4286d7c7427132c702416c16518aa4f5 pmc_24514884.pdf 45489452f88eca19b17d6739b546dd8c2de05b5b pmc_24514884.pdf b4454f25fab01f2d79139feac900bcc0c081cba30bb2521e3b0e6352b64197ec pmc_24514884.pdf Title: Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks Subject: The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices’ battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions. Keywords: human-centric wireless sensor network; energy consumption; topology control messages; message prediction; participatory sensing; opportunistic sensing Author: Roc Meseguer 1, Carlos Molina 2, Sergio F. Ochoa 3,* , Rodrigo Santos 4 Creator: Microsoft® Office Word 2007 Producer: Microsoft® Office Word 2007 CreationDate: Fri Feb 7 18:35:19 2014 CET ModDate: Fri Feb 7 10:37:57 2014 CET Tagged: yes UserProperties: no Suspects: no Form: none JavaScript: no Pages: 25 Encrypted: no Page size: 595.62 x 841.68 pts Page rot: 0 File size: 903192 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- Times New Roman,Italic TrueType WinAnsi no no no 5 0 Times New Roman TrueType WinAnsi no no no 7 0 Times New Roman,Bold TrueType WinAnsi no no no 9 0 Times New Roman,BoldItalic TrueType WinAnsi no no no 11 0 Times New Roman CID TrueType Identity-H yes no yes 13 0 Arial TrueType WinAnsi no no no 31 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2020-11-11 06:20:40 CET RepresentationInformation: pmc_24514884.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2018-01-29 05:53:23 CET Size: 903192 Format: PDF Version: 1.5 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 640 FreeObjects: 508 IncrementalUpdates: 2 DocumentCatalog: PageLayout: SinglePage PageMode: UseThumbs Language: en-US Info: Title: Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks Author: Roc Meseguer 1, Carlos Molina 2, Sergio F. Ochoa 3,* , Rodrigo Santos 4 Subject: The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices’ battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions. Keywords: human-centric wireless sensor network; energy consumption; topology control messages; message prediction; participatory sensing; opportunistic sensing Creator: Microsoft® Office Word 2007 Producer: Microsoft® Office Word 2007 CreationDate: Fri Feb 07 18:35:19 CET 2014 ModDate: Fri Feb 07 10:37:57 CET 2014 ID: 0x74d253a975a7c94ea7f514c3cd8cdb1b, 0x3205049c09e5304581ce1f735cebd82c Filters: FilterPipeline: FlateDecode FilterPipeline: DCTDecode Images: Image: NisoImageMetadata: FormatName: image/png ImageWidth: 915 ImageHeight: 547 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 793 ImageHeight: 496 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Interpolate: true Image: NisoImageMetadata: FormatName: image/jpg CompressionScheme: JPEG ImageWidth: 665 ImageHeight: 575 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Interpolate: true Image: NisoImageMetadata: FormatName: image/png ImageWidth: 613 ImageHeight: 481 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 617 ImageHeight: 474 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 629 ImageHeight: 480 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 623 ImageHeight: 480 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 615 ImageHeight: 482 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 627 ImageHeight: 470 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 619 ImageHeight: 482 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 627 ImageHeight: 470 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 621 ImageHeight: 481 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 629 ImageHeight: 480 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Image: NisoImageMetadata: FormatName: image/png ImageWidth: 622 ImageHeight: 480 ColorSpace: RGB BitsPerSample: 8 BitsPerSampleUnit: integer Filter: FlateDecode Interpolate: false Fonts: Type0: Font: BaseFont: Times New Roman Encoding: Identity-H ToUnicode: true TrueType: Font: Name: F1 BaseFont: Times New Roman,Italic FirstChar: 32 LastChar: 122 FontDescriptor: FontName: Times New Roman,Italic Flags: Nonsymbolic FontBBox: -498, -216, 1333, 694 Encoding: WinAnsiEncoding Font: Name: F2 BaseFont: Times New Roman FirstChar: 32 LastChar: 243 FontDescriptor: FontName: Times New Roman Flags: Nonsymbolic FontBBox: -568, -216, 2000, 693 Encoding: WinAnsiEncoding Font: Name: F3 BaseFont: Times New Roman,Bold FirstChar: 32 LastChar: 121 FontDescriptor: FontName: Times New Roman,Bold Flags: Nonsymbolic FontBBox: -558, -216, 2000, 677 Encoding: WinAnsiEncoding Font: Name: F4 BaseFont: Times New Roman,BoldItalic FirstChar: 32 LastChar: 115 FontDescriptor: FontName: Times New Roman,BoldItalic Flags: Nonsymbolic FontBBox: -547, -216, 1401, 677 Encoding: WinAnsiEncoding Font: Name: F6 BaseFont: Arial FirstChar: 32 LastChar: 121 FontDescriptor: FontName: Arial Flags: Nonsymbolic FontBBox: -665, -210, 2000, 728 Encoding: WinAnsiEncoding CIDFontType2: Font: BaseFont: Times New Roman CIDSystemInfo: Registry: Adobe Registry: Identity Supplement: 0 FontDescriptor: FontName: Times New Roman Flags: Nonsymbolic FontBBox: -568, -216, 2000, 693 FontFile2: true XMP: application/pdf Roc Meseguer 1, Carlos Molina 2, Sergio F. Ochoa 3,* , Rodrigo Santos 4 Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices’ battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions. human-centric wireless sensor network energy consumption topology control messages message prediction participatory sensing opportunistic sensing 2014-02-07T17:35:19Z Microsoft® Office Word 2007 2014-02-07T17:37:57+08:00 2014-02-07T17:37:57+08:00 Microsoft® Office Word 2007 human-centric wireless sensor network; energy consumption; topology control messages; message prediction; participatory sensing; opportunistic sensing uuid:4e4b2202-68a6-4165-8d4b-156463f9b6dc uuid:0dbcfa1f-9b73-439a-8c3d-40292cf5d889 Pages: Page: Sequence: 1 Page: Sequence: 2 Page: Sequence: 3 Page: Sequence: 4 Page: Sequence: 5 Page: Sequence: 6 Page: Sequence: 7 Page: Sequence: 8 Page: Sequence: 9 Page: Sequence: 10 Page: Sequence: 11 Page: Sequence: 12 Page: Sequence: 13 Page: Sequence: 14 Page: Sequence: 15 Page: Sequence: 16 Page: Sequence: 17 Page: Sequence: 18 Page: Sequence: 19 Page: Sequence: 20 Page: Sequence: 21 Page: Sequence: 22 Page: Sequence: 23 Page: Sequence: 24 Page: Sequence: 25 Checksum: 4c153cb1 Type: CRC32 Checksum: 4286d7c7427132c702416c16518aa4f5 Type: MD5 Checksum: 45489452f88eca19b17d6739b546dd8c2de05b5b Type: SHA-1