQ is based on the classic priority queue with the accumulation rate added to it, which means that the priority of a user is a function of the waiting time. By analyzing the values for different arrival rates and priorities, we discuss the factors that affect the average waiting times and key performance indicators. We also consider the related optimization problem and propose an improved bisection algorithm. According to the requirements of different classes of users, we can adjust the accumulation rates and arrival rates to meet the performance indicators. © 2023 IEEE.

关键词： Wireless networks

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31st international Colloquium on Structural Information and Communication Complexity, SIROCCO 2024

31st International Colloquium on Structural Information and ...

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31st international Colloquium on Structural Information and Communication Complexity, SIROCCO 2024

ISBN: (纸本)9783031606021

The proceedings contain 31 papers. The special focus in this conference is on Structural Information and Communication Complexity. The topics include: Awake Complexity of distributed Minimum Spanning Tree;all You Need are Random Walks: Fast and Simple distributed Conductance Testing;k-Center Clustering in distributed Models;optimal Memory Requirement for Self-stabilizing Token Circulation;stand-Up Indulgent Gathering on Rings;in Search of the Lost Tree;near-Optimal Fault Tolerance for Efficient Batch Matrix Multiplication via an Additive Combinatorics Lens;deterministic Leader Election for Stationary Programmable Matter with Common Direction;mutual Visibility in Hypercube-Like Graphs;non-negotiating distributed Computing;better Sooner Rather Than Later;Highly-Efficient Persistent FIFO Queues;locally Balanced Allocations Under Strong Byzantine Influence;distributed Fractional Local Ratio and Independent Set Approximation;towards Singular Optimality in the Presence of Local Initial Knowledge;efficient Wait-Free Linearizable Implementations of Approximate Bounded Counters Using Read-Write Registers;on distributed Computation of the Minimum Triangle Edge Transversal;stability of P2P networks Under Greedy Peering;universal Coating by 3D Hybrid Programmable Matter;distributed Binary Labeling Problems in High-Degree Graphs;Computing Replacement Paths in the CONGEST Model;reaching Agreement Among k out of n Processes;on the Bit Complexity of Iterated Memory;sharding in Permissionless systems in Presence of an Adaptive Adversary;online Drone Scheduling for Last-Mile Delivery;efficient Self-stabilizing Simulations of Energy-Restricted Mobile Robots by Asynchronous Luminous Mobile Robots;network Abstractions for Characterizing Communication Requirements in Asynchronous distributed systems;on the Existence of Consensus Converging Organized Groups in Large Social networks;multi-agent Online Graph Exploration on Cycles and Tadpole Graphs.

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Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments 11th

Adaptive Cyber Defence: Leveraging GANs for Simulating and ...

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11th international symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Rao, P. Kiran Chatterjee, Subarna Prakash, P. Suman Ramana, K. Seshadri Rajeev Gandhi Memorial College of Engineering and Technology Andra Pradesh Nandyal518501 India Department of CSE MS Ramaiah University of Applied Science Karnataka Bengaluru India Department of CAI G Pullaiah College of Engineering and Technology Andra Pradesh Kurnool India Department of CAI Ravindra College of Engineering for Women Andra Pradesh Kurnoo518001 India

ISBN: (纸本)9789819797615

The progressive dissemination of the Internet of Things (IoT) and Wireless Sensor networks (WSNs) has ushered in a new era of connectivity, with vast applications spanning from medicare to smart city infrastructure. However, this expansion has been paralleled by a corresponding increase in the sophistication and variety of cyber threats targeting these networks. Traditional cyber security measures, designed for a less dynamic threat landscape, are proving increasingly insufficient in protecting against the innovative and varied attack methods now in commonplace. This study introduces an innovative application of Generative Adversarial networks (GANs) to address this challenge, presenting a novel framework for the simulation and mitigation of advanced network attacks, particularly focusing on distributed Denial of Service (DDoS) and spoofing attacks which pose significant threats in IoT environments. Generative Adversarial networks (GANs), comprising two neural networks-the generator and the discriminator compete in a game-theoretic scenario, facilitating a deep understanding of attack patterns through the generation of realistic, synthetic cyber-attack scenarios. This research exploits GANs to bridge the gap between the static nature of traditional security protocols and the dynamic, evolving landscape of cyber threats. By training on a comprehensive dataset of known attacks and normal network activities, our proposed model, the Dynamic Adaptive Threat Simulation GAN (DATS-GAN), is capable of producing varied and realistic attack scenarios. These simulations serve a dual purpose: they not only enhance the detection capabilities and responsiveness of current security systems but also provide a basis for the development of new, adaptive security mechanisms capable of dynamically responding to the ever-changing cyber threat landscape. The effectiveness of DATS-GAN is demonstrated through extensive empirical analysis, highlighting significant improvements in the detecti

关键词： Generative adversarial networks

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A Novel Bidirectional distributed Cross Domain Solution Security Architecture 11

A Novel Bidirectional Distributed Cross Domain Solution Secu...

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11th international symposium on Telecommunication, IST 2024

作者： Ansariyan, Sajjad Doostari, Mohammadali Shahed University Faculty of Computer Engineering Tehran Iran

ISBN: (纸本)9798350356250

With the paradigm change and the need for two-way communication and secure data transfer based on two-way correspondence between security domains with high and low information importance, the need for cross-domain solutions (CDS) has increased. The cross-domain solution is a security approach that implements data exchange between different security domains by security policies. Nowadays, distributed energy resources(DER) as a solution for small-scale power plants to produce electricity are growing more and more due to their high *** exchange between power plants and its upstream security domain, such as the control center of the SCADA, is an inevitable necessity. To establish two-way communication, CDS equipment must follow the two-way security policy of security domains. By establishing a two-way correspondence between the high and low security domains, the CDS is able to confirm the request and the associated response and reject it otherwise. This study proposes two central ideas. The first idea proposes a two-way CDS architecture that is able to check the correspondence between the response and the received request in SCADA based on DER architecture. The second idea is a distributed CDS architecture that is able to connect several low security domains to a control center in a high security domain. Considering the large number of solar power plants as a low-security domain and how to connect them to the SCADA control center in the high-security domain, using a one-to-one architecture is a costly approach. Obviously, with the implementation of distributed architecture may lead to reduction of CDS network downtime, scalability, redundancy, flexibility, cost-effectiveness, advanced fault tolerance and preventing a single point of failure in CDS. In the current studies, there is no distributed CDS and CDS by checking two-way correspondence of packets. Therefore, in this study, a prototype of a distributed CDS architecture and two-way CDS architecture based

关键词： SCADA systems

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