An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Yang, Simon X.

doi:10.1007/s11235-017-0372-6

Cited by 39 publications

(24 citation statements)

References 30 publications

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“…CRDP is a distributed clustering protocol for heterogeneous networks in which the cluster heads are selected based on the remaining energy and distance to base station. In Yang et al, an unequal clustering method is proposed for the heterogeneous WSN where energy consumption in the clusters is balanced through finding the optimal number of nodes in each cluster. CREEP protocol is a distributed energy‐efficient clustering method that proposes a new threshold for cluster head selection in large‐scale heterogeneous WSNs.…”

Section: Related Workmentioning

confidence: 99%

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

Mostafavi

Hakami

2020

Int J Communication

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Energy efficient resource management is critical for prolonging the lifetime of wireless sensor networks (WSNs). Clustering of sensor nodes with the aim of distributing the traffic loads in the network is a proven approach for balanced energy consumption in WSN. The main body of literature in this topic can be classified as hierarchical and distance-based clustering techniques in which multi-hop, multi-level forwarding, and distance-based criteria are utilized for categorization of sensor nodes. In this study, we propose the approximate rank-order wireless sensor networks (ARO-WSNs) clustering algorithm as a combined hierarchical and distance-based clustering approach. Different from absolute distance, ARO-WSN algorithm utilizes a new rank-order distance measure for agglomerative hierarchical clustering. Specifically, for each sensor node, we generate a ranking order list by sorting all other sensor nodes in the neighborhood by absolute distance. Then, the rank-order distance of two sensor nodes is computed using their ranking orders. The designed algorithm iteratively group all sensor nodes into a small number of sub-clusters. The results show that ARO-WSN outperforms the competitive clustering algorithms in terms of efficiency and precision/recall. The lifetime of the network with the first node death criterion improved relative to LEACH, LEACH-C, LEACH with fuzzy descriptors, and BPA-CRP by 60%, 85%, 22%, and 18%, respectively, and with last node death criterion improved relative to K-means, LEACH, LEACH-C, and LEACH with fuzzy descriptors by 42%, 67%, 64%, and 24%, respectively. KEYWORDSapproximate rank-order, clustering, network lifetime, wireless sensor networks INTRODUCTIONWireless sensor networks (WSNs) have been utilized for a wide range of applications such as environmental monitoring, 1 target tracking, 2 health care monitoring, 3 and crisis management. 4 Sensors networks, as a group of collaborative sensors for a specific application, have made significant changes in the way that people interact with the environment. However, the WSNs face strict limitations in terms of communication bandwidth, battery, and computing power. These inherent constraints in sensor nodes have posed great challenges to the WSNs. Therefore, how to find cost-effective solutions to reduce energy consumption in WSN and efficiently exploit the scare resources in the network remains an important research area.Int J Commun Syst. 2020;33:e4313.wileyonlinelibrary.com/journal/dac

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Section: Related Workmentioning

confidence: 99%

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

Mostafavi

Hakami

2020

Int J Communication

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“…Zhao et al 15 introduced a new network structure model and combined the original energy consumption model to construct a new method to determine the optimal number of clusters. Yang et al 16 presented an unequal cluster-based routing scheme for WSNs with multi-level energy heterogeneity called UCR-H. It calculates the number of clusters in each unit by balancing energy consumption among the CHs in different units and finds the optimal number of units by minimizing the total energy consumption of inter-cluster forwarding.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient unequal clustering routing protocol for wireless sensor networks

Fang

Wei

2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks have drawn tremendous attentions from all fields because of their wide application. Maximizing network lifetime is one of the main problems in wireless sensor networks. This article proposes an energy-efficient routing protocol which adopts unequal clustering technology to solve the hot spots problem and proposes double cluster head strategy to reduce the energy consumption of head nodes in the clusters. In addition, to balance the energy consumption between cluster heads and cluster members, a hybrid cluster head rotation strategy based on time-driven and energy-driven is proposed, which can make the timing of rotation more reasonable and the energy consumption more efficient. Finally, we compare the proposed protocol with LEACH, DEBUC, and UCNPD by simulation experiments. The simulation results prove that our proposed protocol can effectively improve the performance in terms of network lifetime, energy consumption, energy balance, stability, and throughput.

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“…The radio model [ 30 , 32 , 33 ] adopted in this paper is shown in Figure 2 , and it contains a two-ray ground propagation model and free space model according to the distance from data transmitter to receiver.…”

Section: System Modelmentioning

confidence: 99%

“…In this section, the details of our protocol will be described and we call it a game theoretic approach for balancing energy consumption in clustered WSNs (GTAB). The procedure [ 30 , 32 ] of GTAB is composed of a one-time initialization phase and lots of repeated rounds which can further be divided into topology formation and steady-state phases, as shown in Figure 3 .…”

Section: The Protocol Detailsmentioning

confidence: 99%

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Yang

Xiong

et al. 2017

Sensors

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Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

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An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks

Cited by 39 publications

References 30 publications

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

An energy-efficient unequal clustering routing protocol for wireless sensor networks

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

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