Matches in SemOpenAlex for { <https://semopenalex.org/work/W3148408059> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3148408059 abstract "Smart grids merge intelligent computing technologies and electrical grid networks for better monitoring, control and management of electrical energy and facilities. The maturity of cloud computing has been the major driving factor for its adoption in smart grid deployments. Despite the elasticity of cloud resources, centrality and long distances to remote data centers cause high latency, high bandwidth consumptions and unstable connectivity, which are undesirable for IoT-based smart grid applications. Fog computing as an extension of cloud computing services to the edges of the network overcomes these challenges and perfectly suit the distributed nature of the low voltage (LV) electrical distribution networks as part of smart grid. The pressing issues with the adoption of fog computing for smart grid applications are finding the best placement plan for fog node locations in LV distribution networks to enhance monitoring and control. The main goal of this work is to present a mathematical model to address the aforementioned issues focusing on minimizing deployment cost and network delay. In addressing this multi-objective problem, a new algorithm, namely Future Search Particle Swarm Non-dominated Sorting Genetic Algorithm (FPNSGA), is proposed based on the combination of the best features of the NSGA-II, SMPSO, and a recently formed algorithm, Future Search. The effectiveness of the algorithm is evaluated based on the benchmarking technique (Weighted Sum approach), the convergence and diversification of the solutions using HV indicators and CPU time. The results from simulations show that the proposed mechanism is very competitive and outperforms other fog planning network schemes." @default.
- W3148408059 created "2021-04-13" @default.
- W3148408059 creator A5003228581 @default.
- W3148408059 creator A5011148656 @default.
- W3148408059 creator A5030812391 @default.
- W3148408059 date "2021-03-01" @default.
- W3148408059 modified "2023-09-23" @default.
- W3148408059 title "Evolutionary Approaches to Fog Node Placement in LV Distribution Networks" @default.
- W3148408059 cites W1558919105 @default.
- W3148408059 cites W1997929229 @default.
- W3148408059 cites W2104017154 @default.
- W3148408059 cites W2123115451 @default.
- W3148408059 cites W2126105956 @default.
- W3148408059 cites W2167505557 @default.
- W3148408059 cites W2442258215 @default.
- W3148408059 cites W2586181304 @default.
- W3148408059 cites W2771778153 @default.
- W3148408059 cites W2807637204 @default.
- W3148408059 cites W2808844758 @default.
- W3148408059 cites W2885657717 @default.
- W3148408059 cites W2890447671 @default.
- W3148408059 cites W2906195520 @default.
- W3148408059 cites W2910182792 @default.
- W3148408059 cites W2911645189 @default.
- W3148408059 cites W2974927677 @default.
- W3148408059 cites W2995644446 @default.
- W3148408059 cites W2998429528 @default.
- W3148408059 cites W3003798210 @default.
- W3148408059 cites W3005536503 @default.
- W3148408059 cites W3084847794 @default.
- W3148408059 cites W3118115463 @default.
- W3148408059 doi "https://doi.org/10.20508/ijsmartgrid.v5i1.141.g134" @default.
- W3148408059 hasPublicationYear "2021" @default.
- W3148408059 type Work @default.
- W3148408059 sameAs 3148408059 @default.
- W3148408059 citedByCount "1" @default.
- W3148408059 countsByYear W31484080592021 @default.
- W3148408059 crossrefType "journal-article" @default.
- W3148408059 hasAuthorship W3148408059A5003228581 @default.
- W3148408059 hasAuthorship W3148408059A5011148656 @default.
- W3148408059 hasAuthorship W3148408059A5030812391 @default.
- W3148408059 hasBestOaLocation W31484080591 @default.
- W3148408059 hasConcept C10558101 @default.
- W3148408059 hasConcept C111919701 @default.
- W3148408059 hasConcept C119599485 @default.
- W3148408059 hasConcept C120314980 @default.
- W3148408059 hasConcept C127413603 @default.
- W3148408059 hasConcept C144133560 @default.
- W3148408059 hasConcept C162853370 @default.
- W3148408059 hasConcept C187691185 @default.
- W3148408059 hasConcept C2524010 @default.
- W3148408059 hasConcept C33923547 @default.
- W3148408059 hasConcept C41008148 @default.
- W3148408059 hasConcept C79974875 @default.
- W3148408059 hasConcept C86251818 @default.
- W3148408059 hasConceptScore W3148408059C10558101 @default.
- W3148408059 hasConceptScore W3148408059C111919701 @default.
- W3148408059 hasConceptScore W3148408059C119599485 @default.
- W3148408059 hasConceptScore W3148408059C120314980 @default.
- W3148408059 hasConceptScore W3148408059C127413603 @default.
- W3148408059 hasConceptScore W3148408059C144133560 @default.
- W3148408059 hasConceptScore W3148408059C162853370 @default.
- W3148408059 hasConceptScore W3148408059C187691185 @default.
- W3148408059 hasConceptScore W3148408059C2524010 @default.
- W3148408059 hasConceptScore W3148408059C33923547 @default.
- W3148408059 hasConceptScore W3148408059C41008148 @default.
- W3148408059 hasConceptScore W3148408059C79974875 @default.
- W3148408059 hasConceptScore W3148408059C86251818 @default.
- W3148408059 hasLocation W31484080591 @default.
- W3148408059 hasOpenAccess W3148408059 @default.
- W3148408059 hasPrimaryLocation W31484080591 @default.
- W3148408059 hasRelatedWork W12397325 @default.
- W3148408059 hasRelatedWork W12895295 @default.
- W3148408059 hasRelatedWork W14324141 @default.
- W3148408059 hasRelatedWork W14648189 @default.
- W3148408059 hasRelatedWork W14682810 @default.
- W3148408059 hasRelatedWork W3523212 @default.
- W3148408059 hasRelatedWork W377067 @default.
- W3148408059 hasRelatedWork W4155817 @default.
- W3148408059 hasRelatedWork W8317513 @default.
- W3148408059 hasRelatedWork W9091480 @default.
- W3148408059 isParatext "false" @default.
- W3148408059 isRetracted "false" @default.
- W3148408059 magId "3148408059" @default.
- W3148408059 workType "article" @default.