Uneven Clustering Routing Algorithm Based on Optimal Clustering for Wireless Sensor Networks

Li, Jianpo; Huo, Junyuan

doi:10.12720/jcm.11.2.132-142

Cited by 11 publications

(12 citation statements)

References 19 publications

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“…Some significant factors such as the distance between a certain node and sink node, the node density, and the rate of energy consumption are not considered. In our previous works, the UCRA [18] algorithm is proposed. The approach for electing the cluster head is described as a probability formula as…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

“…In the stage of cluster construction, the cluster head will broadcast cluster information to the neighbour nodes when it is elected. The uneven clustering algorithm EEUC [20] is complex in calculating the cluster radius. On the other hand, it does not take the influence of node density into consideration.…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

“…On the other hand, it does not take the influence of node density into consideration. In [18], we calculate the cluster radius as…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

P = \max ()(, \frac{k}{N} * \frac{E_{res}}{E_{\max}} * \frac{E_{avecons}}{E_{con}} * (η_{1} \frac{N_{N N}}{N_{A}} + false(1 - η false) \frac{d_{t o B S_M A X} - d_{toBS}}{d_{t o B S_M A X} - d_{t o B S_M I N}}), P_{\min})

where

η_{1}

is a constant coefficient between 0 and 1,

E_{res}

is the residual energy of node,

E_{\max}

is the maximum energy storage of node,

N_{N N}

is the number of the neighbour nodes,

N_{A}

is the number of the surviving nodes,

N_{N N} / N_{A}

is called node degree which is treated as a measurement of density,

E_{con}

is the energy consumption of the node in the previous round,

E_{avecons}

is the average energy consumption of all neighbour nodes in the previous round obtained by nodes inter‐communication,

E_{avercons} / E_{con}

represents the energy consumption rate of node in its neighbour area in order to ensure the nodes with lower energy consumption rate get larger probability to become cluster head,

p_{\min}

is a certain threshold (e.g.…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

“…However, it will waste a lot of energy due to repeated iterations. A cluster head election strategy based on energy iteration is proposed in the previous works [19]. It reduces the iteration number by considering the multi‐parameters as the basis for iteration straightly rather than probability.…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

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Fuzzy power‐optimised clustering routing algorithm for wireless sensor networks

Hou

et al. 2017

IET wirel. sens. syst.

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Transmission power control is an effective method to reduce energy consumption for wireless sensor networks. However, the current algorithms of power control have relatively low accuracy. At the same time, the parameters cannot be adjusted dynamically. In order to improve the energy utilisation as well as data transmission efficiency and balance the load, therefore, a fuzzy power-optimised clustering routing algorithm is proposed in this study. The algorithm optimises the iteration radius and classifies the sensor nodes into different categories according to their node degree. Then select the cluster head by multi-parameter iteration adaptively among the same category, and optimise the cluster structure with a comprehensive consideration of parameters such as degree of centralisation, distance between node and base station and so on. Finally, fuzzy control is used to adjust the transmission power of cluster nodes dynamically to minimise the energy consumption. Simulation results show that when the average cluster radius R L = 60, weight of election parameters α = 0.2, adjustment parameter of cluster radius η = 0.7, compared with other similar algorithms, the proposed algorithm prolongs the lifetime by at least 42.2% and increases the amount of data packets by at least 40.1%.

show abstract

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

“…On the other hand, it does not take the influence of node density into consideration. In [18], we calculate the cluster radius as…”

Section: Basic Process Of Wsn Routing and Related Workmentioning

confidence: 99%

P = \max ()(, \frac{k}{N} * \frac{E_{res}}{E_{\max}} * \frac{E_{avecons}}{E_{con}} * (η_{1} \frac{N_{N N}}{N_{A}} + false(1 - η false) \frac{d_{t o B S_M A X} - d_{toBS}}{d_{t o B S_M A X} - d_{t o B S_M I N}}), P_{\min})

where

η_{1}

is a constant coefficient between 0 and 1,

E_{res}

is the residual energy of node,

E_{\max}

is the maximum energy storage of node,

N_{N N}

is the number of the neighbour nodes,

N_{A}

is the number of the surviving nodes,

N_{N N} / N_{A}

is called node degree which is treated as a measurement of density,

E_{con}

is the energy consumption of the node in the previous round,

E_{avecons}

is the average energy consumption of all neighbour nodes in the previous round obtained by nodes inter‐communication,

E_{avercons} / E_{con}

represents the energy consumption rate of node in its neighbour area in order to ensure the nodes with lower energy consumption rate get larger probability to become cluster head,