A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks

Aslam, Nauman; Phillips, William J.; Robertson, William; Sivakumar, S.

doi:10.1016/j.inffus.2009.12.005

Cited by 125 publications

(75 citation statements)

References 24 publications

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“…The authors in [5] used Kohonen SOM neural networks for clustering and their analysis to study unpredictable behaviors of network parameters and applications. Clustering of sensor nodes using Kohonen Self Organizing Map (KSOM) is computed for various numbers of nodes by taking different parameters of sensor node such as direction, position, number of hops, energy levels, sensitivity, latency, etc.…”

Section: Energy Efficient Node Clusteringmentioning

confidence: 99%

Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey

Mittal¹,

Kumar²

2015

IJCA

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Wireless sensor network (WSN) is one of emerging trends in networking technologies being used for communication purpose in modern life. It has mainly comprised of small sensor nodes (SNs) with limited resources. Individual SNs are connected with each other and make the communication possible. Enhancement in the communication among sensor nodes or Sensor-to-Sink nodes is today's most prominent objective. In this paper we have surveyed artificial neural network for different QOS parameters of WSN. Artificial neural network (ANN) is very prominent emerging area for WSN applications. Generally, artificial neural networks are classified in supervised learning and unsupervised learning. Unsupervised learning includes algorithms like Hebbian, Winner-take-all, ART, ART1, ART2, counter propagation network etc., while supervised learning includes perceptron model, delta learning rule, error back-propagation etc. ANN helps to achieve the better quality of services for communication in wireless sensor networks at the greater extent. We have summarized the survey of neural networks' techniques applied for WSN applications so far. KeywordsWireless sensor network; artificial neural network; unsupervised learning; supervised learning; Fuzzy ART; ART1; ART2; perceptron model; error back propagation; quality of services.

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Section: Energy Efficient Node Clusteringmentioning

confidence: 99%

Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey

Mittal¹,

Kumar²

2015

IJCA

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“…A new routing algorithm ADADR [21] has been proposed based on the concept of potential in the discipline of physics. The main objective of this paper has been to get better the degree of data aggregation and minimize ANT.…”

Section: Related Workmentioning

confidence: 99%

Improving the Network Lifetime for WSNs using Multiple Data Collectors

Kaur¹,

Singh²

2015

IJCA

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Energy is the main component of sensor nodes. IEESSC used sink mobility to increase the network lifetime. IEESSC also leads to uneven cluster. EEEGBC proposed algorithm uses grid based method to make same size clusters and it also used the concept of multiple sink to increase the network lifetime. It removes the idea of re-clustering and energy is saved that use for re-clustering. The simulation analysis are done based on three parameters: Network Lifetime, Stability Period and Middle Node Death.

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“…However, in WSN scenario the major goals for developing routing protocols are improvement of network availability, increasing of the sensor network battery life time, efficient energy consumption [25], minimizing the delay in data transmission, and reducing of complexity of operation.…”

Section: New Research Directionsmentioning

confidence: 99%

Energy Efficient Routing Strategies for Clustered Wireless Sensor Networks: An Analytical Framework

Anandh¹,

Baburaj²

2013

IJCA

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The efficiency of a wireless sensor network depends on its life time. By conserving the battery power of individual sensor devices, considerable amount of total energy can be saved thereby increasing the total lifetime of the network. The operation phases of a wireless sensor network comprises of sensing the data, aggregating the data in an intermediate node, communicating and forwarding the aggregated data to the sink for further process. Among these, communication or routing process requires more energy than any other operation. Hence a better approach would be allowing only a selected number of nodes to communicate with the sink. This idea led to the construction of the sensor network as a number of clusters with a dynamically elected cluster-head node is only allowed to forward the data to the sink. Various routing techniques for clustered Wireless Sensor Networks has emerged yielding good result in improving the lifetime of the network. Here in this paper, a survey of these routing techniques are presented.

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A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks

Cited by 125 publications

References 24 publications

Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey

Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey

Improving the Network Lifetime for WSNs using Multiple Data Collectors

Energy Efficient Routing Strategies for Clustered Wireless Sensor Networks: An Analytical Framework

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