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A Community-Driven Approach to Democratize Access to Satellite Ground Stations 21

A Community-Driven Approach to Democratize Access to Satelli...

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27th ACM Annual international conference on Mobile computing and networking (MobiCom)

作者： Singh, Vaibhav Prabhakara, Akarsh Zhang, Diana Yagan, Osman Kumar, Swarun Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450383424

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. This is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure. In this paper, we present a community-driven distributed reception paradigm for LEO satellite signals where signals received on many tiny handheld receivers (not necessarily deployed on rooftops but also indoors) are coherently combined to recover the desired signal. This is made possible by employing new synchronization and receiver orientation techniques that study satellite trajectories and leverage the presence of other ambient signals. We compare our results with a large commercial receiver deployed on a rooftop and show a 8 dB SNR increase both indoors and outdoors using 8 receivers, costing $38 per RF frontend.

关键词： LEO Satellites Ground stations Synchronization Coherent Combination low-cost distributed Systems

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Arbitrary Pattern Formation on Infinite Regular Tessellation Graphs 21

Arbitrary Pattern Formation on Infinite Regular Tessellation...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Cicerone, Serafino Di Fonso, Alessia Di Stefano, Gabriele Navarra, Alfredo Univ Aquila Dip Ingn & Sci Informaz & Matemat Laquila Italy Univ Perugia Dip Matemat & Informat Perugia Italy

ISBN: (纸本)9781450389334

Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that vertical bar R vertical bar = vertical bar F vertical bar, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible discretization of the Euclidean plane only the standard square grid has been considered in the context of the classical Look-Compute-Move model. However, it is natural to consider the other regular tessellation graphs, that are triangular and hexagonal grids. For any regular tessellation graph, we provide a resolution algorithm for APF when the initial configuration is asymmetric.

关键词： distributed algorithms Mobile and collaborative systems Asynchrony Self-organization Pattern formation Graphs

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Secure Conflict-free Replicated Data Types 21

Secure Conflict-free Replicated Data Types

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22nd international conference on distributed computing and networking (ICDCN)

作者： Barbosa, Manuel Ferreira, Bernardo Marques, Joao Portela, Bernardo Preguica, Nuno DCC FC UP Porto Portugal INESC TEC Porto Portugal DI FC UL Lisbon Portugal LASIGE Lisbon Portugal DI FCT UNL Lisbon Portugal NOVA LINCS Costa Da Caparica Portugal

ISBN: (纸本)9781450389334

Conflict-free Replicated Data Types (CRDTs) are abstract data types that support developers when designing and reasoning about distributed systems with eventual consistency guarantees. In their core they solve the problem of how to deal with concurrent operations, in a way that is transparent for developers. However in the real world, distributed systems also suffer from other relevant problems, including security and privacy issues and especially when participants can be untrusted. In this paper we present new privacy-preserving CRDT protocols that can be used to help secure distributed cloud-backed applications, including NoSQL geo-replicated databases. Our proposals are based on standard CRDTs, such as sets and counters, augmented with cryptographic mechanisms that allow their operations to be performed on encrypted data. We accompany our proposals with formal security proofs and implement and integrate them in An-tidoteDB, a geo-replicated NoSQL database that leverages CRDTs for its operations. Experimental evaluations based on the Danish Shared Medication Record dataset (FMK) exhibit the tradeoffs that our different proposals make and show that they are ready to be used in practical applications.

关键词： distributed Systems Cloud computing Security

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distributed Learning for Application Placement at the Edge Minimizing Active Nodes

Distributed Learning for Application Placement at the Edge M...

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6G networking (6GNet), international conference on

作者： Claudia Torres-Pérez Estefanía Coronado Cristina Cervelló-Pastor Juan Sebastian Camargo Muhammad Shuaib Siddiqui i2CAT Foundation Barcelona Spain Universidad de Castilla-La Mancha Albacete Spain Universitat Politécnica de Catalunya Castelldefels Barcelona Spain

The main goal of application placement in Multi-Access Edge computing (MEC) is to map their requirements to the infrastructure for desired Service Level Agreement (SLA). In highly distributed infrastructures in beyond 5G and 6G networks, meeting this need and minimizing energy use are crucial. Fo-cusing solely on meeting SLA requirements can lead to resource fragmentation and reduced energy efficiency, as nodes utilize only a small portion of their resources. Furthermore, when multiple orchestrators govern MEC nodes, achieving optimal efficiency becomes a more complex challenge. This paper addresses the application placement problem by employing distributed deep reinforcement learning to efficiently minimize the overall cost of active MEC nodes in a distributed scenario involving multiple MEC systems. Our technique reduces the number of active nodes maintaining an average accuracy of up to 98%, meets SLA requirements, and is scalable for hosting several MEC nodes.

