- -

Improving energy-efficiency with a green cognitive algorithm to overcome weather's impact in 2.4 GHz wireless networks

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Improving energy-efficiency with a green cognitive algorithm to overcome weather's impact in 2.4 GHz wireless networks

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Bri et al paper_r ...
Tamaño: 1.455Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: -Bri;García;Ramos ...
Tamaño: 2.054Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Bri Molinero, Diana es_ES
dc.contributor.author García Pineda, Miguel es_ES
dc.contributor.author Ramos Pascual, Francisco es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2016-05-10T08:06:23Z
dc.date.available 2016-05-10T08:06:23Z
dc.date.issued 2015-10
dc.identifier.issn 1383-469X
dc.identifier.uri http://hdl.handle.net/10251/63831
dc.description.abstract The necessity of energy-efficient systems in order to protect our environment, cope with global warming, and facilitate sustainable development is paramount for the researching world because the survival of the planet is at stake. Thus, optimizing the energy efficiency of wireless communications not only reduces environmental impact, but also cuts overall network costs and helps make communication more practical and affordable in a pervasive setting. This paper is focused on a solution to enhance the energy efficiency in outdoor wireless local area networks using the standard IEEE 802.11b/g. So, from a previous study about the weather s impact on the number of control frame errors and retransmissions, we propose a green cognitive algorithm that adapts wireless transmissions to the channel conditions caused by the weather. The goal is to reduce retransmissions and control errors in order to save energy and to enhance network performance. Our proposal is based on a mathematical analysis in which we see how the frame error rate is related to the power consumption according to the modulation scheme and data rate used by transmitters. Finally, several simulations show that the green cognitive algorithm presented in this paper involves significant energy savings for outdoors WLANs. es_ES
dc.description.sponsorship This work has been supported by the Vice-Rectorate for Research, Innovation and Transfer of the Universitat Politecnica de Valencia through the programme of International Campus of Excellence funded by Ministry of Education of Spain, and through the programme of Predoctoral Research Grants (FPI-UPV). The authors would like to thank the Information and Communications Systems Office (ASIC), Borja Opticos Enterprise and Azimut Electronics Company for their collaboration and support. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Mobile Networks and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Energy efficiency es_ES
dc.subject MAC layer es_ES
dc.subject Green cognitive algorithm es_ES
dc.subject Outdoor WLAN es_ES
dc.subject IEEE 802.11 es_ES
dc.subject Weather’s Impact es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Improving energy-efficiency with a green cognitive algorithm to overcome weather's impact in 2.4 GHz wireless networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11036-015-0602-7
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres es_ES
dc.description.bibliographicCitation Bri Molinero, D.; García Pineda, M.; Ramos Pascual, F.; Lloret, J. (2015). Improving energy-efficiency with a green cognitive algorithm to overcome weather's impact in 2.4 GHz wireless networks. Mobile Networks and Applications. 20(5):673-691. doi:10.1007/s11036-015-0602-7 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s11036-015-0602-7 es_ES
dc.description.upvformatpinicio 673 es_ES
dc.description.upvformatpfin 691 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 308202 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Educación es_ES
dc.description.references Ad-hoc Advisory Group Report (2008) ICT for energy efficiency. DG-Information Society and Media, European Commission, Brussels es_ES
dc.description.references Mills MP (2013) The cloud begins with coal, big data, big networks, big infraestructure and big power. Digital Power Group es_ES
dc.description.references Rodrigues J (2013) Green communications and networking. Netw Protocol Algorithm 5(1):37–40 es_ES
dc.description.references Serrano P, de la Oliva A, Patras P, Mancuso V, Banchs A (2012) Greening wireless communications: status and future directions. Comput Commun 35:1651–1661 es_ES
dc.description.references Tsao SL, Huang CH (2011) A survey of energy efficient MAC protocols for IEEE 802.11 WLAN. Comput Commun 34(1):54–67 es_ES
