Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network

Agbehadji, Israel Edem; Millham, Richard; Abayomi, Abdultaofeek; Jung, Jason J.; Fong, Simon; Frimpong, Samuel

doi:10.1016/j.asoc.2021.107171

Cited by 30 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e DBSCAN algorithm can efficiently extract clusters of arbitrary shape and correctly identify noise points and outliers, but with higher spatiotemporal complexity than BIRCH. In addition, both the birch algorithm and the DBSCAN algorithm require users to provide several threshold parameters, and the selection of parameters directly affects the clustering effect [20][21][22].…”

Section: Algorithm Analysismentioning

confidence: 99%

DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Zhang

2022

Security and Communication Networks

View full text Add to dashboard Cite

In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious.

show abstract

Section: Algorithm Analysismentioning

confidence: 99%

DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Zhang

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Existing nature-inspired algorithms research primarily addresses the following areas of research: optimization [1, 2, 60, 61] using metaheuristics [62] or heuristics [63] approaches; greening processes, for example greening the supply chain [64], smart energy management [65], data center energy efficiency [66]; energy efficiency [67] and energy optimization [68] in wireless sensor network clustering. Our critical literature review has shown that to date, there is no research on energy efficient natureinspired algorithms and thus, our research aims to address this identified gap.…”

Section: Nature-inspired Algorithms and Energy Efficiencymentioning

confidence: 99%

Analyzing energy consumption of nature-inspired optimization algorithms

Jamil

Kor

2022

GRN TECH RES SUSTAIN

View full text Add to dashboard Cite

Nature-Inspired Optimization (NIO) algorithms have become prevalent to address a variety of optimization problems in real-world applications because of their simplicity, flexibility, and effectiveness. Some application areas of NIO algorithms are telecommunications, image processing, engineering design, vehicle routing, etc. This study presents a critical analysis of energy consumption and their corresponding carbon footprint for four popular NIO algorithms. Microsoft Joulemeter is employed for measuring the energy consumption during the runtime of each algorithm, while the corresponding carbon footprint of each algorithm is calculated based on the UK DEFRA guide. The results of this study evidence that each algorithm demonstrates different energy consumption behaviors to achieve the same goal. In addition, a one-way Analysis of Variance (ANOVA) test is conducted, which shows that the average energy consumption of each algorithm is significantly different from each other. This study will help guide software engineers and practitioners in their selection of an energy-efficient NIO algorithm. As for future work, more NIO algorithms and their variants can be considered for energy consumption analysis to identify the greenest NIO algorithms amongst them all. In addition, future work can also be considered to ascertain possible relationships between NIO algorithms and the energy usage of hardware resources of different CPU architectures.

show abstract

“…Identi-fying these communities is crucial to simulating the disease spread [36], especially for a highly infectious virus like SARS-CoV-2 [55], and to determining optimal vaccination strategies. Much effort has been given to developing community-detection algorithms in social networks [1,53]. In this work, we make use of the Fluid community detection algorithm proposed by [53], which is advantageous for sparse graphs since the algorithm complexity is linear to the number of non-zero edges in the network, i.e.…”

Section: Numerical Experiments In Real-world Social Contact Network 41 Assumptions and Preliminary Analysismentioning

confidence: 99%

“…If the body is eventually exposed to such disease-causing germs, it is ready to kill them instantly, avoiding illness. By the end of July 2021, nearly 300 vaccine candidates for COVID-19 are currently in trials 1 , and several of them, such as AstraZeneca, Pfizer, Moderna and Gamaleya, have already been distributed in all countries to protect individuals. No other vaccine in human history has been so eagerly anticipated, especially given that until now no drugs are demonstrated to be available to treat COVID- 19.…”

Section: Introductionmentioning

confidence: 99%

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Cheng

Arcucci

Pain

et al. 2021

Preprint

View full text Add to dashboard Cite

Vaccination strategy is crucial in fighting against the COVID-19 pandemic. Since the supply is limited, contact network-based interventions can be most powerful to set an optimal strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamical systems where the contact networks are highly time-variant. Furthermore, numerous mutations of SARS-CoV-2 impact considerably the current infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from the assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamical networks collected in a high school, following by sequential multi-layer networks, generated relying on the Barabasi-Albert model emulating the department of Computing at Imperial College London in the UK as an example.

show abstract

Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network

Cited by 30 publications

References 10 publications

DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Analyzing energy consumption of nature-inspired optimization algorithms

Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies

Contact Info

Product

Resources

About