Cluster-Head Restricted Energy Efficient Protocol (CREEP) for Routing in Heterogeneous Wireless Sensor Networks

Dutt, Suniti; Agrawal, Sunil; Vig, Renu

doi:10.1007/s11277-018-5649-x

Cited by 71 publications

(45 citation statements)

References 30 publications

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“…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. In LA‐MHR, a learning automata–based solution is proposed for cluster head selection in which the sensor nodes are divided into four categories based on their available energy and the suitable cluster head in each round is selected from these categories.…”

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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“…In addition, Dutt et al [22], Tanwar et al [23], and Hong et al [24] developed CREEP (cluster-head restricted energy- Journal of Sensors efficient protocol), LA-MHR (automata-based multilevel heterogeneous routing), and EDCS (efficient and dynamic clustering scheme) protocols, respectively, for heterogeneous clustering networks. CREEP focuses on reducing the number of cluster heads per round to improve network lifetime through two-level heterogeneity.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient QoS-Aware Intelligent Hybrid Clustered Routing Protocol for Wireless Sensor Networks

Singh

2019

Journal of Sensors

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A wireless sensor network consists of numerous low-power microsensor devices that can be deployed in a geographical area for remote sensing, surveillance, control, and monitoring applications. The advancements of wireless devices in terms of userfriendly interface, size, and deployment cost have given rise to many smart applications of wireless sensor networks (WSNs). However, certain issues like energy efficiency, long lifetime, and communication reliability restrict their large scale utilization. In WSNs, the cluster-based routing protocols assist nodes to collect, aggregate, and forward sensed data from event regions towards the sink node through minimum cost links. A clustering method helps to improve data transmission efficiency by dividing the sensor nodes into small groups. However, improper cluster head (CH) selection may affect the network lifetime, average network energy, and other quality of service (QoS) parameters. In this paper, a multiobjective clustering strategy is proposed to optimize the energy consumption, network lifetime, network throughput, and network delay. A fitness function has been formulated for heterogenous and homogenous wireless sensor networks. This fitness function is utilized to select an optimum CH for energy minimization and load balancing of cluster heads. A new hybrid clustered routing protocol is proposed based on fitness function. The simulation results conclude that the proposed protocol achieves better efficiency in increasing the network lifetime by 63%, 26%, and 10% compared with three well-known heterogeneous protocols: DEEC, EDDEEC, and ATEER, respectively. The proposed strategy also attains better network stability than a homogenous LEACH protocol.

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“…When 0.5 ≤ α i < 1, choosing a closer node j can extend the network lifetime effectively. Equation (9) shows that when α i is small, α i C ij /Ê i can be adjusted to be small, and the part that affects the U(i, j) value is mainly (1 − α i )C js /Ê j . The node i selecting node j closer to sink node is advantageous for lowering energy consumption of the node j and vice versa.…”

Section: Routing Strategymentioning

confidence: 99%

“…Among them, routing and charging technology are two common ways. Routing energy-saving algorithms [6][7][8][9] are used to reduce nodes' energy consumption by all means. However, the method cannot fundamentally solve the energy constraint problems; that the capacity of sensor battery is always small and needs to be replaced frequently for long-term operation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Energy Balanced Routing Strategy for Wireless Rechargeable Sensor Networks

et al. 2019

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The network lifetime of wireless rechargeable sensor network (WRSN) is commonly extended through routing strategy or wireless charging technology. In this paper, we propose an optimization algorithm from the aspects of both charging and routing process. To balance the network energy in charging part, node’s charging efficiency is balanced by dynamically planning charging point positions and the charging time is allocated according to the energy consumption rate of nodes. Moreover, the routing method is adapted to the node’s charging efficiency. The adaptive routing strategy assigns more forwarding tasks to nodes that can get more energy during the charging phase, and makes the data packets transmit farther away, thus reducing the average hops and energy consumption of the network. Finally, the simulation results reveal that the proposed algorithm has certain advantages in prolonging the network lifetime, reducing the average hop counts and balancing the energy of each node.

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Cluster-Head Restricted Energy Efficient Protocol (CREEP) for Routing in Heterogeneous Wireless Sensor Networks

Cited by 71 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

Energy-Efficient QoS-Aware Intelligent Hybrid Clustered Routing Protocol for Wireless Sensor Networks

Adaptive Energy Balanced Routing Strategy for Wireless Rechargeable Sensor Networks

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