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Location-aware MAC Scheduling in Industrial-like Environment
dc.contributor.author | Rea, Maurizio | |
dc.contributor.author | Garlisi, Domenico | |
dc.contributor.author | Cordobés de la Calle, Héctor | |
dc.contributor.author | Giustiniano, Domenico | |
dc.date.accessioned | 2021-07-13T09:35:08Z | |
dc.date.available | 2021-07-13T09:35:08Z | |
dc.date.issued | 2018-09-19 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12761/610 | |
dc.description.abstract | We consider an environment strongly affected by the presence of metallic objects, that can be considered representative of an indoor industrial environment with metal obstacles. This scenario is a very harsh environment where radio communication has notorious difficulties, as metallic objects create a strong blockage component and surfaces are highly reflective. In this environment, we investigate how to dynamically allocate MAC resources in time to static and mobile users based on context awareness extracted from a legacy WiFi positioning system. In order to address this problem, we integrate our WiFi ranging and positioning system in the WiSHFUL architecture and then define a hypothesis test to declare if the link is in line-of-sight (LOS) or non-line-of-sight (NLOS) based on angular information derived from ranging and position information. We show that context information can help increase the network throughput in the above industrial-like scenario. | |
dc.language.iso | eng | |
dc.title | Location-aware MAC Scheduling in Industrial-like Environment | en |
dc.type | conference object | |
dc.conference.date | 19–20 September 2018 | |
dc.conference.place | Faro, Portugal | |
dc.conference.title | The 9th International Conference on Broadband Communications, Networks, and Systems (BROADNETS 2018) | * |
dc.event.type | conference | |
dc.pres.type | paper | |
dc.type.hasVersion | AM | |
dc.rights.accessRights | open access | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.eprint.id | http://eprints.networks.imdea.org/id/eprint/1872 |