Matches in SemOpenAlex for { <https://semopenalex.org/work/W3163132017> ?p ?o ?g. }
Showing items 1 to 98 of
98
with 100 items per page.
- W3163132017 endingPage "1299" @default.
- W3163132017 startingPage "1289" @default.
- W3163132017 abstract "While it is well understood that edge computing can significantly facilitate IoT-related applications by deploying edge servers close to IoT devices, it also faces many challenges with numerous IoT devices connected and interacted. One of the most important issues is how to efficiently deploy edge servers under a certain budget with the explosive growth of data scale and user base. Existing studies for edge server placement fail to consider user’s query preferences since individual users may be interested in events in particular regions and are keen to receive up-to-date data streams that originate in regions of interest. In this article, we present a preference-aware edge server placement approach that offers better workload distribution in terms of both minimizing query latency and balancing the load of edge servers. To achieve this, we formulate edge server placement with multiobjective optimization as a <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${p}$ </tex-math></inline-formula> -center problem and design two progressive approaches. We first propose quadratic integer programming (QIP) for small-scale data sets. Since the <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>${p}$ </tex-math></inline-formula> -center problem is an NP-hard problem, we thus propose a heuristic algorithm named TAKG (TAbu search with <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$K$ </tex-math></inline-formula> -means and Genetic algorithm) for large-scale data sets. To evaluate the utility of the proposed models, we have conducted a comprehensive evaluation on a large data set that is collected by more than 1900 IoT devices during 30 days. Experimental results indicate our approaches outperform all baselines significantly in terms of both query latency and load balancing." @default.
- W3163132017 created "2021-05-24" @default.
- W3163132017 creator A5020156280 @default.
- W3163132017 creator A5039318240 @default.
- W3163132017 creator A5040337525 @default.
- W3163132017 creator A5056140843 @default.
- W3163132017 creator A5071754382 @default.
- W3163132017 creator A5086517405 @default.
- W3163132017 date "2022-01-15" @default.
- W3163132017 modified "2023-10-17" @default.
- W3163132017 title "Preference-Aware Edge Server Placement in the Internet of Things" @default.
- W3163132017 cites W1971496656 @default.
- W3163132017 cites W2172214027 @default.
- W3163132017 cites W2293090515 @default.
- W3163132017 cites W2416799949 @default.
- W3163132017 cites W2514431540 @default.
- W3163132017 cites W2626489784 @default.
- W3163132017 cites W2757948226 @default.
- W3163132017 cites W2782886739 @default.
- W3163132017 cites W2792846363 @default.
- W3163132017 cites W2804504693 @default.
- W3163132017 cites W2809740924 @default.
- W3163132017 cites W2894009982 @default.
- W3163132017 cites W2895945505 @default.
- W3163132017 cites W2906068868 @default.
- W3163132017 cites W2916014425 @default.
- W3163132017 cites W2923257088 @default.
- W3163132017 cites W2972270478 @default.
- W3163132017 cites W2982179363 @default.
- W3163132017 cites W2996413393 @default.
- W3163132017 cites W2996782590 @default.
- W3163132017 cites W2999867301 @default.
- W3163132017 cites W3003703488 @default.
- W3163132017 cites W3003879944 @default.
- W3163132017 cites W3008177173 @default.
- W3163132017 cites W3009051479 @default.
- W3163132017 cites W3035221273 @default.
- W3163132017 cites W3036545765 @default.
- W3163132017 cites W3036790738 @default.
- W3163132017 cites W3040873268 @default.
- W3163132017 cites W3043084201 @default.
- W3163132017 cites W3047040155 @default.
- W3163132017 cites W3047147270 @default.
- W3163132017 cites W3047207517 @default.
- W3163132017 cites W3080671748 @default.
- W3163132017 cites W3090069470 @default.
- W3163132017 cites W3124043402 @default.
- W3163132017 doi "https://doi.org/10.1109/jiot.2021.3079328" @default.
- W3163132017 hasPublicationYear "2022" @default.
- W3163132017 type Work @default.
- W3163132017 sameAs 3163132017 @default.
- W3163132017 citedByCount "9" @default.
- W3163132017 countsByYear W31631320172021 @default.
- W3163132017 countsByYear W31631320172022 @default.
- W3163132017 countsByYear W31631320172023 @default.
- W3163132017 crossrefType "journal-article" @default.
- W3163132017 hasAuthorship W3163132017A5020156280 @default.
- W3163132017 hasAuthorship W3163132017A5039318240 @default.
- W3163132017 hasAuthorship W3163132017A5040337525 @default.
- W3163132017 hasAuthorship W3163132017A5056140843 @default.
- W3163132017 hasAuthorship W3163132017A5071754382 @default.
- W3163132017 hasAuthorship W3163132017A5086517405 @default.
- W3163132017 hasConcept C154945302 @default.
- W3163132017 hasConcept C162307627 @default.
- W3163132017 hasConcept C31258907 @default.
- W3163132017 hasConcept C41008148 @default.
- W3163132017 hasConcept C80444323 @default.
- W3163132017 hasConcept C93996380 @default.
- W3163132017 hasConceptScore W3163132017C154945302 @default.
- W3163132017 hasConceptScore W3163132017C162307627 @default.
- W3163132017 hasConceptScore W3163132017C31258907 @default.
- W3163132017 hasConceptScore W3163132017C41008148 @default.
- W3163132017 hasConceptScore W3163132017C80444323 @default.
- W3163132017 hasConceptScore W3163132017C93996380 @default.
- W3163132017 hasFunder F4320321001 @default.
- W3163132017 hasFunder F4320322162 @default.
- W3163132017 hasFunder F4320338464 @default.
- W3163132017 hasIssue "2" @default.
- W3163132017 hasLocation W31631320171 @default.
- W3163132017 hasOpenAccess W3163132017 @default.
- W3163132017 hasPrimaryLocation W31631320171 @default.
- W3163132017 hasRelatedWork W1583717361 @default.
- W3163132017 hasRelatedWork W1751890932 @default.
- W3163132017 hasRelatedWork W2118449739 @default.
- W3163132017 hasRelatedWork W2157390616 @default.
- W3163132017 hasRelatedWork W2350199049 @default.
- W3163132017 hasRelatedWork W2380898862 @default.
- W3163132017 hasRelatedWork W2989221764 @default.
- W3163132017 hasRelatedWork W3006227554 @default.
- W3163132017 hasRelatedWork W4310682883 @default.
- W3163132017 hasRelatedWork W4364377380 @default.
- W3163132017 hasVolume "9" @default.
- W3163132017 isParatext "false" @default.
- W3163132017 isRetracted "false" @default.
- W3163132017 magId "3163132017" @default.
- W3163132017 workType "article" @default.