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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 total network throughput comparison of the proposed model against SEED, ABC, CEEC, and ZBR in the lifetime of the network on the basis of initial energies of the node. (a) The network throughput analysis; (b) The evaluation of network throughput with different initial energies.
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sensors-17-00440-f013: The total network throughput comparison of the proposed model against SEED, ABC, CEEC, and ZBR in the lifetime of the network on the basis of initial energies of the node. (a) The network throughput analysis; (b) The evaluation of network throughput with different initial energies.

Mentions: The successful delivery of the data packets at the BS is called the throughput of the network. There is always a tradeoff between the network lifetime and the throughput of the network. The greater the lifetime of the network, the greater is the throughput and vice versa. The Figure 13a describes the comparison of network throughput of the proposed model against SEED, ABC, CEEC, and ZBR. The throughput of MOCHs, SEED, ABC, CEEC, and ZBR is , , , , and data packets, respectively. The throughput of the proposed model is , , , and data packets greater than SEED, ABC, CEEC, and ZBR, respectively. The proposed model has , , , and greater throughput than SEED, ABC, CEEC, and ZBR, respectively. As we discussed earlier, the network with the greater lifetime has the greater throughput. The proposed model has 1095 rounds greater lifetime than SEED; therefore, the proposed model has greater output than SEED. The throughput of the CEEC, and ABC is persuasive due to the longer lifetime. Both these schemes are using the centralized CHs selection methods and saving a lot of network energy, which leads to a longer network lifetime with a greater throughput. While in ZBR the network depletes its resources earlier than expected due to the CHs closer to the BS. The nodes closer to the BS selected as CHs and also working as relay nodes. When these nodes run out of batteries the lagged behind nodes cannot convey their sensed information to the BS and the network collapse resulting a much lower throughput. Figure 13b portrays the network throughput of SEED, ABC, CEEC, and ZBR with different network energies like 0.25 J, 0.75 J, and 1 J. The proposed model performance remains very persuasive with different network energies such as 0.25 J, 0.75 J, and 1 J. We also compare the throughput of the network with varying the node densities. The Table 6 and Table 7 reveal the comparison of the throughput of the network with and . From the tables, we can notice that the proposed model outperforms as compared to all of the above discussed methods.


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
The total network throughput comparison of the proposed model against SEED, ABC, CEEC, and ZBR in the lifetime of the network on the basis of initial energies of the node. (a) The network throughput analysis; (b) The evaluation of network throughput with different initial energies.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00440-f013: The total network throughput comparison of the proposed model against SEED, ABC, CEEC, and ZBR in the lifetime of the network on the basis of initial energies of the node. (a) The network throughput analysis; (b) The evaluation of network throughput with different initial energies.
Mentions: The successful delivery of the data packets at the BS is called the throughput of the network. There is always a tradeoff between the network lifetime and the throughput of the network. The greater the lifetime of the network, the greater is the throughput and vice versa. The Figure 13a describes the comparison of network throughput of the proposed model against SEED, ABC, CEEC, and ZBR. The throughput of MOCHs, SEED, ABC, CEEC, and ZBR is , , , , and data packets, respectively. The throughput of the proposed model is , , , and data packets greater than SEED, ABC, CEEC, and ZBR, respectively. The proposed model has , , , and greater throughput than SEED, ABC, CEEC, and ZBR, respectively. As we discussed earlier, the network with the greater lifetime has the greater throughput. The proposed model has 1095 rounds greater lifetime than SEED; therefore, the proposed model has greater output than SEED. The throughput of the CEEC, and ABC is persuasive due to the longer lifetime. Both these schemes are using the centralized CHs selection methods and saving a lot of network energy, which leads to a longer network lifetime with a greater throughput. While in ZBR the network depletes its resources earlier than expected due to the CHs closer to the BS. The nodes closer to the BS selected as CHs and also working as relay nodes. When these nodes run out of batteries the lagged behind nodes cannot convey their sensed information to the BS and the network collapse resulting a much lower throughput. Figure 13b portrays the network throughput of SEED, ABC, CEEC, and ZBR with different network energies like 0.25 J, 0.75 J, and 1 J. The proposed model performance remains very persuasive with different network energies such as 0.25 J, 0.75 J, and 1 J. We also compare the throughput of the network with varying the node densities. The Table 6 and Table 7 reveal the comparison of the throughput of the network with and . From the tables, we can notice that the proposed model outperforms as compared to all of the above discussed methods.

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.