FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384342262> ?p ?o ?g. }

• W4384342262 abstract "Abstract An intensive computation source has become increasingly important in recent years to meet the time-critical and low-latency needs of Industrial Internet of Things (IIoT) systems. Existing IIoT-based devices are built with limited computational resources, delivering results in a limited fashion when used in highly resource-intensive applications. Since then, a novel concept known as Edge Computing (EC) has been introduced to reduce network latency and alleviate strain on cloud data centers using an EC server located at the network's periphery. The EC server only managed to gather a small number of resources compared to the resource cloud. Without prior context about task deadline and load, an EC server could not optimally handle latency-sensitive and computation-intensive tasks. Additionally, the EC server did not significantly improve overhead minimization when sending data to and from the remote cloud and the user's device. Parallel to the development of EC, nonorthogonal multiple access (NOMA) has been identified as a technique with the potential to substantially increase spectrum efficiency. In this paper, a NOMA-based EC framework for IIoT system is examined, in which multiple task nodes transfer their task via NOMA to multiple edge servers in proximity for execution. As such, this paper aims to develop a joint optimization model for making decisions about task offloading and allocating resources in Industrial edge computing. An adaptive resource allocation decision model (ARADM) based on deep reinforcement learning (DRL) and heuristically modified long short-term memory (H-LSTM) using hybrid Cat and Mouse Dingo Optimization (HCMDO) is proposed to allocate the task optimally. We formulate joint optimization by considering multi-constraint objective function with communication, computation, and cache parameters using HCMDO. Further, these optimal parameters are used in training an H-LSTM along with benchmark dataset. The outcome of the H-LSTM network utilized in DRL to improve convergence speed, accuracy and stability by predicting optimal cost and load. The goal is to minimize service delay, energy consumption, balance load and maximize resource utilization. The experimental results validated the developed model and its ability to improve the quality of resource allocation in Industrial edge computing." @default.
• W4384342262 created "2023-07-15" @default.
• W4384342262 creator A5069199633 @default.
• W4384342262 creator A5077629977 @default.
• W4384342262 date "2023-07-14" @default.
• W4384342262 modified "2023-09-25" @default.
• W4384342262 title "Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach" @default.
• W4384342262 cites W2169539509 @default.
• W4384342262 cites W2615926310 @default.
• W4384342262 cites W2792526074 @default.
• W4384342262 cites W2888894561 @default.
• W4384342262 cites W2896918824 @default.
• W4384342262 cites W2904245688 @default.
• W4384342262 cites W2908816692 @default.
• W4384342262 cites W2920054549 @default.
• W4384342262 cites W2952544742 @default.
• W4384342262 cites W2962766183 @default.
• W4384342262 cites W2963529010 @default.
• W4384342262 cites W2963644017 @default.
• W4384342262 cites W2979224496 @default.
• W4384342262 cites W2981134958 @default.
• W4384342262 cites W2981726317 @default.
• W4384342262 cites W2989463999 @default.
• W4384342262 cites W2994626336 @default.
• W4384342262 cites W2995057554 @default.
• W4384342262 cites W3007564224 @default.
• W4384342262 cites W3008703059 @default.
• W4384342262 cites W3010723141 @default.
• W4384342262 cites W3032998286 @default.
• W4384342262 cites W3034730089 @default.
• W4384342262 cites W3046117130 @default.
• W4384342262 cites W3092644015 @default.
• W4384342262 cites W3094445110 @default.
• W4384342262 cites W3117089396 @default.
• W4384342262 cites W3120444619 @default.
• W4384342262 cites W3123895149 @default.
• W4384342262 cites W3130210998 @default.
• W4384342262 cites W3131678150 @default.
• W4384342262 cites W3135506433 @default.
• W4384342262 cites W3158904979 @default.
• W4384342262 cites W3158998940 @default.
• W4384342262 cites W3166618232 @default.
• W4384342262 cites W3189033801 @default.
• W4384342262 cites W3189938971 @default.
• W4384342262 cites W3197538542 @default.
• W4384342262 cites W3204676010 @default.
• W4384342262 cites W3214285294 @default.
• W4384342262 cites W3216388332 @default.
• W4384342262 cites W4200135040 @default.
• W4384342262 cites W4205136930 @default.
• W4384342262 cites W4206044098 @default.
• W4384342262 cites W4221143742 @default.
• W4384342262 cites W4281384582 @default.
• W4384342262 cites W4285820401 @default.
• W4384342262 cites W4293821713 @default.
• W4384342262 cites W4297509735 @default.
• W4384342262 cites W4312601533 @default.
• W4384342262 cites W4312772356 @default.
• W4384342262 doi "https://doi.org/10.21203/rs.3.rs-3150864/v1" @default.
• W4384342262 hasPublicationYear "2023" @default.
• W4384342262 type Work @default.
• W4384342262 citedByCount "0" @default.
• W4384342262 crossrefType "posted-content" @default.
• W4384342262 hasAuthorship W4384342262A5069199633 @default.
• W4384342262 hasAuthorship W4384342262A5077629977 @default.
• W4384342262 hasBestOaLocation W43843422621 @default.
• W4384342262 hasConcept C111919701 @default.
• W4384342262 hasConcept C120314980 @default.
• W4384342262 hasConcept C138236772 @default.
• W4384342262 hasConcept C154945302 @default.
• W4384342262 hasConcept C173801870 @default.
• W4384342262 hasConcept C2778456923 @default.
• W4384342262 hasConcept C29202148 @default.
• W4384342262 hasConcept C31258907 @default.
• W4384342262 hasConcept C41008148 @default.
• W4384342262 hasConcept C76155785 @default.
• W4384342262 hasConcept C79974875 @default.
• W4384342262 hasConcept C82876162 @default.
• W4384342262 hasConcept C93996380 @default.
• W4384342262 hasConceptScore W4384342262C111919701 @default.
• W4384342262 hasConceptScore W4384342262C120314980 @default.
• W4384342262 hasConceptScore W4384342262C138236772 @default.
• W4384342262 hasConceptScore W4384342262C154945302 @default.
• W4384342262 hasConceptScore W4384342262C173801870 @default.
• W4384342262 hasConceptScore W4384342262C2778456923 @default.
• W4384342262 hasConceptScore W4384342262C29202148 @default.
• W4384342262 hasConceptScore W4384342262C31258907 @default.
• W4384342262 hasConceptScore W4384342262C41008148 @default.
• W4384342262 hasConceptScore W4384342262C76155785 @default.
• W4384342262 hasConceptScore W4384342262C79974875 @default.
• W4384342262 hasConceptScore W4384342262C82876162 @default.
• W4384342262 hasConceptScore W4384342262C93996380 @default.
• W4384342262 hasLocation W43843422621 @default.
• W4384342262 hasOpenAccess W4384342262 @default.
• W4384342262 hasPrimaryLocation W43843422621 @default.
• W4384342262 hasRelatedWork W3009325463 @default.
• W4384342262 hasRelatedWork W3112631746 @default.
• W4384342262 hasRelatedWork W3156755687 @default.
• W4384342262 hasRelatedWork W3174690704 @default.
• W4384342262 hasRelatedWork W3175605422 @default.

• next