Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks

Hamzah, Abdulmughni Y.; Shurman, Mohammad; Al-Jarrah, Omar; Taqieddin, Eyad

doi:10.3390/s19030561

Cited by 116 publications

(80 citation statements)

References 54 publications

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“…Hamzah et al [33] proposed a fuzzy logic-based energy-efficient clustering for wireless sensor networks on the basis of minimum separation distance imposition between cluster heads. In this work, the authors emphasized the fact that if the clustering algorithm exploits more energy affecting factors, then the clustering will be more efficient.…”

Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

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Section: Fuzzy Logic-based Wsn Clusteringmentioning

confidence: 99%

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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“…In TL‐LEACH as an extension to LEACH protocol, the mechanism for rotating the cluster head role is modified to better distribute the energy consumption in the network. Hamzah et al utilized the fuzzy logic based on minimum separation distance to select the best cluster head in each round. In Sasikumar and Khara, two centralized and distributed k ‐means clustering algorithm is implemented as an alternative to the LEACH‐based protocols. From the perspective of energy efficiency mechanism, three distinct directions in the research works can be identified: sensor lifetime enhancement, load balancing, and network lifetime maximization.…”

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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“…Hamzah et al [10] proposed the Fuzzy Logic-based Energy-Efficient Clustering for WSNs based on the minimum partition Distance (FL-EEC/D) enforcement between CHs. In this protocol, the Gini index was considered for measuring the clustering algorithm's energy efficiency in terms of their ability to balance the allocation of power via sensor nodes.…”

Section: Literature Surveymentioning

confidence: 99%

Traffic-Aware Reliability Enhanced Cluster Head Selection Based Routing for Heterogeneous WSNs

Tamilselvi*,

Rizwana

2020

IJITEE

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One of the most important challenges for Wireless Sensor Networks (WSNs) is improving the lifespan of the network. This is tackled by the clustering technique that simplifies the routing in WSNs. The clustering strategy only takes into account a small number of nodes randomly selected as Cluster Heads (CHs). CH’s main task is to collect the sensed data from their member nodes, aggregate it and transmit it to the sink or Base Station (BS). Several clustering strategies are proposed to significantly increase energy efficiency and lifespan of the network. Among those, Reliability-based Enhanced Technique for Ordering of Preference by Similarity-Ideal-Solution (RETOPSIS) using fuzzy logic model promotes effective CHs selection and network lifespan with the consideration of five different criteria like remaining power, distance between neighboring nodes, distance between the sink and CHs, energy efficiency and availability of adjacent nodes. This algorithm is only appropriate for energy heterogeneity scenarios. Conversely, under multi-level traffic heterogeneities, routing is not successful due to the considered criteria. For WSNs with heterogeneity scenarios, considering traffic as well as energy heterogeneities is the most important to maximize the network’s lifespan. Hence, a Traffic-Aware RE-TOPSIS (TARE-TOPSIS) for an effective CH selection protocol is proposed in this article which considers the traffic of nodes along with their initial and residual power. This protocol provides the framework for selecting CH under multilevel traffic and energy heterogeneities. This considers the node’s initial energy, traffic load including five criteria in RE-TOPSIS to efficiently choose the CH during each cycle. At last, simulation outcomes exhibit that the proposed technique outperforms the RE-TOPSIS technique in terms of network lifespan, average energy consumption, throughput and Packet Delivery Ratio (PDR).

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Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks

Cited by 116 publications

References 54 publications

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

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

Traffic-Aware Reliability Enhanced Cluster Head Selection Based Routing for Heterogeneous WSNs

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