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

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.


Analysis of random and optimal CHs selection on the lifetime of ZBR, SEED, and the proposed algorithm. (a) The effect of random and optimal CHs selection on the lifetime of MOCHs; (b) The effect of optimal CHs selection on the lifetime of ZBR, and SEED.
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sensors-17-00440-f010: Analysis of random and optimal CHs selection on the lifetime of ZBR, SEED, and the proposed algorithm. (a) The effect of random and optimal CHs selection on the lifetime of MOCHs; (b) The effect of optimal CHs selection on the lifetime of ZBR, and SEED.

Mentions: To check the suitability of optimal and random CHs of the proposed model, we perform simulations in different scenarios in which the proposed model selects the random, optimal, and random and optimal CHs. When the proposed model selects the CHs randomly, it uses the distributed algorithm to select the CHs. In this case, sometimes the numbers of CHs are near optimal. However, most of the time, the selected CHs are not optimal and consume the network resources, which rapidly leads to ending the network lifetime earlier. However, the optimal number of CHs selection is only possible with the help of the BS. Due to the BS selection, the nodes do not use their energies for computation and communication. So, the network saves a lot of energy through optimal CH selection which leads to a greater lifetime. However, the BS’s CHs selection is dependent on residual energies and distance from itself. The nodes with higher energies and at the lesser distance are selected while the member nodes suffer backtracking or link breakage due to the unsuitability of received messages from CH nodes. Conversely, this problem is solved when the proposed model simultaneously uses the optimal and random CHs selection as depicted in Figure 10a. We also check the effect of optimizing the CHs selection process on ZBR and SEED. Both these protocols use distributed algorithms for CHs selection and suffer a lot due to uneven energy consumption. After optimal CHs selection, we can see the increment in the lifetime of both the models in Figure 10b.


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
Analysis of random and optimal CHs selection on the lifetime of ZBR, SEED, and the proposed algorithm. (a) The effect of random and optimal CHs selection on the lifetime of MOCHs; (b) The effect of optimal CHs selection on the lifetime of ZBR, and SEED.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00440-f010: Analysis of random and optimal CHs selection on the lifetime of ZBR, SEED, and the proposed algorithm. (a) The effect of random and optimal CHs selection on the lifetime of MOCHs; (b) The effect of optimal CHs selection on the lifetime of ZBR, and SEED.
Mentions: To check the suitability of optimal and random CHs of the proposed model, we perform simulations in different scenarios in which the proposed model selects the random, optimal, and random and optimal CHs. When the proposed model selects the CHs randomly, it uses the distributed algorithm to select the CHs. In this case, sometimes the numbers of CHs are near optimal. However, most of the time, the selected CHs are not optimal and consume the network resources, which rapidly leads to ending the network lifetime earlier. However, the optimal number of CHs selection is only possible with the help of the BS. Due to the BS selection, the nodes do not use their energies for computation and communication. So, the network saves a lot of energy through optimal CH selection which leads to a greater lifetime. However, the BS’s CHs selection is dependent on residual energies and distance from itself. The nodes with higher energies and at the lesser distance are selected while the member nodes suffer backtracking or link breakage due to the unsuitability of received messages from CH nodes. Conversely, this problem is solved when the proposed model simultaneously uses the optimal and random CHs selection as depicted in Figure 10a. We also check the effect of optimizing the CHs selection process on ZBR and SEED. Both these protocols use distributed algorithms for CHs selection and suffer a lot due to uneven energy consumption. After optimal CHs selection, we can see the increment in the lifetime of both the models in Figure 10b.

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.