A cluster based energy balancing strategy to improve Wireless Sensor Networks lifetime

Gamwarige, Sankalpa; Kulasekere, Chulantha

doi:10.1109/iciinfs.2007.4579211

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Hence according to (6) we can expect a .5018 fraction of nodes belonging to a given CH's broadcasting range R neighborhood to join its cluster. Further using (5) and (6) we can derive…”

Section: Nomenclaturementioning

confidence: 99%

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An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks

Gamwarige

Kulasekere

2009

2009 International Conference on Industrial and Information Systems (ICIIS)

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Energy Driven Cluster-Head Rotation (EDCR) class of algorithms are energy aware distributed clustering techniques for effective ad hoc deployed Wireless Sensor Network (WSN) organization. The application of this class of algorithms requires the setting of salient parameters at the design stage of the WSN to achieve desired results. Two such parameters which should be known in advance are the cluster density and the distance between neighbouring cluster heads (CHs). In this research we analyze the effect of algorithm design based on these two parameters. Simulation techniques are given to support and verify the analytical results. I. INTRODUCTIONDistributed clustering is identified as the most energy efficient technique to organize large numbers of ad hoc-deployed energy constrained wireless sensors which are used for periodic data gathering and transmission to a remote base station (BS) [1] - [3]. We have shown in [4] that the EDCR is one such very effective algorithm for distributed clustering. However, the deployment of such WSNs and their clustering algorithms require the following design parameters to be known. The first of these is the cluster density or the desired number of clusters on the sensor bed during operations. For example it is a common requirement of most WSN deployment strategies to form k number of clusters each containing n number of nodes within the given deployment region (area a × b) taking into consideration the redundancy required for reliable operation. The second parameter of interest is, the knowledge of the average distance between neighboring CHs prior to deployment. This is required when the algorithm uses multi hopping over a CH overlay in sending the aggregated cluster data to .In order to facilitate effective WSN deployment, analytical results for the above two parameters should be known a priori. To the authors knowledge analytical methods to determine the above two parameters for energy driven algorithms which produce well distributed CHs are not found in literature. In this research we will find the probability distribution of the cluster area and then use it to derive the relationship between this distribution and the cluster density. Further we will consider the boundary effect due to the finite area of the WSN which is referred to as the border effect in our analysis. We note that

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“…Hence according to (6) we can expect a .5018 fraction of nodes belonging to a given CH's broadcasting range R neighborhood to join its cluster. Further using (5) and (6) we can derive…”

Section: Nomenclaturementioning

confidence: 99%

“…The second parameter of interest is, the knowledge of the average distance between neighboring CHs prior to deployment. This is required when the algorithm uses multi hopping over a CH overlay in sending the aggregated cluster data to BS [5] - [6].…”

Section: Introductionmentioning

confidence: 99%

An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks

Gamwarige

Kulasekere

2009

2009 International Conference on Industrial and Information Systems (ICIIS)

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“…The aim of the topology management scheme [6][7][8][9] is to maintain the sufficient network connectivity between the nodes such that the data can be forwarded efficiently to the sink. In Geographic Adaptive Fidelity (GAF) scheme [3,10,11], a virtual grid is formed throughout the deployed network, and each node is assigned to the virtual grid cell in which it resides.…”

Section: Related Workmentioning

confidence: 99%

Connectivity-Guaranteed Hybrid Topology Management Scheme for Improving the Operational Lifetime of 3-Dimensional Wireless Sensor Networks

Jawahar¹,

Radha²,

Vadivelan³

2010

International Journal of Distributed Sensor Networks

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The battery of limited energy operates wireless sensor nodes, it is important to increase the lifetime of the wireless sensor network. The aim of our work is to design a hybrid topology management scheme for improving the lifetime of the wireless sensor networks with guaranteed connectivity. The sensing range of the sensor node is smaller than the radio range and hence there is a large number of nodes within the coverage area of a node. Hence the network connectivity can be maintained with less number of nodes. By using this fact, we can save energy by switching off the transceiver, which consumes most of the energy. On the other hand, the sensor node spends most of the time in monitoring state and the transceiver is idle. In the idle state the transceiver consumes almost the same energy as in receiving state. Hence putting the transceiver in the sleep mode when it is idle can save the significant energy. We have designed a hybrid topology management scheme for 3-Dimensional sensor networks by making use of these facts. Our hybrid scheme improves the lifetime of the 3-Dimensional sensor network by a factor of 19.

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“…Furthermore, the variation between the theoretical expected number of CHs for these algorithms is considerable when compared to the actual number of CHs obtained after deployment [8]. The second category consists of distributed clustering algorithms like DMAC [1], HEED [9], ANTCLUST based [10], MEDIC [11], EDCR [12], and its derivatives [13]. The location of a CH for these algorithms is dependent on its neighbors decision as well.…”

Section: Introductionmentioning

confidence: 99%

Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks

Gamwarige

Kulasekere

2012

Journal of Computer Networks and Communications

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Distributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node's decision to be a CH depends on the decision of its neighbors. An analytical technique to determine the cluster density of this class of algorithms is proposed. This information is required to set the algorithm parameters before a wireless network is deployed. Simulation results are presented in order to verify the analytical findings.

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A cluster based energy balancing strategy to improve Wireless Sensor Networks lifetime

Cited by 5 publications

References 7 publications

An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks

An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks

Connectivity-Guaranteed Hybrid Topology Management Scheme for Improving the Operational Lifetime of 3-Dimensional Wireless Sensor Networks

Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks

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