Cluster Head Enhanced Election Type-2 Fuzzy Algorithm for Wireless Sensor Networks

Cuevas-Martínez, J.C.; Yuste-Delgado, Antonio Jesús; Triviño-Cabrera, Alicia

doi:10.1109/lcomm.2017.2703905

Cited by 28 publications

(17 citation statements)

References 9 publications

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“…The skip parameter tries to model the inertia of the network avoiding unnecessary CHs updates. In a previous work [5,15], we detected that the CHs election process tends to throw the same results from one round to the following one. Thus, it is commonly unnecessary to recompute the CHs.…”

Section: 3 Computation Of the Skip Valuementioning

confidence: 93%

“…Otherwise, the highest value of the interval, sh, is used in the comparison. This strategy was followed in some previous works such as [5,15]. This rule avoids that a node with a high output value could be selected continuously as a CH.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…These techniques show that the use of the Type-2 Fuzzy logic improves the performance when compared with Type-I fuzzy logic systems. This improvement is due to the best management of the existing uncertainty, which is intrinsically present in this kind of network, as it is shown in the Cluster Head Enhanced Election Type-2 fuzzy Algorithm (CHEETAH) [15] or Enhanced Unequal Distributed Type-2 Fuzzy Clustering Algorithm (EUDCF) in [5].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A New Centralized Clustering Algorithm for Wireless Sensor Networks

Cuevas-Martínez

Yuste-Delgado

Leon-Sanchez

et al. 2019

Sensors

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Clustering is presently one of the main routing techniques employed in randomly deployed wireless sensor networks. This paper describes a novel centralized unequal clustering method for wireless sensor networks. The goals of the algorithm are to prolong the network lifetime and increase the reliability of the network while not compromising the data transmission. In the proposed method, the Base Station decides on the cluster heads according to the best scores obtained from a Type-2 Fuzzy system. The input parameters of the fuzzy system are estimated by the base station or gathered from the network with a careful design that reduces the control message exchange. The whole network is controlled by the base station in a rounds-based schedule that alternates rounds when the base station elects cluster heads, with other rounds in which the cluster heads previously elected, gather data from their contributing nodes and forward them to the base station. The setting of the number of rounds in which the Base Station keeps the same set of cluster heads is another contribution of the present paper. The results show significant improvements achieved by the proposal when compared to other current clustering methods.

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Section: 3 Computation Of the Skip Valuementioning

confidence: 93%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A New Centralized Clustering Algorithm for Wireless Sensor Networks

Cuevas-Martínez

Yuste-Delgado

Leon-Sanchez

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequently, Karnik and Mendel proposed type-2 FLS in [19] and developed the details of the system method in [20] to cope with rule and linguistic uncertainties. Later, the type-2 FLS was widely used in many fields and has also been used in WSN lifetime analysis [17], [21], [22] and WSN security analysis [23]. Because the membership grade of an interval type-2 fuzzy set is an interval, the interval type-2 FLS has demonstrated a better ability to handle uncertainties than the type-1 FLS in [24].…”

Section: Figure 1 Unequal Clustering Network Scenariomentioning

confidence: 99%

“…Currently, unequal clustering algorithms based on FLS mostly use the traditional type-1 Mamdani FLS, which cannot effectively deal with the uncertainty in WSNs. To better address the rule uncertainties, some studies have begun to use the interval type-2 Mamdani FLS for CH elections [21], [22]. However, the Mamdani method was employed for fuzzy inference, where the inference process was relatively complicated and it is not a fast and efficient method [15].…”

Section: Related Workmentioning

confidence: 99%

An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

2020

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Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the "hot spot" problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

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An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

Xiao

Gan

Wang

et al. 2022

Int J Communication

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Summary In recent trends, wireless sensor network (WSN) is often used in harsh environments. When WSNs work, they need to deploy a large number of sensor nodes in the monitoring area. Due to the limited resources carried by the sensor nodes, the monitoring process of WSN will be limited by the energy of the sensor nodes. An energy‐efficient clustering algorithm combining interval type‐2 fuzzy logic and dual‐super‐cluster‐head (IT2FL‐DSCH) mechanism is proposed, aiming at reducing and balancing the energy consumption of sensor nodes. Considering the four fuzzy parameters, that is, cluster heads' (CHs') battery power, CHs' centrality, base station's (BS's) mobility, and the distance from CHs to BS, interval type‐2 fuzzy logic (IT2FL) is used to select the appropriate super cluster head (SCH). And SCH is the only way to communicate with the mobile BS, thus effectively using bandwidth. Then, the DSCH mechanism to balance the energy consumption of SCH is introduced in detail. In addition, we utilize mathematical derivation to obtain the supremum of the energy consumption of the SCH algorithm under different network distribution. Finally, the MATLAB simulation results show that this protocol outperforms low energy adaptive clustering hierarchy (LEACH), LEACH‐centralized (LEACH‐C), distributed energy efficient clustering (DEEC), WSNs using deep‐Q‐networks (DQN), and two‐tier distributed fuzzy logic based protocol (TTDFP) in terms of sensor nodes energy saving, network stability, and network lifetime.

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Cluster Head Enhanced Election Type-2 Fuzzy Algorithm for Wireless Sensor Networks

Cited by 28 publications

References 9 publications

A New Centralized Clustering Algorithm for Wireless Sensor Networks

A New Centralized Clustering Algorithm for Wireless Sensor Networks

An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

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