Scalable Infrastructure for Distributed Sensor Networks
Scalable Infrastructure for Distributed Sensor Networks
Collaborative beamforming for distributed wireless ad hoc sensor networks
IEEE Transactions on Signal Processing
On the Feasibility of Distributed Beamforming in Wireless Networks
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
Weighted cross-layer cooperative beamforming for wireless networks
IEEE Transactions on Signal Processing
Collaborative null-steering beamforming for uniformly distributed wireless sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Distributed null-steering beamforming for wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
IEEE Transactions on Wireless Communications
Sidelobe control in collaborative beamforming via node selection
IEEE Transactions on Signal Processing
Distributed transmit beamforming without phase feedback
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
IEEE Transactions on Communications
Energy-efficient data dissemination using beamforming in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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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.