Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks

Karjee, Jyotirmoy; Jamadagni, H. S.

doi:10.1007/978-1-4614-3363-7_22

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The term

f_{T} ()(, y falsefalse| i) = b ()(, y, i, u_{S_{2}})

follows a binomial distribution, and defines the termination probability that y nodes exit the active state, given that there are i active nodes in the frame. The steady‐state probability

π_{j}

is the probability of being in state

x = j

where there are j number of active nodes in a frame, this probability is derived by applying the stationary distribution to the transition matrix

{bold-italicP}_{i j}

and solving the equations below

π_{j} = \sum_{i = 0}^{K} π_{i} {bold-italicP}_{i j}, \sum_{j = 0}^{K} π_{j} = 1

The probability that out of the j active sensor nodes in a network of z clusters, k nodes belong to a cluster is a binomial process given by [19, 20]

P ()(, k \leq CN - 1 falsefalse| j) = \sum_{k = 0}^{CN - 1} ()(, \begin{matrix} 1em4pt \\ j \\ k \end{matrix}) {(\frac{1}{z})}^{k} {(1 - \frac{1}{z})}^{j - k} π_{j},

where

CN

is the number of sensor nodes in a cluster.…”

Section: Analytical Modelmentioning

confidence: 99%

Energy‐efficient medium access control and routing protocol for multihop wireless sensor networks

Sefuba

Walingo

2018

IET wirel. sens. syst.

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This work presents a combined energy-efficient medium access control (MAC) and routing protocol for large-scale wireless sensor networks that aims to minimise energy consumption and prolong the network lifetime. The proposed communication framework employs the following measures to enhance the network energy efficiency. Firstly, it provides an adaptive intra-cluster schedule to arbitrate media access of sensor nodes within a cluster, minimising idle listening on sensor nodes, leading to improved energy performance. Secondly, it proposes an on-demand source cross-layer routing protocol ensuring selection of best routes based on energy level and channel quality indicator for the multihop inter-cluster data transmission. Lastly, an unequal cluster size technique based on cluster head residual energy and distance away from the base station is utilised. This technique balances the energy among clusters and avoids early network partitioning. This work further presents the analytical performance model for energy consumption and delay of the proposed communication framework. The performance measures used for evaluation are energy consumption, delay, and network lifetime. The results indicate that combining routing and MAC schemes conserves energy better than utilising MAC scheme alone.

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“…The term

f_{T} ()(, y falsefalse| i) = b ()(, y, i, u_{S_{2}})

follows a binomial distribution, and defines the termination probability that y nodes exit the active state, given that there are i active nodes in the frame. The steady‐state probability

π_{j}

is the probability of being in state

x = j

where there are j number of active nodes in a frame, this probability is derived by applying the stationary distribution to the transition matrix

{bold-italicP}_{i j}

and solving the equations below

π_{j} = \sum_{i = 0}^{K} π_{i} {bold-italicP}_{i j}, \sum_{j = 0}^{K} π_{j} = 1

The probability that out of the j active sensor nodes in a network of z clusters, k nodes belong to a cluster is a binomial process given by [19, 20]

P ()(, k \leq CN - 1 falsefalse| j) = \sum_{k = 0}^{CN - 1} ()(, \begin{matrix} 1em4pt \\ j \\ k \end{matrix}) {(\frac{1}{z})}^{k} {(1 - \frac{1}{z})}^{j - k} π_{j},

where

CN

is the number of sensor nodes in a cluster.…”

Section: Analytical Modelmentioning

confidence: 99%

Energy‐efficient medium access control and routing protocol for multihop wireless sensor networks

Sefuba

Walingo

2018

IET wirel. sens. syst.

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Information Estimation with Node Placement Strategy in 3D Wireless Sensor Networks

Karjee¹,

Jamadagni

2017

IETE Journal of Research

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The cluster formation in Three Dimensional Wireless Sensor Networks (3D-WSN) give rise to overlapping of signals due to spherical sensing range which leads to information redundancy in the network. To address this problem, we develop a sensing algorithm for 3D-WSN based on dodecahedron topology which we call Three Dimensional Distributed Clustering (3D-DC) algorithm. Using 3D-DC algorithm in 3D-WSN, accurate information extraction appears to be a major challenge due to the environmental noise where a Cluster Head (CH) node gathers and estimates information in each dodecahedron cluster. Hence, to extract precise information in each dodecahedron cluster, we propose Three Dimensional Information Estimation (3D-IE) algorithm. Moreover, Node deployment strategy also plays an important factor to maximize information accuracy in 3D-WSN. In most cases, sensor nodes are deployed deterministically or randomly. But both the deployment scenario are not aware of where to exactly place the sensor nodes to extract more information in terms of accuracy. Therefore, placing nodes in its appropriate positions in 3D-WSN is a challenging task. We propose a Three Dimensional Node Placement (3D-NP) algorithm which can find the possible nodes and their deployment strategy to maximize information accuracy in the network. We perform simulations using MATLAB to validate the 3D-DC, 3D-IE and 3D-NP, algorithms respectively.

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Spatial data estimation in three dimensional distributed wireless sensor networks

Karjee

Kleasinsteuber

Jamadagni³

2014

2014 International Conference on Embedded Systems (ICES)

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Due to deployment of inflated amount of sensor nodes in three dimensional space, observed data are highly correlated among sensor nodes. Since the data are highly correlated, it produces large quantity of redundant data in the network. To reduce data redundancy, we propose a clustering algorithm called Three Dimensional Event based Spatially Correlated Clustering (3D-ESCC) algorithm. Moreover, to extract more accurate data in each distributed cluster of 3D-ESCC algorithm, we propose an Event based Data Estimation (EDE) model in three dimensional space and compare it with other data estimation models. In distributed wireless sensor networks, it may be possible that due to extreme physical condition (e.g heavy rainfall, high temperature and battery discharge) the sensor nodes fails to operate. In such situation, we are able to develop a data prediction model in distributed cluster in case of node failure. Computer simulations and validations are performed to validate 3D-ESCC algorithm and EDE model.

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Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks

Cited by 4 publications

References 28 publications

Energy‐efficient medium access control and routing protocol for multihop wireless sensor networks

Energy‐efficient medium access control and routing protocol for multihop wireless sensor networks

Information Estimation with Node Placement Strategy in 3D Wireless Sensor Networks

Spatial data estimation in three dimensional distributed wireless sensor networks

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