Federated Edge Intelligence and Edge Caching Mechanisms

Karras, Aristeidis; Karras, Christos; Giotopoulos, Konstantinos C.; Tsolis, Dimitrios; Οικονόμου, Κωνσταντίνος; Sioutas, Spyros

doi:10.3390/info14070414

Cited by 11 publications

(4 citation statements)

References 63 publications

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“…Each Raspberry Pi is configured with 4 GB of RAM and a 1.5 GHz quad-core processor. Importantly, these Raspberry Pi units are connected to a distinct network, separate from the HPC cluster, to emulate a realistic communication scenario as we have proposed in [89,90].…”

Section: Computational Framework For Iot Model Training and Evaluationmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

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Section: Computational Framework For Iot Model Training and Evaluationmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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“…Edge intelligence and TinyML, underscored by recent studies [34][35][36], emerge as essential technological improvements with the potential to significantly reshape precision agriculture, increasing its efficiency and productivity. By processing data locally, edge computing systems not only enhance the reliability of complex communication protocols but also encourage further improvements in precision agriculture methodologies.…”

Section: Machine Learning Techniques In Precision Agriculture and Agr...mentioning

confidence: 99%

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Balatsouras,

Karras,

Karras

et al. 2023

Sensors

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In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.

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“…Instead of transmitting individual client data to a central server, the central server sends its model to the clients, and each client trains the model with its own data. This approach helps protect the privacy of the data collected by autonomous vehicles while still allowing for model improvement [187,193].…”

Section: Privacy Preservation Techniques In Vehicular Communicationsmentioning

confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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Federated Edge Intelligence and Edge Caching Mechanisms

Cited by 11 publications

References 63 publications

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

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