FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285187732> ?p ?o ?g. }

• W4285187732 endingPage "18" @default.
• W4285187732 startingPage "1" @default.
• W4285187732 abstract "To monitor environments, Wireless Sensor Networks (WSNs) are used for collecting data in divers domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, we adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. Our approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. Our analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, we use SHAP method to explain predictions." @default.
• W4285187732 created "2022-07-14" @default.
• W4285187732 creator A5002813349 @default.
• W4285187732 creator A5035000065 @default.
• W4285187732 creator A5050928604 @default.
• W4285187732 date "2022-07-08" @default.
• W4285187732 modified "2023-09-26" @default.
• W4285187732 title "Using Machine Learning in WSNs for Performance Prediction MAC Layer" @default.
• W4285187732 cites W1597355471 @default.
• W4285187732 cites W2012207967 @default.
• W4285187732 cites W2049491135 @default.
• W4285187732 cites W2124101897 @default.
• W4285187732 cites W2138862945 @default.
• W4285187732 cites W2164012210 @default.
• W4285187732 cites W2395187259 @default.
• W4285187732 cites W2462492795 @default.
• W4285187732 cites W2501123166 @default.
• W4285187732 cites W2598645336 @default.
• W4285187732 cites W2795411881 @default.
• W4285187732 cites W2916641566 @default.
• W4285187732 cites W2920757090 @default.
• W4285187732 cites W2923913142 @default.
• W4285187732 cites W2945744693 @default.
• W4285187732 cites W2963092481 @default.
• W4285187732 cites W2973049920 @default.
• W4285187732 cites W2982435754 @default.
• W4285187732 cites W2996705655 @default.
• W4285187732 cites W3003455629 @default.
• W4285187732 cites W3005086430 @default.
• W4285187732 cites W3014660692 @default.
• W4285187732 cites W3022298391 @default.
• W4285187732 cites W3032989751 @default.
• W4285187732 cites W3035353528 @default.
• W4285187732 cites W3043435488 @default.
• W4285187732 cites W3095752837 @default.
• W4285187732 cites W3097363423 @default.
• W4285187732 cites W3100857292 @default.
• W4285187732 cites W3103168224 @default.
• W4285187732 cites W3108141335 @default.
• W4285187732 cites W3109310558 @default.
• W4285187732 cites W3112382556 @default.
• W4285187732 doi "https://doi.org/10.4018/ijisp.303667" @default.
• W4285187732 hasPublicationYear "2022" @default.
• W4285187732 type Work @default.
• W4285187732 citedByCount "0" @default.
• W4285187732 crossrefType "journal-article" @default.
• W4285187732 hasAuthorship W4285187732A5002813349 @default.
• W4285187732 hasAuthorship W4285187732A5035000065 @default.
• W4285187732 hasAuthorship W4285187732A5050928604 @default.
• W4285187732 hasConcept C119857082 @default.
• W4285187732 hasConcept C142724271 @default.
• W4285187732 hasConcept C158379750 @default.
• W4285187732 hasConcept C178790620 @default.
• W4285187732 hasConcept C185592680 @default.
• W4285187732 hasConcept C203274722 @default.
• W4285187732 hasConcept C204787440 @default.
• W4285187732 hasConcept C24590314 @default.
• W4285187732 hasConcept C2779227376 @default.
• W4285187732 hasConcept C2780385302 @default.
• W4285187732 hasConcept C31258907 @default.
• W4285187732 hasConcept C41008148 @default.
• W4285187732 hasConcept C513196756 @default.
• W4285187732 hasConcept C527821871 @default.
• W4285187732 hasConcept C555944384 @default.
• W4285187732 hasConcept C71924100 @default.
• W4285187732 hasConcept C76155785 @default.
• W4285187732 hasConceptScore W4285187732C119857082 @default.
• W4285187732 hasConceptScore W4285187732C142724271 @default.
• W4285187732 hasConceptScore W4285187732C158379750 @default.
• W4285187732 hasConceptScore W4285187732C178790620 @default.
• W4285187732 hasConceptScore W4285187732C185592680 @default.
• W4285187732 hasConceptScore W4285187732C203274722 @default.
• W4285187732 hasConceptScore W4285187732C204787440 @default.
• W4285187732 hasConceptScore W4285187732C24590314 @default.
• W4285187732 hasConceptScore W4285187732C2779227376 @default.
• W4285187732 hasConceptScore W4285187732C2780385302 @default.
• W4285187732 hasConceptScore W4285187732C31258907 @default.
• W4285187732 hasConceptScore W4285187732C41008148 @default.
• W4285187732 hasConceptScore W4285187732C513196756 @default.
• W4285187732 hasConceptScore W4285187732C527821871 @default.
• W4285187732 hasConceptScore W4285187732C555944384 @default.
• W4285187732 hasConceptScore W4285187732C71924100 @default.
• W4285187732 hasConceptScore W4285187732C76155785 @default.
• W4285187732 hasIssue "1" @default.
• W4285187732 hasLocation W42851877321 @default.
• W4285187732 hasOpenAccess W4285187732 @default.
• W4285187732 hasPrimaryLocation W42851877321 @default.
• W4285187732 hasRelatedWork W1995421447 @default.
• W4285187732 hasRelatedWork W2034825888 @default.
• W4285187732 hasRelatedWork W2077688278 @default.
• W4285187732 hasRelatedWork W2148526127 @default.
• W4285187732 hasRelatedWork W2160853270 @default.
• W4285187732 hasRelatedWork W2356969556 @default.
• W4285187732 hasRelatedWork W2373804358 @default.
• W4285187732 hasRelatedWork W2562070253 @default.
• W4285187732 hasRelatedWork W4243925910 @default.
• W4285187732 hasRelatedWork W4320027696 @default.
• W4285187732 hasVolume "16" @default.

• next