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dc.contributor.authorChang, Tung-Chunen_US
dc.contributor.authorLin, Chi-Hanen_US
dc.contributor.authorLin, Kate Ching-Juen_US
dc.contributor.authorChen, Wen-Tsuenen_US
dc.date.accessioned2019-04-02T06:00:26Z-
dc.date.available2019-04-02T06:00:26Z-
dc.date.issued2019-03-01en_US
dc.identifier.issn1536-1233en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TMC.2018.2840692en_US
dc.identifier.urihttp://hdl.handle.net/11536/148847-
dc.description.abstractTraditional IEEE 802.11 network is designed for the use of small scale local wireless networks. However, the emergence of the Internet of Things (loT) has changed the scene of wireless communications. Thus, recently, the IEEE task group ah (TGah) has been dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts a grouping-based MAC protocol to reduce the contention overhead for each group of devices. However, most existing designs simply randomly partition devices into groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to heterogeneity in traffic demands of devices, and propose a traffic-aware grouping algorithm to improve channel utilization. Since channel utilization of a group closely depends on the collision probability, we further derive a regression-based analytical model to estimate the contention success probability with consideration of sensors' heterogeneous traffic demands. The evaluation via NS-3 simulations shows that the proposed regression-based model is quite accurate even when clients have diverse traffic demands, and our traffic-aware grouping outperforms other baseline approaches, especially when the network is nearly saturated.en_US
dc.language.isoen_USen_US
dc.subject802.11ahen_US
dc.subjectgrouping-based MAC protocolen_US
dc.subjectIoT networksen_US
dc.titleTraffic-Aware Sensor Grouping for IEEE 802.11ah Networks: Regression Based Analysis and Designen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TMC.2018.2840692en_US
dc.identifier.journalIEEE TRANSACTIONS ON MOBILE COMPUTINGen_US
dc.citation.volume18en_US
dc.citation.spage674en_US
dc.citation.epage687en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000458185200014en_US
dc.citation.woscount0en_US
Appears in Collections:Articles