Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks

Darabkh, Khalid A.; Odetallah, Saja M.; Al-qudah, Zouhair; Khalifeh, Ala’; Shurman, Mohammad

doi:10.1016/j.asoc.2019.03.025

Cited by 75 publications

(27 citation statements)

References 36 publications

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“…We compare the proposed ICR with existing protocols, LEACH [13], JCR [20], EA-DB-CRP [32], BPA-CRP [44], FBCFP [33], EGBM [45], MOFGSA [50], O-LEACH [46], and DL-LEACH [47]. The ICR protocol performs better than these protocols, specifically when comparing network lifetimes, and it is suitable for multihop IoTbased WSNs.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the most recent research efforts [32,33,44,45,50], the performance of the ICR protocol is better in terms of energy efficiency and the packet delivery ratio. The EA-DB-CRP protocol [32] follows all steps as the JCR protocol with an additional step of merging low-density clusters. Nevertheless, th process of merging requires more energy comparatively.…”

Section: Discussionmentioning

confidence: 99%

“…However, this model is based on the strong assumption that a node can adopt multiple transmission ranges. The comparison of the EA-DB-CRP protocol [32] and BPA-CRP protocol [44] with ICR is given in Fig. 15.…”

Section: Discussionmentioning

confidence: 99%

“…Most existing studies also treat clustering and routing as separate issues and cause hot-spot problems and unbalanced energy distributions among the nodes in WSNs [12,13]. The authors in [20,26,32,33,34] argue that routing and clustering are interlinked issues and must not be treated separately.…”

Section: Related Workmentioning

confidence: 99%

“…Very few studies address clustering and routing as a single unified problem in WSNs [23,26,32]. For example, the JCR protocol [20] uses a back-off timer and gradient routing to develop a network topology for data collection in a large-scale WSN.…”

Section: Related Workmentioning

confidence: 99%

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Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

Alharbi

Kolberg

Zeeshan³

2020

Preprint

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Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multihop communication and a shorter transmission range. Researchers propose different but isolated clustering and routing solutions that are inefficient in terms of energy efficiency and network connectivity in IoT-based WSNs. In this work, we emphasize the importance of considering the context of IoT applications that have further requirements for dedicated data collection per node. We address two interlinked issues, clustering and routing, in a large-scale IoT-based WSN. We propose an improved clustering and routing (ICR) protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. This clustering also develops a strong network backbone that provides fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols, Joint Clustering and Routing (JCR), Low Energy Adaptive Hierarchical Clustering (LEACH) and other recent protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

Alharbi

Kolberg

Zeeshan³

2020

Preprint

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A mobile sink path planning for wireless sensor networks based on priority‐ordered dependent nonparametric trees

Yalçın

Erdem

2020

Int J Communication

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SummaryThe professional design of the routing protocols with mobile sink(s) in wireless sensor networks (WSNs) is important for many purposes such as maximizing energy efficiency, increasing network life, and evenly distributing load balance across the network. Moreover, mobile sinks ought to first collect data from nodes which have very important and dense data so that packet collision and loss can be prevented at an advanced level. For these purposes, the present paper proposes a new mobile path planning protocol by introducing priority‐ordered dependent nonparametric trees (PoDNTs) for WSNs. Unlike traditional clustered or swarm intelligence topology‐based routing methods, a topology which has hierarchical and dependent infinite tree structure provides a robust link connection between nodes, making it easier to reselect ancestor nodes (ANs). The proposed priority‐ordered infinite trees are sampled in the specific time frames by introducing new equations and hierarchically associated with their child nodes starting from the root node. Hence, the nodes with the highest priority and energy that belong to the constructed tree family are selected as ANs with an opportunistic approach. A mobile sink simply visits these ANs to acquire data from all nodes in the network and return to where it started. As a result, the route traveled is assigned as the mobile path for the current round. We have performed comprehensive performance analysis to illustrate the effectiveness of the present study using NS‐2 simulation environment. The present routing protocol has achieved better results than the other algorithms over various performance metrics.

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Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Juneja

2021

Int J Communication

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A sensor network is a situation-based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load-balanced and fuzzy-ACO (DLB-fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree-and energy-balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster-based hierarchical routing. Communication is performed at two levels. In the first level, node-to-cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy-integrated ACO method is applied for inter-cluster route formation. The route optimization is here performed under fuzzy-based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed loadbalanced protocol is analyzed against the LEACH, LEACH-C, LEACH-CC, M-LEACH, Fuzzy-PSO, Fuzzy-ACO, Fuzzy-Cuckoo, and FMCB-ER protocols.The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols.

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Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks

Cited by 75 publications

References 36 publications

Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

A mobile sink path planning for wireless sensor networks based on priority‐ordered dependent nonparametric trees

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

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