A low energy intelligent clustering protocol for wireless sensor networks

Qiao, Li; Cui, Lingguo; Zhang, Baihai; Fan, Zhun

doi:10.1109/icit.2010.5472629

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…Ankit Sharma et al in [5] proposed an idea of clustering by combining the BFA, LEACH and HEED protocols which increase the network life. Qiao Li et al in [12] presented a Low Energy Intelligent Clustering Protocol (LEICP) which is enhancement of LEACH. It works more efficiently than LEACH.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal Sensor Nodes Deployment Method Using Bacteria Foraging Algorithm in Wireless Sensor Networks

Nagchoudhury

Maheshwari

Choudhary

2015

Advances in Intelligent Systems and Computing

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Abstract. The nodes in wireless sensor networks (WSN) need to be deployed optimally to cover whole geographic area and the communication link between them should be optimal. During deployment in remote locations, simulation of the proposed algorithm can be used for optimally deploying the sensor nodes in the area. Bacteria foraging is the nature inspired algorithm which is used to make clusters among various similar entities. Here the nodes to be deployed can be taken as the bacteria that are in search of the food which is depicted by the best possible communication link. This paper presents a novel algorithm for optimal sensor node deployment leading to optimal clustering of WSN nodes. This paper utilizes the fact that an area can be covered fully using regular hexagon. So using a bacteria foraging algorithm for optimization the locations are such adjusted that all the nodes in the network moves to vertices of regular hexagons connected with each other. This leads to complete coverage of the area and all the nodes are equidistant.

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Section: Literature Surveymentioning

confidence: 99%

Optimal Sensor Nodes Deployment Method Using Bacteria Foraging Algorithm in Wireless Sensor Networks

Nagchoudhury

Maheshwari

Choudhary

2015

Advances in Intelligent Systems and Computing

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“…Qiao Li et al [4] proposed a LEICP (low energy intelligent clustering protocol), an improvement of the LEACH protocol to overcome the shortcomings of LEACH. LEICP achieved balancing the energy consumption in every cluster and prolonged the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Bacteria Foraging Algorithm based clustering in Wireless Sensor Networks

Pitchaimanickam

Radhakrishnan

2013

2013 Fifth International Conference on Advanced Computing (ICoAC)

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Wireless Sensor Network (WSN) has a battery oriented device and each sensor node collects the information from the environment and passes to the base station. It spents more energy during the communication and sensor nodes loose its energy quickly. Energy efficiency is one of the important factor in designing wireless sensor network. For improving lifetime of the network, the cluster based protocol i.e., LEACH-C protocol was used to select the cluster head to form k-optimal clusters by using the simulated annealing optimization. But this optimization is not suitable for extending the life time and also it provides the results with few local minima. These problems can be overcome by introducing the Bacteria Foraging Algorithm that forms optimal clusters by identifying the cluster head that have energy more than the average energy of the k-optimal clusters. We have implemented this method in ns2 and simulation results show the improvement of the life time of the network by increase in the number of alive nodes, reduction in the energy consumption.

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“…In addition, the node can be set to several states, such as lowenergy consumption state, transmission state, receiving state and so on. In LEICP [9], in order to prolong the lifetime of the WSNs, a fitness function is defined to balance the energy consumption in every cluster according to the residual energy and positions of nodes. In [10], the authors present a comprehensive energy model for a fully function wireless sensor network, and the model is divided into 2 parts, energy consumption due to synchronization and the energy consumption due to data transmission.…”

Section: A Related Workmentioning

confidence: 99%

A Failure Self-recovery Strategy with Balanced Energy Consumption for Wireless Ad Hoc Networks

Qiu¹,

Wang²,

Xia³

et al. 2012

JCP

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<span style="line-height: 115%; font-family: "TimesNewRomanPS-BoldMT","sans-serif"; font-size: 9pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-bidi-font-family: TimesNewRomanPS-BoldMT; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">In energy constrained wireless sensor networks, it is significant to make full use of the limited energy and maximize the network lifetime even when facing some unexpected situation. In this paper, all sensor nodes are grouped into clusters, and for each cluster, it has a mobile cluster head to manage the whole cluster. We consider an emergent situation that one of the mobile cluster heads is broken down, and hence the whole cluster is consequently out of work. An efficient approach is proposed for recovering the failure cluster by selecting multiple static sensor nodes as the cluster heads to collect packets and transmit them to the sink node. Improved simulated annealing algorithm is utilized to achieve the uniform deployment of the cluster heads. The new cluster heads are dynamically changed in order to keep balanced energy consumption. Among the new cluster heads, packets are transmitted through multi-hop forwarding path which is cost-lowest path found by Dijkstra’s algorithm. A balanced energy consumption model is provided to help find the cost-lowest path and prolong the lifetime of the network. The forwarding path is updated dynamically according to the cost of the path and residual energy of the node in that path. The experimental results show that the failure cluster is recovered and the lifetime of the cluster is prolonged.<span style="mso-spacerun: yes;"> </span></span>

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A low energy intelligent clustering protocol for wireless sensor networks

Cited by 17 publications

References 13 publications

Optimal Sensor Nodes Deployment Method Using Bacteria Foraging Algorithm in Wireless Sensor Networks

Optimal Sensor Nodes Deployment Method Using Bacteria Foraging Algorithm in Wireless Sensor Networks

Bacteria Foraging Algorithm based clustering in Wireless Sensor Networks

A Failure Self-recovery Strategy with Balanced Energy Consumption for Wireless Ad Hoc Networks

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