dc.description.references Lloret J, Sendra S, Coll H, Miguel Garcia M (2009) Saving energy in wireless local area sensor networks. Comput J 53(10):1658–1673 es_ES
dc.description.references Serrano P, Garcia Saavedra A, Bianchi G, Banchs A, Azcorra A (2014) Per-frame energy consumption in 802.11 devices and its implication on modeling and design. IEEE/ACM Transactions on networking es_ES
dc.description.references Sendra S, Lloret J, Garcia M, Toledo JF (2011) Power saving and energy optimization techniques for Wireless Sensor Networks. J Commun 6(6):439–459 es_ES
dc.description.references IEEE (2012) IEEE Std 802.11™-2012 (Revision of IEEE Std 802.11-2007) part 11: wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. IEEE Computer Society, New York es_ES
dc.description.references Khanna VK, Gupta HM, Maheshwari S (2008) A high throughput and low power ad-hoc wireless LAN protocol. Wirel Netw 14(1):1–16 es_ES
dc.description.references Bri D, Ramos P, Lloret J, Garcia M (2012) The influence of meteorological variables on the performance of outdoor wireless local area networks. In: IEEE International Conference on Communications. Ottawa, Canada, 2012 es_ES
dc.description.references Bri D, Garcia M, Lloret J, Misic J (2014) Measuring the weather’s impact on MAC layer over 2.4 GHz outdoor radio links. Measurement 61:221–233 es_ES
dc.description.references Bri D, Fernandez-Diego M, Garcia M, Ramos F, Lloret J (2012) How the weather impacts on the performance of an outdoor WLAN. IEEE Commun Lett 16(8):1184–1187 es_ES
dc.description.references Lombardo A, Panarello C, Schembra G (2013) EE-ARQ: a green ARQ-based algorithm for the transmission of video streams on noise wireless channels. Netw Protocol Algorithm 5(1):43–70 es_ES
dc.description.references Wang L, Manner J (2010) Energy Consumption Analysis of WLAN, 2G and 3G interfaces. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing (GREENCOM-CPSCOM ‘10). Washington, DC, USA es_ES
dc.description.references Noda C, Prabh S, Alves M, Voigt T (2013) On packet size and error correction optimisations in low-power wireless networks. In: IEEE International Conference on Sensing, Communications and Networking (SECON). New Orleans, USA es_ES
dc.description.references Nasaruddin, Andriani M, Melinda, Irhamsyah M (2013) Analysis of energy efficiency for Wi-Fi 802.11b. In: IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Yogyakarta, Indonesia es_ES
dc.description.references Gomez K, Riggio R, Rasheed T, Granelli F (2011) Analysing the energy consumption behaviour of WiFi networks. In: Online Conference onGreen Communications (GreenCom) es_ES
dc.description.references Sweedy AM, Semeia AI, Sayed SY, Konber AH (2010) The effect of frame length, fragmentation and RTS/CTS mechanism on IEEE 802.11 MAC performance. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA). Cairo, Egypt, 2010. es_ES
dc.description.references Tauber M, Bhatti SN, Yu Y (2011) Application level energy and performance measurements in a wireless LAN. In: IEEE/ACM International Conference on Green Computing and Communications (GreenCom). Chengdu, China es_ES
dc.description.references Krishnan M, Haghani E, Zakhor A (2011) Packet length adaptation in WLANs with hidden nodes and time-varying channels. In: Global Telecommunications Conference (GLOBECOM 2011). Houston, Texas, USA es_ES
dc.description.references Song W, Krishnan MN, Zakhor A (2009) Adaptive packetization for error-prone transmission over 802.11 WLANs with Hidden Terminals. In: the 11th international workshop on multimedia signal processing (MMSP’09), Rio de Janeiro, Brazil es_ES
dc.description.references Naydenov GA, Stoyanov PS (2007) Bit error period determination and optimal frame length prediction for a noisy communication channel. AU J Technol 11(1):7–13 es_ES
dc.description.references Balaji B, Tamma B, Manoj B (2010) A novel power saving strategy for greening IEEE 802.11 based wireless networks. In: Global Telecommunications Conference (GLOBECOM 2010). Miami, USA es_ES
dc.description.references Zhou J, Jacobsson M, Niemegeers I (2010) Link quality-based transmission power adaptation for reduction of energy consumption and interference. EURASIP J Wirel Commun Netw, vol. open access, pp. 1–17 es_ES
dc.description.references Le B, Rondeau TW, Bostian CW (2007) Cognitive radio realities. Wirel Commun Mob Comput 7(9):1037–1048 es_ES
dc.description.references T. W. G. f. W. Standards “IEEE 802.11 Wireless Local Area Networks,” [Online]. Available: http://grouper.ieee.org/groups/802/11/. Accessed Aug 2014 es_ES
