FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309721259> ?p ?o ?g. }

• W4309721259 endingPage "9054" @default.
• W4309721259 startingPage "9054" @default.
• W4309721259 abstract "Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE's) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique's accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path-Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path-Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints." @default.
• W4309721259 created "2022-11-29" @default.
• W4309721259 creator A5037835022 @default.
• W4309721259 creator A5040810553 @default.
• W4309721259 creator A5079797573 @default.
• W4309721259 creator A5088606308 @default.
• W4309721259 creator A5091131550 @default.
• W4309721259 date "2022-11-22" @default.
• W4309721259 modified "2023-09-25" @default.
• W4309721259 title "RSSI Fingerprint Height Based Empirical Model Prediction for Smart Indoor Localization" @default.
• W4309721259 cites W1608826770 @default.
• W4309721259 cites W1758464101 @default.
• W4309721259 cites W1981831056 @default.
• W4309721259 cites W1998457487 @default.
• W4309721259 cites W2011502582 @default.
• W4309721259 cites W2012165388 @default.
• W4309721259 cites W2031701896 @default.
• W4309721259 cites W2039794991 @default.
• W4309721259 cites W2042556563 @default.
• W4309721259 cites W2047661604 @default.
• W4309721259 cites W2051376734 @default.
• W4309721259 cites W2058578387 @default.
• W4309721259 cites W2063283661 @default.
• W4309721259 cites W2066631219 @default.
• W4309721259 cites W2099208041 @default.
• W4309721259 cites W2099843660 @default.
• W4309721259 cites W2100989187 @default.
• W4309721259 cites W2111571624 @default.
• W4309721259 cites W2113292116 @default.
• W4309721259 cites W2117206394 @default.
• W4309721259 cites W2119743905 @default.
• W4309721259 cites W2120234055 @default.
• W4309721259 cites W2130403046 @default.
• W4309721259 cites W2130945928 @default.
• W4309721259 cites W2143941749 @default.
• W4309721259 cites W2146270659 @default.
• W4309721259 cites W2151673354 @default.
• W4309721259 cites W2295753081 @default.
• W4309721259 cites W2524355457 @default.
• W4309721259 cites W2562522049 @default.
• W4309721259 cites W2760313038 @default.
• W4309721259 cites W2765929558 @default.
• W4309721259 cites W2767610279 @default.
• W4309721259 cites W2772160782 @default.
• W4309721259 cites W2793005227 @default.
• W4309721259 cites W2794105321 @default.
• W4309721259 cites W2879970644 @default.
• W4309721259 cites W2891206451 @default.
• W4309721259 cites W2899052090 @default.
• W4309721259 cites W2942082696 @default.
• W4309721259 cites W2964060007 @default.
• W4309721259 cites W2968810373 @default.
• W4309721259 cites W2990590023 @default.
• W4309721259 cites W3045926382 @default.
• W4309721259 cites W3048460567 @default.
• W4309721259 cites W3048655512 @default.
• W4309721259 cites W3126034216 @default.
• W4309721259 doi "https://doi.org/10.3390/s22239054" @default.
• W4309721259 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36501756" @default.
• W4309721259 hasPublicationYear "2022" @default.
• W4309721259 type Work @default.
• W4309721259 citedByCount "2" @default.
• W4309721259 countsByYear W43097212592023 @default.
• W4309721259 crossrefType "journal-article" @default.
• W4309721259 hasAuthorship W4309721259A5037835022 @default.
• W4309721259 hasAuthorship W4309721259A5040810553 @default.
• W4309721259 hasAuthorship W4309721259A5079797573 @default.
• W4309721259 hasAuthorship W4309721259A5088606308 @default.
• W4309721259 hasAuthorship W4309721259A5091131550 @default.
• W4309721259 hasBestOaLocation W43097212591 @default.
• W4309721259 hasConcept C105339364 @default.
• W4309721259 hasConcept C111919701 @default.
• W4309721259 hasConcept C115051666 @default.
• W4309721259 hasConcept C127162648 @default.
• W4309721259 hasConcept C127413603 @default.
• W4309721259 hasConcept C154945302 @default.
• W4309721259 hasConcept C194273485 @default.
• W4309721259 hasConcept C20832178 @default.
• W4309721259 hasConcept C2777826928 @default.
• W4309721259 hasConcept C2778913798 @default.
• W4309721259 hasConcept C41008148 @default.
• W4309721259 hasConcept C44154836 @default.
• W4309721259 hasConcept C47798520 @default.
• W4309721259 hasConcept C555944384 @default.
• W4309721259 hasConcept C62611344 @default.
• W4309721259 hasConcept C66938386 @default.
• W4309721259 hasConcept C76155785 @default.
• W4309721259 hasConcept C7720470 @default.
• W4309721259 hasConcept C79403827 @default.
• W4309721259 hasConceptScore W4309721259C105339364 @default.
• W4309721259 hasConceptScore W4309721259C111919701 @default.
• W4309721259 hasConceptScore W4309721259C115051666 @default.
• W4309721259 hasConceptScore W4309721259C127162648 @default.
• W4309721259 hasConceptScore W4309721259C127413603 @default.
• W4309721259 hasConceptScore W4309721259C154945302 @default.
• W4309721259 hasConceptScore W4309721259C194273485 @default.
• W4309721259 hasConceptScore W4309721259C20832178 @default.
• W4309721259 hasConceptScore W4309721259C2777826928 @default.

• next