Cluster based Wireless Sensor Networks' optimization under Energy Constraints

Slama, Ines; Ghedira, M.C.; Jouaber, Badii; Afifi, Haitham

doi:10.1109/issnip.2007.4496936

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The most important issue in the WSN is optimization of minimum resources. Dehghani in [10] proposed many routing algorithms are penned and described in detail for the optimization purpose. Some genetic algorithms also explained in this paper for the power optimization purpose.…”

Section: Literature Surveymentioning

confidence: 99%

Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

Prasad¹,

RaghavaRao²,

Kumar³

et al. 2018

IJET

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Wireless sensor networks are the sensors which are acclimated to sense the environmental condition like temperature, pressure, sultriness, moisture etc, sensing the environment parameters and sending them to the gateway and retrieving the aggregated data from the gateway to the end user. Power is the major constraint in wireless sensor networks. One must need to reduce the power consumption. Wireless sensor networks have sensor nodes in which each node has a processor, antenna and a battery. The batteries consume power so that we require increasing the lifetime of the battery for that some optimization techniques are required to reduce the power consumption. There are some techniques which are inspired from the lifestyle of animals. In this paper proposing an optimization technique which is inspired by the animal demeanor which reduces the power consumption of the sensor nodes which is particle swarm optimization (PSO) technique. PSO is inspired by the convivial demeanor of birds or schooling of fish. By utilizing this bio-inspired technique we can reduce the power consumed by the sensor nodes and at the same time lifetime of the batteries present in the sensor nodes are increased.

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Section: Literature Surveymentioning

confidence: 99%

Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

Prasad¹,

RaghavaRao²,

Kumar³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…A similar approach can be found in [26], but this time the ant colony optimization is applied with respect to an ad hoc network where nodes are not fixed. In [27] a global multi-hop routing strategy for heterogeneous sensor networks is proposed mixing tiered and clustered architectures through some heuristics.…”

Section: Wsn Optimizationmentioning

confidence: 99%

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

2012

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Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, etc. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences.

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“…A similar approach can be found in [24], but this time the ant colony optimization is applied with respect to an ad hoc network where nodes are not fixed. In [25] a global multihop routing strategy for heterogeneous sensor networks is proposed mixing tiered and clustered architectures through some heuristics.…”

Section: Wireless Sensor Network Optimization Issuesmentioning

confidence: 99%

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

Caputo

Grimaccia

Mussetta

et al. 2010

Applied Computational Intelligence and Soft Computing

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In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.

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Cluster based Wireless Sensor Networks' optimization under Energy Constraints

Cited by 4 publications

References 13 publications

Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

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