Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes
Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual...
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| Veröffentlicht in: | IEEE transactions on wireless communications 2009-02, Vol.8 (2), p.638-643 |
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| creator | Ahmed, M.F.A. Vorobyov, S.A. |
| description | Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes. |
| doi_str_mv | 10.1109/TWC.2009.071339 |
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The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.</description><identifier>ISSN: 1536-1276</identifier><identifier>EISSN: 1558-2248</identifier><identifier>DOI: 10.1109/TWC.2009.071339</identifier><identifier>CODEN: ITWCAX</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Ad hoc networks ; Antennas ; antennas and propagation ; Applied sciences ; Array signal processing ; Arrays ; Beamforming ; Collaboration ; Detection, estimation, filtering, equalization, prediction ; Exact sciences and technology ; Gaussian ; Information, signal and communications theory ; Networks ; Probability density function ; Probability density functions ; Radiocommunications ; Random variables ; resource allocation and interference management ; Resource management ; Sensor arrays ; sensor networks ; Sensor phenomena and characterization ; Sensors ; Services and terminals of telecommunications ; Sidelobes ; Signal and communications theory ; Signal, noise ; Spatial distribution ; Studies ; Systems, networks and services of telecommunications ; Telecommunications ; Telecommunications and information theory ; Telemetry. Remote supervision. Telewarning. Remote control ; Transmission and modulation (techniques and equipments) ; Uncertainty ; Wireless sensor networks</subject><ispartof>IEEE transactions on wireless communications, 2009-02, Vol.8 (2), p.638-643</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.</description><subject>Ad hoc networks</subject><subject>Antennas</subject><subject>antennas and propagation</subject><subject>Applied sciences</subject><subject>Array signal processing</subject><subject>Arrays</subject><subject>Beamforming</subject><subject>Collaboration</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Gaussian</subject><subject>Information, signal and communications theory</subject><subject>Networks</subject><subject>Probability density function</subject><subject>Probability density functions</subject><subject>Radiocommunications</subject><subject>Random variables</subject><subject>resource allocation and interference management</subject><subject>Resource management</subject><subject>Sensor arrays</subject><subject>sensor networks</subject><subject>Sensor phenomena and characterization</subject><subject>Sensors</subject><subject>Services and terminals of telecommunications</subject><subject>Sidelobes</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Spatial distribution</subject><subject>Studies</subject><subject>Systems, networks and services of telecommunications</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Telemetry. Remote supervision. Telewarning. 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Remote supervision. Telewarning. Remote control</topic><topic>Transmission and modulation (techniques and equipments)</topic><topic>Uncertainty</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ahmed, M.F.A.</creatorcontrib><creatorcontrib>Vorobyov, S.A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on wireless communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ahmed, M.F.A.</au><au>Vorobyov, S.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes</atitle><jtitle>IEEE transactions on wireless communications</jtitle><stitle>TWC</stitle><date>2009-02-01</date><risdate>2009</risdate><volume>8</volume><issue>2</issue><spage>638</spage><epage>643</epage><pages>638-643</pages><issn>1536-1276</issn><eissn>1558-2248</eissn><coden>ITWCAX</coden><abstract>Collaborative beamforming has been recently introduced in the context of wireless sensor networks (WSNs) to increase the transmission range of individual sensor nodes. The challenge in using collaborative beamforming in WSNs is the uncertainty regarding the sensor node locations. However, the actual sensor node spatial distribution can be modeled by a properly selected probability density function (pdf). In this paper, we model the spatial distribution of sensor nodes in a cluster of WSN using Gaussian pdf. Gaussian pdf is more suitable in many WSN applications than, for example, uniform pdf which is commonly used for flat ad hoc networks. The average beampattern and its characteristics, the distribution of the beampattern level in the sidelobe region, and the distribution of the maximum sidelobe peak are derived using the theory of random arrays. We show that both the uniform and Gaussian sensor node deployments behave qualitatively in a similar way with respect to the beamwidths and sidelobe levels, while the Gaussian deployment gives wider mainlobe and has lower chance of large sidelobes.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TWC.2009.071339</doi><tpages>6</tpages></addata></record> |
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| subjects | Ad hoc networks Antennas antennas and propagation Applied sciences Array signal processing Arrays Beamforming Collaboration Detection, estimation, filtering, equalization, prediction Exact sciences and technology Gaussian Information, signal and communications theory Networks Probability density function Probability density functions Radiocommunications Random variables resource allocation and interference management Resource management Sensor arrays sensor networks Sensor phenomena and characterization Sensors Services and terminals of telecommunications Sidelobes Signal and communications theory Signal, noise Spatial distribution Studies Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Transmission and modulation (techniques and equipments) Uncertainty Wireless sensor networks |
| title | Collaborative beamforming for wireless sensor networks with Gaussian distributed sensor nodes |
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