Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization

Siew, Zhan Wei; Wong, Chen How; Chin, Chia Seet; Kiring, Aroland; Teo, Kenneth Tze Kin

doi:10.1109/cicsyn.2012.25

Cited by 21 publications

(9 citation statements)

References 11 publications

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“…For example, if there are i nodes in a topology that has network coding node potential, a chromosome will split into i segments to represent the link state of each potential node. Consider a potential node i has j incoming flow and k outgoing flow, the total bit of segment i is shown in (1).…”

Section: A Chromosome Representationmentioning

confidence: 99%

“…Every node in wireless network communicates with each other by transmitting information packet among nodes. Although some protocols in wireless technology have been developed based on custom applications related to wireless sensor networks, most of the wireless networks still suffering from throughput limitation [1]. To overcome this limitation, network coding is introduced to increase network throughput [2,3,4].…”

mentioning

confidence: 99%

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Performance Analysis of Enhanced Genetic Algorithm Based Network Coding in Wireless Networks

Teo

Chin

Tan

et al. 2014

2014 8th Asia Modelling Symposium

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Network coding combines several packets from different sources and broadcasts the combined packet to several destinations in single transmission time slot. Each destination is capable to extract the intended information by decoding from a common packet. In short, network coding improves the throughput for wireless and wired networks but also causes side effects such as complexity of packets management and increased delay for coding opportunity. Hence, genetic algorithm is used to optimize the resources for network coding. Genetic algorithm will search for suitable routes to the destination according to the desired throughput with a desired multicast rate. In this paper, genetic algorithm is further enhanced in searching of optimum route for a packet. The simulation results show that enhanced genetic algorithm can adapt to various situations with different topologies, providing an optimum route for a packet in the network.

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Section: A Chromosome Representationmentioning

confidence: 99%

mentioning

confidence: 99%

Performance Analysis of Enhanced Genetic Algorithm Based Network Coding in Wireless Networks

Teo

Chin

Tan

et al. 2014

2014 8th Asia Modelling Symposium

Self Cite

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“…For CHD [9] the cluster head selection takes place according to PSO algorithm in [4].The velocity calculation is given as equation 7.The value of the constants c1 and c2 is calculated from the formula:…”

Section: H Cluster Head Distribution Via Adaptive Pso (Chd)mentioning

confidence: 99%

“…A reselect mechanism is proposed in CHD to overcome stagnation in the CH selection [9]. If global best does not change after a number of iterations, 25% particles from the worst set will be re-selected.…”

Section: H Cluster Head Distribution Via Adaptive Pso (Chd)mentioning

confidence: 99%

Particle Swarm Optimization based Cluster Head Election Approach for Wireless Sensor Network

Sharma¹,

Tomar²,

Kumar³

et al. 2014

IJSBT

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Wireless sensor networks comprise several sensor nodes being supported by limited capacity battery source. The network hierarchy may be chosen as per required applications, such as clustered arrangement. Cluster heads play a major role in clustering Wireless Sensor Network. Particle swarm optimization (PSO) is one of the swarm based intelligence methods for locating optimum solution by imitating the behavior of flocks of birds and fish schooling. PSO is based on the movement and intelligence of swarms. Social learning factor can achieve better convergence speed and particle reselection mechanism reduces the chances of being trapped in local maximum. In this paper cluster head selection based on PSO approach is proposed. The performance of the proposed approach is compared with low energy adaptive cluster Hierarchy (LEACH). Simulation result shows that proposed approach outperforms LEACH in terms of first node died (FND), total data received by base station and energy consume per round. The simulation has been carried out over different network size and with different number of cluster heads and it is clearly seen from the results that the proposed approach outperform LEACH in large network size also.

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“…To improve the energy conservation during the communication duration, network protocol such as LEACH is introduced in WSNs [1], [2]. The protocol has been further improved using fuzzy [3] and Adaptive Particle Swarm Optimization algorithm [4] which has shown notable improvement in extending the lifetime of the sensor nodes. Alternative solutions such as collaborative beamforming (CB) is introduced to increase the energy conservation in [5]- [7].…”

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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Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization

Cited by 21 publications

References 11 publications

Performance Analysis of Enhanced Genetic Algorithm Based Network Coding in Wireless Networks

Performance Analysis of Enhanced Genetic Algorithm Based Network Coding in Wireless Networks

Particle Swarm Optimization based Cluster Head Election Approach for Wireless Sensor Network

Node collaboration for distributed beamforming in wireless sensor networks

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