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography 33

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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33rd Euromicro international conference on Parallel, distributed, and Network-Based Processing, PDP 2025

作者： Russo, Massimiliano Nisar, Mehwish Pauciullo, Antonio Imperatore, Pasquale Lapegna, Marco Romano, Diego University of Naples Federico II Dept. of Mathematics and Applications Naples Italy National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (纸本)9798331524937

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth's surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring. © 2025 IEEE.

关键词： Light scattering

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Machine Learning Based Sound Speed Prediction for Underwater networking Applications 17

Machine Learning Based Sound Speed Prediction for Underwater...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Ahmed, Ambrin B. Riaz Younis, Mohamed De Leon, Miguel Hernandez Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)9781665439299

Underwater acoustic networks operate in an inhomogeneous and dynamic environment, which makes it difficult to model the propagation path of signals. In essence acoustic signals experience reflection and refraction due to sound speed variation, based on many parameters such as salinity, temperature, and depth. To enable modeling of signal propagation, the sound speed profile (SSP) has to be accurately estimated. The most famous SSP equation has been proposed by Mackenzie and has been widely used among others like the Coppens' and UNESCO equations. The drawback of these equations is that they yield different accuracy levels for various setups. They are also valid only for certain limits of salinity, depth and temperature. Moreover, the SSP estimation method should suit both deep and shallow water environments. In this paper, we use machine learning algorithms to predict sound speed in both deep and shallow waters and compare our results with data collected from acoustic tomography measurements. For training we have considered sound speed measurements across various oceans like Pacific Ocean, Arctic Ocean, Indian Ocean, etc. Our results show that our model achieves 99.99% accuracy and outperforms Leroy and Mackenzie equations.

关键词： Sound speed acoustic communication Underwater networks Machine learning

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Decentralized Data Privacy Protection and Cloud Auditing Security Management 3

Decentralized Data Privacy Protection and Cloud Auditing Sec...

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3rd IEEE international conference on computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Mageshwari, M. Naresh, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chengalpattu Tamilnadu Chennai603 203 India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chengalpattu Tamilnadu Chennai603 203 India

ISBN: (纸本)9781665462006

Decentralized data protection is becoming more important as more firms employ Cloud resources. Securing, protecting, and processing user data are primary issues that Cloud computing faces. This study's primary goal is to detect and address attack protection in Cloud. Our objectives are to comprehend the present state of cloud computing security concerns and methods, identify future cloud computing security concerns to offer countermeasures for potential. Preventative measures to employing cloud services include data security and privacy concerns. The challenge is to create cloud platforms for integrating decentralized information systems to ensure the user's privacy. For management units of distributed systems, the requirements analysis yielded a model for organizational levels of cloud systems. By bypassing the single-point-of-failure of TPA, our suggested approach may prevent service disruption and privacy-preserving concerns. The extensive security analysis proves that our suggested solution meets all the needed security criteria. We also implement our suggested method with PSO-Homomorphic Algorithm (PSO-HA) and evaluate its efficiency in real-world situations, demonstrating its efficacy, high efficiency, and portability. Our results show that our methodology for simultaneously-integrated recovery can be implemented in polynomial time and is thriving. © 2022 IEEE.

关键词： Cloud security

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EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle via Edge Devices

EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle...

