LEADER 02172oam 2200433K 450 001 9910793521803321 005 20190620101709.0 010 $a1-000-00878-9 010 $a1-000-00194-6 010 $a0-415-00587-6 010 $a0-429-28684-8 035 $a(CKB)4100000008339386 035 $a(MiAaPQ)EBC5781407 035 $a(OCoLC)1103320684 035 $a(OCoLC-P)1103320684 035 $a(FlBoTFG)9780429286841 035 $a(EXLCZ)994100000008339386 100 $a20190603d2019 uy 0 101 0 $aeng 135 $aurcnu---unuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA complete guide to wireless sensor networks $efrom inception to current trends /$fAnkur Dumka, Sandip Chaurasia, Arindam Wiswas, Hardwari Lal Mandoria 210 1$aBoca Raton :$cTaylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc,$d2019. 215 $a1 online resource (357 pages) 311 $a1-138-57828-2 320 $aIncludes bibliographical references. 330 $aThis book provides comprehensive coverage of the major aspects in designing, implementing, and deploying wireless sensor networks by discussing present research on WSNs and their applications in various disciplines. It familiarizes readers with the current state of WSNs and how such networks can be improved to achieve effectiveness and efficiency. It starts with a detailed introduction of wireless sensor networks and their applications and proceeds with layered architecture of WSNs. It also addresses prominent issues such as mobility, heterogeneity, fault-tolerance, intermittent connectivity, and cross layer optimization along with a number of existing solutions to stimulate future research. 606 $aWireless sensor networks 615 0$aWireless sensor networks. 676 $a006.2/5 700 $aDumka$b Ankur$f1984-$01512723 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910793521803321 996 $aA complete guide to wireless sensor networks$93746792 997 $aUNINA LEADER 03867nam 22006855 450 001 9910299595803321 005 20230504012836.0 010 $a3-319-90146-X 024 7 $a10.1007/978-3-319-90146-6 035 $a(CKB)4100000003359665 035 $a(MiAaPQ)EBC5355989 035 $a(DE-He213)978-3-319-90146-6 035 $a(PPN)226697312 035 $a(EXLCZ)994100000003359665 100 $a20180420d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnergy Optimization and Prediction in Office Buildings $eA Case Study of Office Building Design in Chile /$fby Carlos Rubio-Bellido, Alexis Pérez-Fargallo, Jesús Pulido-Arcas 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (89 pages) 225 1 $aSpringerBriefs in Energy,$x2191-5539 311 $a3-319-90145-1 320 $aIncludes bibliographical references. 327 $aIntroduction -- Research Method -- Energy Demand Analysis -- Multiple Linear Regressions -- Artificial Neural Networks -- Conclusions. 330 $aThis book explains how energy demand and energy consumption in new buildings can be predicted and how these aspects and the resulting CO2 emissions can be reduced. It is based upon the authors? extensive research into the design and energy optimization of office buildings in Chile. The authors first introduce a calculation procedure that can be used for the optimization of energy parameters in office buildings, and to predict how a changing climate may affect energy demand. The prediction of energy demand, consumption and CO2 emissions is demonstrated by solving simple equations using the example of Chilean buildings, and the findings are subsequently applied to buildings around the globe. An optimization process based on Artificial Neural Networks is discussed in detail, which predicts heating and cooling energy demands, energy consumption and CO2 emissions. Taken together, these processes will show readers how to reduce energy demand, consumption and CO2 emissions associated with office buildings in the future. Readers will gain an advanced understanding of energy use in buildings and how it can be reduced. 410 0$aSpringerBriefs in Energy,$x2191-5539 606 $aSustainable architecture 606 $aEnergy policy 606 $aEnergy policy 606 $aBuildings?Design and construction 606 $aNeural networks (Computer science) 606 $aMathematical optimization 606 $aSustainable Architecture/Green Buildings 606 $aEnergy Policy, Economics and Management 606 $aBuilding Construction and Design 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aOptimization 615 0$aSustainable architecture. 615 0$aEnergy policy. 615 0$aEnergy policy. 615 0$aBuildings?Design and construction. 615 0$aNeural networks (Computer science) 615 0$aMathematical optimization. 615 14$aSustainable Architecture/Green Buildings. 615 24$aEnergy Policy, Economics and Management. 615 24$aBuilding Construction and Design. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aOptimization. 676 $a725.23 700 $aRubio-Bellido$b Carlos$4aut$4http://id.loc.gov/vocabulary/relators/aut$0998278 702 $aPérez-Fargallo$b Alexis$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPulido-Arcas$b Jesús$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299595803321 996 $aEnergy Optimization and Prediction in Office Buildings$92289776 997 $aUNINA