Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals, Tom; Volckaert, Bruno; Turck, Filip De

doi:10.1109/ojcoms.2021.3116437

Cited by 7 publications

(2 citation statements)

References 145 publications

(143 reference statements)

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“…The advent of federated learning marks a disruptive breakthrough in the field of ma-chine learning that involves a decentralized training mechanism across the devices net-work as part of the preservation of data privacy. Different from the centralized teaching techniques, with federated learning, the models are trained locally on the devices of indi-viduals, therefore the minimum amount of raw data needs to be exchanged between them [12]. In a nutshell, this trend is of the most significance in cases where privacy is of high priority, say in smart vehicle networks, where highly sensitive information is gener-ated and shared.…”

Section: Introductionmentioning

confidence: 99%

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Javaid Ahmad Malik

2024

pst

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Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

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Section: Introductionmentioning

confidence: 99%

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Javaid Ahmad Malik

2024

pst

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“…Additional obstacles occur due to the requirement to transfer and manage sensitive data at the network's edge while simultaneously addressing concerns regarding privacy and security. Another significant consideration is how AI algorithms interact with different UAV platforms and edge computing architectures regarding scalability and interoperability 13 . It is necessary for researchers from various domains, such as AI, Edge Computing (EC), communication, and cybersecurity, to collaborate to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

Suganya,

Gopi,

Kumar

et al. 2024

Sci Rep

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Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.

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Industrial Edge MLOps: Overview and Challenges

Rani,

Chollet,

Vogt

et al. 2024

Computer Aided Chemical Engineering

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Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Cited by 7 publications

References 145 publications

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

Industrial Edge MLOps: Overview and Challenges

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