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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.


(a) The angle of signal arrival, (b) decrease in signal strength as it propagates from sender towards the receiver nodes, (c) neighbor discovery process.
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sensors-17-00440-f002: (a) The angle of signal arrival, (b) decrease in signal strength as it propagates from sender towards the receiver nodes, (c) neighbor discovery process.

Mentions: The received signal strength indication is utilized to compute the distance of one node from another. The strength of a signal fades as long it propagates from sender nodes towards the receiver nodes as revealed in Figure 2. A radio propagation model can be used to estimate the distance between two nodes on the basis of receiving signal.


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
(a) The angle of signal arrival, (b) decrease in signal strength as it propagates from sender towards the receiver nodes, (c) neighbor discovery process.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00440-f002: (a) The angle of signal arrival, (b) decrease in signal strength as it propagates from sender towards the receiver nodes, (c) neighbor discovery process.
Mentions: The received signal strength indication is utilized to compute the distance of one node from another. The strength of a signal fades as long it propagates from sender nodes towards the receiver nodes as revealed in Figure 2. A radio propagation model can be used to estimate the distance between two nodes on the basis of receiving signal.

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.