Exploring the Effect of Various Cluster Structures on Energy Consumption and End-to-End Delay in Cognitive Radio Wireless Sensor Networks

Samir, Rasha; El-Mahallawy, Mohamed S.; Gasser, Safa M.; Zaher, Nawal A.

doi:10.1109/access.2018.2854872

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…In [31], an Advanced TEEN (ATEEN) clustering-based routing was proposed for CR-WSNs, forming a limit delicate energyeffective sensor organization that was both delicate and effective for stable clustering and energy efficiency. In [32], three distinct CR-WSN cluster structures were explored: an adjusted single-jump structure, a multibounce cluster structure, and a half-and-half cluster structure. At the first site, the impact of three buildings in various configurations on the movement of the defined region was investigated.…”

Section: A State-of-the-art Solutionsmentioning

confidence: 99%

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Panbude,

Iyer,

Nandgaonkar

et al. 2023

Eng. Technol. Appl. Sci. Res.

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Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

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Section: A State-of-the-art Solutionsmentioning

confidence: 99%

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Panbude,

Iyer,

Nandgaonkar

et al. 2023

Eng. Technol. Appl. Sci. Res.

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“…Thus, data aggregation in clustering techniques improves network efficiency by managing the resources of the network. Moreover, nodes that cannot reach the sink directly can relay their data through their cluster head [7].…”

Section: Introductionmentioning

confidence: 99%

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

Dine,

Finnerty,

Ohta

2024

IEEE Access

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Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.INDEX TERMS Clustering, linear programming, network lifetime, power optimization, wireless sensor network.

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“…However, the traditional static spectrum allocation policies have resulted in a scarcity of radio spectrum. [1][2][3]. An emerging paradigm that effectively reduces spectrum scarcity and makes better use of the spectrum is the cognitive radio sensor network (CRSN) [4] where the cognitive sensor nodes (CSNs) opportunistically access the spectrum bands which are already licensed to the primary users (PUs) without interfering with them with the help of equipped cognitive radio module.…”

Section: Introductionmentioning

confidence: 99%

Energy and Spectrum Aware Clustering Routing Protocol for Cognitive Radio Sensor Networks

2024

IJIES

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Cognitive radio technology is introduced to identify the spectrum holes dynamically. Such dynamic spectrum access operations cause the nodes in the cognitive radio sensor networks (CRSNs) into energy depletion problem. To handle this problem or to utilize the entire network's energy efficiently, clustering is found as one of the best solution. Even though uniform clustering mechanism reduce the energy consumption but it is not suitable when the network node density increases. Hence, this paper proposes a new clustering mechanism called as energy and spectrum aware clustering routing protocol (ESCRP) for CRSNs. Initially, the entire network is clustered nonuniformly into several clusters. Next, energy, channel availability rate, geographical and temporal correlation metrics are used to select the cluster head (CH) for each cluster. Finally, the collected information from each CM is transferred to the sink node through multi-hop communication between CHs. Extensive simulation experiments are carried out over the proposed ESCRP and the performance is measured with several performance metrics including Network lifetime and average energy consumed. From the experimental results, we observed that the weight combination 𝛼 =

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Exploring the Effect of Various Cluster Structures on Energy Consumption and End-to-End Delay in Cognitive Radio Wireless Sensor Networks

Cited by 6 publications

References 30 publications

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

Energy and Spectrum Aware Clustering Routing Protocol for Cognitive Radio Sensor Networks

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