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international conference on computing and networking Technology (ICCNT)

作者： V Arunodhai Leena P Susan Jinesh M. Kannimoola Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Internet of Things (IoT) revolutionizes technology interaction by enabling seamless connectivity and automation, but it introduces significant security challenges. A distributed ledger technology (DLT) called IOTA Tangle, a utilizing a Directed Acyclic Graph (DAG), enables secure and feeless transactions among IoT devices. Despite its advantages, the IOTA Tangle faces threats such as replay attacks and double spending, which highlight vulnerabilities in distributed ledger technologies. To address these security concerns, we initially developed EdgeGuard, a rule-based system for spam detection within the IOTA ecosystem. However, its reliance on predetermined rulesets limited its adaptability. We have now enhanced our approach with EdgeShield, incorporating a neural network for superior threat *** enhances adaptability over EdgeGuard in threat detection for the IOTA Tangle by integrating machine learning. EdgeShield analyzes network packets to identify threats, leveraging machine learning to adapt to evolving risks. Trained using the MQTTSet dataset, it detects network-based threats such as denial-of-service (DoS) attacks and packet flooding. Tested with simulated IoT data, EdgeShield demonstrates effectiveness across various domains, particularly in healthcare IoT. Its decentralized processing on edge devices like the Raspberry Pi 4 enables immediate network traffic analysis, reducing reliance on centralized servers and improving system resilience. Additionally, real-time alerts empower administrators to proactively secure IoT deployments and maintain transaction integrity within the IOTA Tangle. This paper traces our research evolution from EdgeGuard to EdgeShield, highlighting advancements and demonstrating the enhanced security of IoT transactions on the IOTA Tangle.

关键词： distributed ledger Image edge detection Neural networks Machine learning Telecommunication traffic Real-time systems Threat assessment Internet of Things Security Servers

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FedAT: A High-Performance and Communication -Efficient Federated Learning System with Asynchronous Tiers 21

FedAT: A High-Performance and Communication -Efficient Feder...

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international conference for High Performance computing, networking, Storage and Analysis (SC21)

作者： Chai, Zheng Chen, Yujing Anwar, Ali Zhao, Liang Cheng, Yue Rangwala, Huzefa George Mason Univ Fairfax VA 22030 USA IBM Res Almaden San Jose CA USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781450384421

Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized and private. This form of collaborative learning exposes new tradeoffs among model convergence speed, model accuracy, balance across clients, and communication cost, with new challenges including: (1) straggler problem where clients lag due to data or (computing and network) resource heterogeneity, and (2) communication bottleneck where a large number of clients communicate their local updates to a central server and bottleneck the server. Many existing FL methods focus on optimizing along only one single dimension of the tradeoff space. Existing solutions use asynchronous model updating or tiering -based, synchronous mechanisms to tackle the straggler problem. However, asynchronous methods can easily create a communication bottleneck, while tiering may introduce biases that favor faster tiers with shorter response latencies. To address these issues, we present FedAT, a novel Federated learning system with Asynchronous Tiers under Non-i.i.d. training data. FedAT synergistically combines synchronous, intra-tier training and asynchronous, cross -tier training. By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy. FedAT uses a straggler-aware, weighted aggregation heuristic to steer and balance the training across clients for further accuracy improvement. FedAT compresses uplink and downlink communications using an efficient, polyline-encodingbased compression algorithm, which minimizes the communication cost. Results show that FedAT improves the prediction performance by up to 21.09% and reduces the communication cost by up to S.5x, compared to state-of-the-art FL methods.

关键词： federated learning asynchronous distributed learning communication efficiency tiering weighted aggregation

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DDoS Attack Detection System for IoT Enabled Smart City Applications with Correlation Analysis

DDoS Attack Detection System for IoT Enabled Smart City Appl...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Veeramachaneni Nirosha V. Hemamalini Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur India

The topic of cybersecurity remains a significant concern for industries involved in digital operations, as seen by the recurring increase in security incidents. The increasing use of Internet of Things (IoT) devices in various settings such as homes, offices, transportation, and healthcare has resulted in a corresponding rise in the frequency of malicious assaults. Machine Learning (ML) is frequently utilized for the purpose of detecting attacks on IoT devices. This study presents a novel approach to cybersecurity-focused network traffic correlation analysis and the detection of distributed Denial of Service (DDoS) attacks using artificial intelligence techniques in the context of an IoT-enabled smart city, with the aim of promoting sustainability. A traffic analysis was conducted by utilizing a wireshark network analyser tool, which effectively improves data transmission. The experimental investigation was conducted to evaluate various performance metrics, including accuracy, precision, recall, attack detection rate and time. The proposed technique achieved anaccuracy rate of 96%, precision and recall rates are 93.6% and 91% respectively.

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