Experimental evaluation of read performance for RFID-based mobile sensor data gathering applications

Currie, Iain A.; Marina, Mahesh K.

doi:10.1145/1543137.1543155

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…A similar inference has been drawn in [9] and [11] wherein the authors suggested defining read range as the range in which a pre-defined read rate or accuracy of reading a tag can be achieved.…”

Section: Sensitivity To Deployment Environmentmentioning

confidence: 63%

“…Hence it is very important to understand the effect that the tags have on each other. The effect of proximity of the other tags to the read range and read accuracy is experimentally analyzed in [11] and [4]. The presence of multiple tag antennas in close range alters the current distribution, the radiation pattern and introduces mutual impedance.…”

Section: Effect Of Tags In Proximity To Each Othermentioning

confidence: 99%

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Performance of Passive UHF RFID Systems in Practice

Bolić

Athalye²,

2010

RFID Systems

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Section: Sensitivity To Deployment Environmentmentioning

confidence: 63%

Section: Effect Of Tags In Proximity To Each Othermentioning

confidence: 99%

Performance of Passive UHF RFID Systems in Practice

Bolić

Athalye²,

2010

RFID Systems

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“…[18][19][20][21][22] We extracted from that material all publicly available codes that fit the categories of the proposed class library. In many cases, the original code was available only in Java or only in C/C++.…”

Section: Library Of Code Snippets For the Generation Of Synthetic Promentioning

confidence: 99%

“…When the test operation is completed, the metrics are collected and stored (lines [17][18][19][20][21][22][23][24][25][26][27].…”

Section: Figurementioning

confidence: 99%

EPE‐Mobile—A framework for early performance estimation of mobile applications

2017

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SummaryConsidering the constrained resources of mobile devices, a thorough performance evaluation of a mobile application is crucial. However, performance evaluation in the mobile domain is still a manual and time-consuming task.The diversity of mobile devices only increases the complexity of this task. We propose EPE-Mobile, a framework to automate early performance estimation in mobile applications. It is composed of a configurable library of basic operations and an engine that automatically creates a synthetic program based on the specification of a new app. The synthetic program that EPE-Mobile generates provides feedback for mobile developers at the first design stages and before the actual implementation of a new application. The fast evaluation can also guide developers in optimizing their applications or in choosing devices with the best trade-off between cost and performance to run a given application.Finally, developers can reuse the data collection infrastructure of the framework to collect performance data during all development stages. We validate the proposed framework using 4 applications from the Android Play Store. Based on their specifications, 4 synthetic programs were generated and executed on different devices. We compared the results to those obtained from the execution of the actual applications in the same devices. Experimental results show that it is possible to create synthetic applications with similar behavior to that of real applications and, thus, classify devices based on the actual application needs. The framework uses aspect-oriented programming to collect the metrics of interest. This approach provides increased modularity and separation of concerns, thus facilitating the improvement of the framework itself, by adding other metrics or basic operations.

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“…It should be noted that RSSI values usually range from 0 dBm to -110 dBm as per manufacturers' specifications. Effects of different tag and antennae orientations on radio signals were also assessed in various studies, which mostly focused on the orientations of passive tags (Currie and Marina, 2008;Huang et al, 2008;Mello et al, 2008) and an antenna independently. Clarke et al (2006) evaluated the read rate of passive tags with different orientations and the materials that they were attached to.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the variability of RSSI values for active RFID-based indoor applications

Çalış¹,

BECERİK-GERBER²,

GÖKTEPE³

et al. 2013

Turkish J Eng Env Sci

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The results indicate that different system parameters caused variations in RSSI values as well as the detectability rates.Nevertheless, different test beds changed the influence of each parameter on the RSSI values. It is concluded that testing environment is a prominent parameter that affects the results and fluctuations in the RSSI values, and that the detectability rates cannot be attributed to any specific parameter that has been included in this study.

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Experimental evaluation of read performance for RFID-based mobile sensor data gathering applications

Cited by 13 publications

References 11 publications

Performance of Passive UHF RFID Systems in Practice

Performance of Passive UHF RFID Systems in Practice

EPE‐Mobile—A framework for early performance estimation of mobile applications

Analysis of the variability of RSSI values for active RFID-based indoor applications

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