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Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

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ABSTRACT

The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

No MeSH data available.


The number of clusters and CHs analysis through standard deviation, and the coefficient of variation. (a) The standard deviation of CHs in the network; (b) The coefficient of variation of CHs in the network.
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sensors-17-00440-f009: The number of clusters and CHs analysis through standard deviation, and the coefficient of variation. (a) The standard deviation of CHs in the network; (b) The coefficient of variation of CHs in the network.

Mentions: The current clustering schemes are using distributed methods for CHs selection which do not assure the optimal value of CHs according to our analysis. We also found that the optimum percentage of CHs is always less than which does not affect the network lifetime. In worst case scenarios, when no CH is selected or the CHs are less than the optimal value the clustering structure will collapse or the network will drain its resources earlier than expected. The unevenness of CHs in the clustering structure badly affects the energy efficiency and the network lifetime. Figure 9a,b demonstrates the COV and the SD in the selection of CHs; the results are extracted from the proposed model in different scenarios. We can see that the larger the sensing field the greater the number of CHs, and the wider the distribution for the number of CHs.


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
The number of clusters and CHs analysis through standard deviation, and the coefficient of variation. (a) The standard deviation of CHs in the network; (b) The coefficient of variation of CHs in the network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5375726&req=5

sensors-17-00440-f009: The number of clusters and CHs analysis through standard deviation, and the coefficient of variation. (a) The standard deviation of CHs in the network; (b) The coefficient of variation of CHs in the network.
Mentions: The current clustering schemes are using distributed methods for CHs selection which do not assure the optimal value of CHs according to our analysis. We also found that the optimum percentage of CHs is always less than which does not affect the network lifetime. In worst case scenarios, when no CH is selected or the CHs are less than the optimal value the clustering structure will collapse or the network will drain its resources earlier than expected. The unevenness of CHs in the clustering structure badly affects the energy efficiency and the network lifetime. Figure 9a,b demonstrates the COV and the SD in the selection of CHs; the results are extracted from the proposed model in different scenarios. We can see that the larger the sensing field the greater the number of CHs, and the wider the distribution for the number of CHs.

View Article: PubMed Central - PubMed

ABSTRACT

The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

No MeSH data available.