BPA-CRP: A balanced power-aware clustering and routing protocol for wireless sensor networks

Darabkh, Khalid A.; El-Yabroudi, Mohammad Z.; El-Mousa, Ali

doi:10.1016/j.adhoc.2018.08.012

Cited by 119 publications

(67 citation statements)

References 25 publications

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“…A wireless sensor network (WSN) comprises spatially circulated self-ruling sensors to agreeably screen and monitor physical or ecological conditions, out of which contain vibration, temperature, weight, sound, movement, or contaminations [1][2][3]. It presents a new generation of real-time embedded control instruments or approaches, for the most part, need precise and complete knowledge about the environment.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…To overcome these drawbacks, many extension over this algorithm has been proposed including LEACH‐C, EHE‐LEACH, LEACH‐B, W‐LEACH, H‐LEACH, LEACH‐GA, Q‐LEACH, and TL‐LEACH . Also, the notion of hierarchical clustering have been utilized in other research activities to extend the lifetime of WSN.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike LEACH protocol, a multi‐hop approach is utilized to minimize the communication overhead among the cluster heads and nodes. In BPA‐CRP, a layered architecture for the sensor network is proposed in which the sensor filed is divided into the equal‐sized layers with multiple clusters. Some relay nodes are placed in the network to reduce the communication energy between cluster heads and sensors.…”

Section: Related Workmentioning

confidence: 99%

“…This parameter depends on many factors such as digital coding, modulation, filtering, and signal propagation . We utilize the most popular energy consumption model, which is used in most of the WSN literature . In this model, the amount of energy consumed by radio transmitter is defined by the following equation:

E_{T x} false(k, d false) = ({, \begin{matrix} left center \\ array k . E_{e l e c} + k . E_{f s} . d^{2} & array if d < d_{o} \\ array k . E_{e l e c} + k . E_{m p} . d^{4} & array if d \geq d_{o} \end{matrix}),

where

E_{f s}

and

E_{m p}

represent the amplifier energy, respectively, in the model of free space channel (energy loss

d^{2}

) and in the model of multipath fading channel (energy loss

d^{4}

) multipath.…”

Section: The Proposed Aro‐wsn Approachmentioning

confidence: 99%

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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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“…The sensor node is comprised of a sensing unit, a processing unit, a transmission unit, and a power unit (Darabkh et al, 2019(Darabkh et al, , 2017a. The most crucial aspect of designing issues of software and hardware of WSNs is energy consumption (Ismail et al, 2015;Al-Zubi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

LBCH: Load Balancing Cluster Head Protocol for Wireless Sensor Networks

Al‐Zubi¹,

Abedsalam²,

Atieh³

et al. 2018

Informatica

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In recent years, Wireless Sensor Networks (WSNs) received great attention because of their important applications in many areas. Consequently, a need for improving their performance and efficiency, especially in energy awareness, is of a great interest. Therefore, in this paper, we proposed a lifetime improvement fixed clustering energy awareness routing protocol for WSNs named Load Balancing Cluster Head (LBCH) protocol. LBCH mainly aims at reducing the energy consumption in the network and balancing the workload over all nodes within the network. A novel method for selecting initial cluster heads (CHs) is proposed. In addition, the network nodes are evenly distributed into clusters to build balanced size clusters. Finally, a novel scheme is proposed to circulate the role of CHs depending on the energy and location information of each node in each cluster. Multihop technique is used to minimize the communication distance between CHs and the base station (BS) thus saving nodes energy. In order to evaluate the performance of LBCH, a thorough simulation has been conducted and the results are compared with other related protocols (i.e. ACBEC-WSNs-CD, Adaptive LEACH-F, LEACH-F, and RRCH). The simulations showed that LBCH overcomes other related protocols for both continuous data and event-based data models at different network densities. LBCH achieved an average improvement in the range of 2-172%, 18-145.5%, 10.18-62%, 63-82.5% over the compared protocols in terms of number of alive nodes, first node died (FND), network throughput, and load balancing, respectively.

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BPA-CRP: A balanced power-aware clustering and routing protocol for wireless sensor networks

Cited by 119 publications

References 25 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

LBCH: Load Balancing Cluster Head Protocol for Wireless Sensor Networks

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