Geometric Analysis-Based Cluster Head Selection for Sectorized Wireless Powered Sensor Networks

Choi, Hyun-Ho; Muy, Sengly; Lee, Jung-Ryun

doi:10.1109/lwc.2020.3044902

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…Similar works on EH-WSN are reported in studies. [35][36][37][38][39] However, it is observed that CH selection is a NP-hard problem, and the meta-heuristic approaches used for CH selection in the existing literature are too old. They lack exploration and high-convergence capabilities.…”

Section: Related Workmentioning

confidence: 99%

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Gupta,

Kumar Rout

2024

Int J Communication

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SummaryWith the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)‐enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH‐enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH‐enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH‐enabled energy‐efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster‐based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH‐enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques.

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Section: Related Workmentioning

confidence: 99%

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Gupta,

Kumar Rout

2024

Int J Communication

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“…Tao et al [23] presented an unequal clustering algorithm based on fuzzy logic theory to increase the energy efficiency of the system by reducing power consumption of the sensors. Choi et al [24] presented a model to enhance the transmission rate of the data. Lata et al [25] developed a LEACH protocol with fuzzy logic based algorithm to increase the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we compare the performance of the proposed POHDA approach with three existing and well-known algorithms: HCCS-WSN [23], FMCA-WSN [24], and APCC-WSN [25]. To evaluate the performance of POHDA-HWSN, we analyze specific parameters such as energy efficiency and packet delivery ratio.…”

Section: Performance Analysesmentioning

confidence: 99%

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

Al-Heeti,

Hammad,

Mustafa

2024

Bulletin EEI

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Heterogeneous wireless sensor network (HWSN) is a trending technology in both the industrial and academic sectors, consisting of a large number of interconnected sensors. However, higher energy consumption and delay are significant drawbacks of this technology in applications such as military, healthcare, and industrial automation. The main objective of this research is to enhance the energy efficiency of HWSN using a clustering technique. In this article, a novel approach, namely power optimization and hybrid data aggregation (POHDA), is proposed to address these challenges in HWSN. POHDA-HWSN focuses on power optimization and congestion avoidance through effective CH selection using hybrid data aggregation based on parameters such as residual energy, distance, mobility, threshold value of the node, and latency. By weight-based effective cluster head (CH) selection, the energy consumption, end-to-end delay, and overhead during communication are reduced in this network. The POHDA-HWSN approach considers specific parameters to compare the results and outcomes with earlier research such as HCCS-WSN, FMCA-WSN, and APCC-WSN. The results prove that the proposed POHDA-HWSN approach achieves higher energy efficiency and delivery ratio.

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“…Models that propose use of "distanceand energy-constrained k-means clustering scheme" (DEKCS) [14], "Bayesian Clustering with Collaborative Online Edge Caching" [15], "Coyote Optimization with Fuzzy Logic" (COFL) [16], Improved Soft-k-Means [17], and "Unequally Clustered Multiple Hop Routing via Fuzzy Logic" (UCM RFL) [18] are discussed, which assist in integration of multiple routing metrics for optimization of cluster formation performance under large-scale networks. Similar models are discussed in [19,20], which propose use of Geometric Analysis, and Nearest Neighbour Distance Distributions for enhancing routing under multiparametric optimization scenarios. But these models are highly complex, which limits their applicability to small & medium scale networks.…”

Section: Literature Reviewmentioning

confidence: 99%

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

Annaji M Kuthe

2023

IJRITCC

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To enhance energy level in WSN is a research requirement, which assists in improving their lifetime over a series of communications. To achieve this target, a various variety of clustering & sleep scheduling models are discussed by researchers. Most of these models deploy static clustering & sleep scheduling operations, which limits their applicability & scalability levels. Moreover, dynamic clustering & scheduling models are highly complex, which reduces temporal QoS performance under real-time use cases. In order to reduce the probability of these issues, this text discusses design of the proposed Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN. The proposed model initially deploys a Grey Wolf Optimization (GWO) Method for dynamic sleep scheduling via temporal performance analysis. The GWO Method models a fitness function that combines temporal usage levels, temporal Quality of Service (QoS), and temporal energy levels. Based on this modelling process, nodes were categorized into wake & sleep nodes, which were further clustered via destination-aware Fan Shaped Clustering (FSC), that assisted in improving QoS performance under multiple scenarios. The FSC Model was combined with a QoS-aware routing model, that assisted in selection of routing paths that can achieve low delay, high throughput, and high packet delivery with higher energy efficiency levels. Efficiency of the model was tested on node & network conditions, and its QoS performance was checked in terms of communication delay, consumption of energy, level of throughput, and Packet Delivery Ratio (PDR) levels. On the basis of these comparative evaluations, it is observed that the new proposed model is able to enhance end-to-end delay by 8.5%, reduce level of energy by 15.5%, while increasing throughput by 8.3%, and PDR by 1.5%, thus making it useful for a different real-time conditions.

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Geometric Analysis-Based Cluster Head Selection for Sectorized Wireless Powered Sensor Networks

Cited by 14 publications

References 12 publications

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network

SSFSCE: Design of a Sleep Scheduling based Fan Shaped Clustering Model to enhance working Energy Efficiency of WSN

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