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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 optimal CHs selection on the basis of residual energy and distance Via BS.
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sensors-17-00440-f007: The optimal CHs selection on the basis of residual energy and distance Via BS.

Mentions: Where, is the energy consumed per bit to run the transmitter or the receiver circuit, and depend on the transmitter amplifier model we use, and d is the distance between the sender and the receiver. We assume that the radio channel is symmetric such that the energy required to transmit a message from node A to node B is the same as the energy required to transmit a message from node B to node A for a given SNR. The energy consumed by a CH for aggregating and sending data to the BS is computed as:(13)ECH=lEelecNK−1+lεampd(CH,BS)4+lεfsd(N,CH)2where, K is the number of clusters in a network of N nodes. As we discuss earlier, that the BS has unlimited resources. Therefore, these calculations and computations cannot affect the network lifetime. When the BS gets the , then it ensures that the already selected are near to the optimal number or not. If the network is uniformly divided into clusters on the basis of selected , then the BS allows the system to move on the next phase. Otherwise, it picks a few nodes with higher residual energy and lesser communication distance according to the calculated optimal number through the Markov model. In the first round, the BS selects the CHs on the basis of communication distance from the entire network. However, in the other rounds the sensor nodes, including CHs send their residual energy information with the data packets. As the rounds proceed, each node consumes energy in sensing, transmission, and reception. At the end of each round the and CHs calculate their remaining energy and send this information to the BS with the data packets. Now, the BS is well aware of the remaining energies of all the and the CHs at the end of every round. So, the BS uses this information for selecting the for the next round. As a result, after each round the BS updates the residual energy information of each node in the network. Finally, the are selected on the basis of remaining energy and communication distance. If sometimes the nodes selected through this criterion are less than an optimal number, then the CHs from the list and the list with higher residual energy are preferred to be selected as . The optimal CH selection, and cluster formation procedures are shown in Figure 7.


Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
The optimal CHs selection on the basis of residual energy and distance Via BS.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00440-f007: The optimal CHs selection on the basis of residual energy and distance Via BS.
Mentions: Where, is the energy consumed per bit to run the transmitter or the receiver circuit, and depend on the transmitter amplifier model we use, and d is the distance between the sender and the receiver. We assume that the radio channel is symmetric such that the energy required to transmit a message from node A to node B is the same as the energy required to transmit a message from node B to node A for a given SNR. The energy consumed by a CH for aggregating and sending data to the BS is computed as:(13)ECH=lEelecNK−1+lεampd(CH,BS)4+lεfsd(N,CH)2where, K is the number of clusters in a network of N nodes. As we discuss earlier, that the BS has unlimited resources. Therefore, these calculations and computations cannot affect the network lifetime. When the BS gets the , then it ensures that the already selected are near to the optimal number or not. If the network is uniformly divided into clusters on the basis of selected , then the BS allows the system to move on the next phase. Otherwise, it picks a few nodes with higher residual energy and lesser communication distance according to the calculated optimal number through the Markov model. In the first round, the BS selects the CHs on the basis of communication distance from the entire network. However, in the other rounds the sensor nodes, including CHs send their residual energy information with the data packets. As the rounds proceed, each node consumes energy in sensing, transmission, and reception. At the end of each round the and CHs calculate their remaining energy and send this information to the BS with the data packets. Now, the BS is well aware of the remaining energies of all the and the CHs at the end of every round. So, the BS uses this information for selecting the for the next round. As a result, after each round the BS updates the residual energy information of each node in the network. Finally, the are selected on the basis of remaining energy and communication distance. If sometimes the nodes selected through this criterion are less than an optimal number, then the CHs from the list and the list with higher residual energy are preferred to be selected as . The optimal CH selection, and cluster formation procedures are shown in Figure 7.

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.