Optimization of Distributed and Collaborative Beamforming in Wireless Sensor Networks

Wong, Chen How; Siew, Zhan Wei; Tan, Min; Chin, Renee Ka Yin; Teo, Kenneth Tze Kin

doi:10.1109/cicsyn.2012.26

Cited by 25 publications

(13 citation statements)

References 12 publications

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“…Similarly, node selection method based on uniform space linear array was done using GA for PSL minimization in [24]. Both papers adopt node selection method, which is basically the array synthesis method to achieve PSL reduction.…”

Section: Beampattern Optimization In Collaborative Beamformingmentioning

confidence: 99%

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

Jayaprakasam

Rahim

Leow

2015

Applied Soft Computing

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Section: Beampattern Optimization In Collaborative Beamformingmentioning

confidence: 99%

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

Jayaprakasam

Rahim

Leow

2015

Applied Soft Computing

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“…To overcome the problem, a uniform linear array has been proposed to select participating nodes to perform a suitable beam pattern by [11], [12]. The solution based on uniform linear array can be further optimized using Particle Swarm Optimization or genetic algorithm [13], [14]. Both solutions show a reduction in sidelobe level (SSL).…”

Section: Introductionmentioning

confidence: 99%

Node collaboration for distributed beamforming in wireless sensor networks

Wong

Siew

Yoong

et al. 2012

2012 IEEE International Conference on Control System, Computing and Engineering

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Collaborative beamforming (CB) is introduced in wireless sensor networks (WSNs) to improve the transmission power and the energy efficiency of the networks. Using a subset number of nodes from a network of sensors, they collectively transmit a common message with different proper weights to an intended location. Different geometrical location of the subset of nodes will produce different transmission gains. Due to erratic deployment of the sensor nodes in the networks, the assignment for the sensor nodes in wireless sensor networks is vital to achieve better array pattern synthesis. In this paper, a node selection method based on uniform circular array is presented. Simulation and performance analysis are carried out to demonstrate the beam pattern performance of uniform circular in performing collaborative beamforming.Index Terms-Collaborative beamforming; circular array; array signal processing; wireless sensor networks.

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“…Such transceivers have digital control of the amplitude and phase of its signals and allow great flexibility in digital signal processing in wireless sensor network (WSN) [1][2][3]. The sensors in WSNs are however often small in size and battery operated.…”

Section: Introductionmentioning

confidence: 99%

“…The sensors in WSNs are however often small in size and battery operated. This poses constraints on the sensors' available resources, namely the energy, memory and the computational capabilities [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method

Jayaprakasam

Rahim

Leow

et al. 2014

2014 IEEE Symposium on Wireless Technology and Applications (ISWTA)

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Distributed beamforming is a communication method in wireless sensor networks (WSNs) where the sensor nodes collaboratively create a virtual antenna to direct their radiating power towards the direction of an intended destination. This method could increase the transmission range of the network and save the sensors' energy. However, due to the random locations of the sensor nodes, the beampattern for a finite number of nodes usually has asymmetrical sidelobes with high sidelobe levels. Higher sidelobe levels cause undesirable interferences at directions other than the intended destination. Conventional sidelobe reduction methods proposed for centralized antenna array cannot be used for distributed beamforming networks. This paper proposes a distributed network compliant, multi-objective weight optimization technique to produce a beampattern with lower sidelobe levels, higher directivity and minimal energy. Exhaustive search for the most favorable weight solutions is time-consuming when the number of sensor nodes is large. Therefore, this paper analyses the use of nature-inspired metaheuristic algorithms to solve for the best weight values at each sensor node. Three algorithms were analysed, namely, genetic algorithm (GA), particle swarm optimization (PSO) and gravitational search algorithm (GSA). Simulation results show that the proposed multi-objective weight optimization using nature inspired algorithm can provide improved beampattern with lower sidelobes, higher directivity and better energy savings.

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Optimization of Distributed and Collaborative Beamforming in Wireless Sensor Networks

Cited by 25 publications

References 12 publications

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

Node collaboration for distributed beamforming in wireless sensor networks

Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method

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