HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

Maratha, Priti; Gupta, Kapil

doi:10.1007/s11277-022-09550-z

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…( 13) is maximized by minimizing the negative log-likelihood function 𝐾(𝑋, 𝐼) in Eq. (15). The gradient descent [26], iterations in Eqs.…”

Section: Non-negative Matrix Factorization (Nmf)mentioning

confidence: 99%

“…IIoT Model ✘ ✘ ✘ ✔ [5] Feature Extraction (NMF) ✘ ✘ ✘ ✔ [8] IDAF-FIT Algorithm ✔ ✔ ✘ ✘ [9] Fuzzy Logic for Routing ✔ ✔ ✘ ✘ [10] F-LEACH ✔ ✔ ✘ ✘ [11] Fog-Assisted EHDA Scheme ✔ ✔ ✘ ✘ [14] Hybrid EPO Algorithm ✔ ✘ ✘ ✘ [15] HFLSBC ✔ ✔ ✘ ✘ [17] Energy-Efficient CH Selection ✔ ✘ ✘ ✘ [18] FEWO Algorithm…”

Section: Introductionmentioning

confidence: 99%

“…It directly influences the lifespan and performance of sensor nodes, making it a crucial metric for CH selection. Additionally, distancerelated metrics, such as the distance to the base station or proximity to the CH, are commonly employed, as evident in [2,9,15,22]. The consideration of node-specific attributes, like mobility, node degree, and energy levels, is also prominent in works like [3,8,15].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, distancerelated metrics, such as the distance to the base station or proximity to the CH, are commonly employed, as evident in [2,9,15,22]. The consideration of node-specific attributes, like mobility, node degree, and energy levels, is also prominent in works like [3,8,15]. These factors collectively contribute to the overall efficiency and load balancing in WSNs.…”

Section: Introductionmentioning

confidence: 99%

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CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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Wireless Sensor Networks (WSNs) play a pivotal role in remote monitoring, surveillance, and Internet of Things (IoT) applications. The efficient utilization of battery-powered sensor nodes in WSNs, given their limited power capacity, is crucial for successful data transmission. Conventional clustering algorithms while efficient in clustering, often lacks the efficient management of data generated by sensor nodes, leading to redundant data in applications like IoT leading to reduced network lifetime. To overcome this issue, this paper introduces a novel approach, named CCOA-DC (Improved Coati Optimization with Cognitive Factor (CCOA) through Data Compression (DC)), in clustering heterogeneous aggregated WSN data. The research unfolds in two novel phases. Initially, a non-negative matrix factorization (NMF) model is introduced data compression for clustering, addressing the challenge of data transmission and energy efficiency. Subsequently, the performance is enhanced through load balancing, featuring dynamic cluster head selection via Improved Coati Optimization (COA) with cognitive factor (C), denoted as CCOA. A distinctive aspect is the incorporation of the NMF data compression technique in both clustering and cluster head selection processes, introducing an energy-efficient, load-balanced, and compressed data aggregation mechanism. The proposed CCOA-DC undergoes rigorous testing, comparing its performance against existing models to validate its superiority. Comparative analyses with renowned models such as TCBDGA, HEED, and FEEC-IIR underscore the distinct advantages of CCOA-DC. Notably, it achieves a reduction of 78.57% of packet loss ratio compared to FEEC-IIR model. The model achieves high packet delivery ratio which is 98.67%, and shows optimized energy consumption of 68.01% Joules. This novel compression-based metaheuristic data aggregation algorithm showcases its effectiveness in addressing the energy conservation challenge, affirming its prominence in the area of WSNs based IoT applications.

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“…( 13) is maximized by minimizing the negative log-likelihood function 𝐾(𝑋, 𝐼) in Eq. (15). The gradient descent [26], iterations in Eqs.…”

Section: Non-negative Matrix Factorization (Nmf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CCOA-DC: A Novel Optimization with NMF Data Compression in WSN Data Aggregation

2024

IJIES

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“…Fuzzy logic has been proven to be applicable to almost all aspects of clustering from CH selection, cluster radius calculation, CM joining cluster, next-hop CH finding, re-clustering, etc. [ 16 , 21 , 27 ]. However, routing protocols are usually responsible for finding suitable next-hop CHs or paths for data transmission in multi-hop mode [ 16 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Wang

2023

Sensors

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Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors’ residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH’s number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

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