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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.

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The optimal number of CH selection, and optimal number of cluster formation in the network.
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sensors-17-00440-f006: The optimal number of CH selection, and optimal number of cluster formation in the network.

Mentions: Proper and careful utilization of the available resources can also help in increasing the lifetime of the network. The first step in this way is that the selection of CHs should be proper. An uneven number of CHs in every round can be a waste of resources. As defined earlier, the CH is responsible for collecting, fusing, and sending data of . So, each step in the cluster formation consumes the power of the nodes. After the random selection of the CHs, it is essential to check whether the selected number of CHs are meeting the optimality criteria or not. In this step, the BS is involved to verify the number of CHs selected in the random selection process as demonstrated in Figure 6. The selection of CHs through a random selection process is not optimal because the CHs are selected through the distributed algorithm. So, there is a need to optimize the resources of the network. It is necessary to check if the resources are consumed in a balanced way or not. To supervise all this CH selection and cluster formation process, we engage the BS to supervise and to certify all these selection procedures. The decisions of BS are more reliable, as the BS is enriched with high-speed processors and storage capabilities as compared to root nodes [9]. The BS is not just following a single criterion, it also takes into account the distance, remaining energy, the average energy of the network, and member nodes for CH selection. Before proceeding to the next phase, the BS makes sure that the selected CHs in a random process are optimized or not. The BS calculates the optimal number of CHs through the Markov model [26] using Equation (4) according to the number of sensor nodes in the network. The BS is the central entity; it can reject an already selected CH in the random selection process as revealed in Figure 6. After that, there are three cases for finalizing the optimal number of CHs as discussed below:CaseĀ 1


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
The optimal number of CH selection, and optimal number of cluster formation in the network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00440-f006: The optimal number of CH selection, and optimal number of cluster formation in the network.
Mentions: Proper and careful utilization of the available resources can also help in increasing the lifetime of the network. The first step in this way is that the selection of CHs should be proper. An uneven number of CHs in every round can be a waste of resources. As defined earlier, the CH is responsible for collecting, fusing, and sending data of . So, each step in the cluster formation consumes the power of the nodes. After the random selection of the CHs, it is essential to check whether the selected number of CHs are meeting the optimality criteria or not. In this step, the BS is involved to verify the number of CHs selected in the random selection process as demonstrated in Figure 6. The selection of CHs through a random selection process is not optimal because the CHs are selected through the distributed algorithm. So, there is a need to optimize the resources of the network. It is necessary to check if the resources are consumed in a balanced way or not. To supervise all this CH selection and cluster formation process, we engage the BS to supervise and to certify all these selection procedures. The decisions of BS are more reliable, as the BS is enriched with high-speed processors and storage capabilities as compared to root nodes [9]. The BS is not just following a single criterion, it also takes into account the distance, remaining energy, the average energy of the network, and member nodes for CH selection. Before proceeding to the next phase, the BS makes sure that the selected CHs in a random process are optimized or not. The BS calculates the optimal number of CHs through the Markov model [26] using Equation (4) according to the number of sensor nodes in the network. The BS is the central entity; it can reject an already selected CH in the random selection process as revealed in Figure 6. After that, there are three cases for finalizing the optimal number of CHs as discussed below:CaseĀ 1

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.