FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386432057> ?p ?o ?g. }

• W4386432057 endingPage "295" @default.
• W4386432057 startingPage "277" @default.
• W4386432057 abstract "For a wide range of smart agriculture use cases, the prospects of utilizing the Internet of Things (IoT) are immense. Many IoT devices can be deployed for precision farming, soil management, automated irritation, information gathering, or performing some local processing to provide various services. Due to the computational capacity limitation of IoT devices and their limited power, UAV-aided mobile-edge computing (MEC) is proposed to provide IoT nodes with additional resources by hosting their computation functions and making smart agriculture use cases more operational. On the other hand, from the implementation viewpoint, Network Function Virtualization (NFV) is an emerging approach recently proposed for flexible management of such computation functions in UAVs and MEC-server. However, efficient orchestration of the virtualized functions is a challenge. In this paper, we consider a decentralized UAV-aided MEC system in which the NFV-enabled processing nodes manage the computational tasks. To be more specific, we consider the smart agriculture use cases that need live streaming/analysis, such as surveillance or environmental monitoring. In such a network, we propose a method for orchestrating the NFVs efficiently, while the network energy consumption throughout the network is minimized. This problem becomes even more crucial when considering a strict condition on the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>instantaneous</i> AoI values. Therefore, the problem is first formulated as a Decentralized <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Constrained</i> Multi-agent Markov Decision Process (Dec-CMMDP). As the formulated problem is NEXP, in the next step, we exploit some structural features of the considered network and introduce the concept of symmetry to simplify the problem. Then, inspired by Augmented Lagrangian dual optimization, a novel decentralized, federated learning-based solution is proposed to solve the problem. Simulation results show the effectiveness of the proposed approach in minimizing the total network energy consumption, minimizing the average AoI, and satisfying the strict condition of <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>AoI</i> < 100 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>msec</i> for supporting real-time applications in our network parameter settings." @default.
• W4386432057 created "2023-09-05" @default.
• W4386432057 creator A5018710229 @default.
• W4386432057 creator A5054767234 @default.
• W4386432057 creator A5080109862 @default.
• W4386432057 creator A5089891162 @default.
• W4386432057 date "2023-01-01" @default.
• W4386432057 modified "2023-10-10" @default.
• W4386432057 title "Constrained Federated Learning for AoI-limited SFC in UAV-aided MEC for Smart Agriculture" @default.
• W4386432057 cites W1904495647 @default.
• W4386432057 cites W2076337359 @default.
• W4386432057 cites W2086556430 @default.
• W4386432057 cites W2088956500 @default.
• W4386432057 cites W2130307780 @default.
• W4386432057 cites W2131959252 @default.
• W4386432057 cites W2136473316 @default.
• W4386432057 cites W2145339207 @default.
• W4386432057 cites W2587466508 @default.
• W4386432057 cites W2604830243 @default.
• W4386432057 cites W2775482448 @default.
• W4386432057 cites W2783613629 @default.
• W4386432057 cites W2806576037 @default.
• W4386432057 cites W2900330501 @default.
• W4386432057 cites W2901980690 @default.
• W4386432057 cites W2962691117 @default.
• W4386432057 cites W2963318081 @default.
• W4386432057 cites W2991154860 @default.
• W4386432057 cites W3005863531 @default.
• W4386432057 cites W3044921708 @default.
• W4386432057 cites W3046016465 @default.
• W4386432057 cites W3046988891 @default.
• W4386432057 cites W3080577972 @default.
• W4386432057 cites W3102383207 @default.
• W4386432057 cites W3128201245 @default.
• W4386432057 cites W3134671698 @default.
• W4386432057 cites W3134949638 @default.
• W4386432057 cites W3137257456 @default.
• W4386432057 cites W3137963285 @default.
• W4386432057 cites W3138616181 @default.
• W4386432057 cites W3159080474 @default.
• W4386432057 cites W3161553238 @default.
• W4386432057 cites W3162366242 @default.
• W4386432057 cites W3162929413 @default.
• W4386432057 cites W3182677369 @default.
• W4386432057 cites W3206219421 @default.
• W4386432057 cites W4210498147 @default.
• W4386432057 cites W4210527295 @default.
• W4386432057 cites W4211024811 @default.
• W4386432057 cites W4232967792 @default.
• W4386432057 cites W4290994004 @default.
• W4386432057 cites W4295046589 @default.
• W4386432057 cites W4321780014 @default.
• W4386432057 cites W4323645975 @default.
• W4386432057 doi "https://doi.org/10.1109/tmlcn.2023.3311749" @default.
• W4386432057 hasPublicationYear "2023" @default.
• W4386432057 type Work @default.
• W4386432057 citedByCount "0" @default.
• W4386432057 crossrefType "journal-article" @default.
• W4386432057 hasAuthorship W4386432057A5018710229 @default.
• W4386432057 hasAuthorship W4386432057A5054767234 @default.
• W4386432057 hasAuthorship W4386432057A5080109862 @default.
• W4386432057 hasAuthorship W4386432057A5089891162 @default.
• W4386432057 hasBestOaLocation W43864320571 @default.
• W4386432057 hasConcept C120314980 @default.
• W4386432057 hasConcept C127413603 @default.
• W4386432057 hasConcept C142362112 @default.
• W4386432057 hasConcept C153349607 @default.
• W4386432057 hasConcept C154945302 @default.
• W4386432057 hasConcept C199168358 @default.
• W4386432057 hasConcept C41008148 @default.
• W4386432057 hasConcept C558565934 @default.
• W4386432057 hasConcept C62611344 @default.
• W4386432057 hasConcept C66938386 @default.
• W4386432057 hasConceptScore W4386432057C120314980 @default.
• W4386432057 hasConceptScore W4386432057C127413603 @default.
• W4386432057 hasConceptScore W4386432057C142362112 @default.
• W4386432057 hasConceptScore W4386432057C153349607 @default.
• W4386432057 hasConceptScore W4386432057C154945302 @default.
• W4386432057 hasConceptScore W4386432057C199168358 @default.
• W4386432057 hasConceptScore W4386432057C41008148 @default.
• W4386432057 hasConceptScore W4386432057C558565934 @default.
• W4386432057 hasConceptScore W4386432057C62611344 @default.
• W4386432057 hasConceptScore W4386432057C66938386 @default.
• W4386432057 hasFunder F4320307798 @default.
• W4386432057 hasFunder F4320322675 @default.
• W4386432057 hasLocation W43864320571 @default.
• W4386432057 hasOpenAccess W4386432057 @default.
• W4386432057 hasPrimaryLocation W43864320571 @default.
• W4386432057 hasRelatedWork W2094884983 @default.
• W4386432057 hasRelatedWork W2290927522 @default.
• W4386432057 hasRelatedWork W2378898096 @default.
• W4386432057 hasRelatedWork W2748952813 @default.
• W4386432057 hasRelatedWork W2899084033 @default.
• W4386432057 hasRelatedWork W2973657051 @default.
• W4386432057 hasRelatedWork W3066706303 @default.
• W4386432057 hasRelatedWork W4283579741 @default.
• W4386432057 hasRelatedWork W560952460 @default.
• W4386432057 hasRelatedWork W79913212 @default.

• next