dc.description.references Vassis D, Kormentzas G, Rouskas A, Maglogiannis I (2005) The IEEE 802.11 g standard for high data. IEEE Netw 19(3):21–26 es_ES
dc.description.references Scalia L, Widmer J, Aad I (2010) On the side effects of packet detection sensitivity in IEEE 802.11 interference management. In: IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), Montreal, Canada es_ES
dc.description.references Boano C, Brown J, He Z, Roedig U, Voigt T (2010) Low-power radio communication in industrial outdoor deployments: the impact of weather conditions and ATEX-compliance. Lect Notes Inst Comput Sci Soc Inform Telecommun Eng 29:159–176 es_ES
dc.description.references Cakaj S (2009) Rain attenuation impact on performance of satellite ground stations for Low Earth Orbiting (LEO) Satellites in Europe. Int J Commun Netw Syst Sci 6:480–485 es_ES
dc.description.references Luccini M (2013) Joint use of on-board reconfigurable antenna pattern and adaptive coding and modulation in satellite communications at high frequency bands, Ph.D. dissertation. Dept. Electrical and Computer Engineering, University of Western Ontario es_ES
dc.description.references Crane RK (2003) Propagation handbook for wireless communication system design. CRC Press es_ES
dc.description.references ITU (2013) Recommendation ITU-R P.676-10 attenuation by atmospheric gases. ITU, Geneva, P Series es_ES
dc.description.references ITU (2013) Recommendation ITU-R P.840-6 attenuation due to clouds and fog. ITU, Geneva, P Series es_ES
dc.description.references ITU (2005) Recommendation ITU-R P.838-3 specific attenuation model for rain for use in prediction methods. ITU, Geneva, P Series es_ES
dc.description.references Bri D, Sendra S, Coll H, Lloret J (2010) How the atmospheric variables affect to the WLAN datalink layer parameters. In: The Sixth Advanced International Conference on Telecommunications. Barcelona, Spain es_ES
dc.description.references Chok NS (2010) Pearson’s versus Spearman’s and Kendall’s correlation, Thesis. University of Pittsburgh es_ES
dc.description.references Sheskin DJ (2003) Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition, CRC Press, 2003 es_ES
dc.description.references IBM “SPSS Statistical Software,” [Online]. Available: http://www-01.ibm.com/software/es/analytics/spss/ . Accessed Feb 2015 es_ES
dc.description.references Thomas RW, DaSilva LA, MacKenzie AB (2005) Cognitive networks. In: 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore es_ES
dc.description.references Thomas RW, Friend DH, DaSilva LA, MacKenzie AB (2006) Cognitive networks: adaptation and learning to achieve end-to-end performance objectives. IEEE Commun Mag 44(12):51–57 es_ES
dc.description.references Gür G, Alagöz F (2011) Green wireless communications via cognitive dimension: an overview. IEEE Netw 25(2):50–56 es_ES
dc.description.references IEEE Std 802.15.2 (2003) Coexistence of wireless personal area networks with other wireless devices operating inunlicensed frequency bands. IEEE es_ES
dc.description.references Goldsmith A (2005) Wireless communications. Cambridge University Press, Cambridge es_ES
dc.description.references IEEE Std. 802.11a (1999) Supplement to IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—specific requirements. Part 11: wireless LAN Medium Access Control (MAC) and Physical Layer (PHY). IEEE es_ES
dc.description.references Toorisaka W, Hasegawa G, Murata M (2012) Power consumption analysis of data transmission in IEEE 802.11 multi-hop networks. de ICNS 2012, The Eighth International Conference on Networking and Services. St. Maarten, Netherlands Antilles es_ES
dc.description.references Feeney LM, Nilsson M (2001) Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. de IEEE INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings, Anchorage, Alaska USA es_ES
dc.description.references Texas I CC3000 I.E. 802.11b/g Solution Module. CC3000 I.E. 802.11b/g solution module, [Online]. Available: http://www.ti.com/product/CC3000/technicaldocuments es_ES
dc.description.references Fortuna C, Mohorcic M (2009) Trends in the development of communication networks: cognitive networks. Comput Netw 53(9):1354–1376 es_ES
dc.description.references Riverbed Riverbed Modeler Wireless Suite. [En línea]. Available: http://www.riverbed.com/products/performance-management-control/network-performance-management/network-simulation.html es_ES
dc.description.references Patil KP, Barge S, Skouby KE, Prasad R (2014) Spectrum occupancy information in support of adaptive spectrum sensing for cognitive radio. Netw Protocol Algorithm 6(1):76–86 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem