An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Wang, Jin; Gao, Yu; Liu, Wei; Wu, Wenbing; Lim, Se-Jung

doi:10.32604/cmc.2019.05450

Cited by 164 publications

(117 citation statements)

References 14 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…It also shows from another side that power load is closely related to temperature and date types. The efficiency of network can be further improved by adapting the optimization methods [58][59][60][61][62]. Figure 11.…”

Section: Prediction Resultsmentioning

confidence: 99%

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Pan

Chu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load. Appl. Sci. 2019, 9, 4514 2 of 22 the process. The algorithm is an effective way to solve global optimization problems, and it has the characteristics of generality, stability, and fast convergence. It includes two criteria, exploration and exploitation. Exploitation reflects the ability of finding the best around a good range, while exploration reflects the ability of searching for new range. At the beginning, it should search the whole range as much as possible, then through using exploitation it searches more carefully around the good solution. But they are contradictory. Too small exploration leads to convergence too fast and easy falling into local optimum; however, too small exploitation makes the algorithm converge too slowly.The No Free Lunch (NFL) theorem considers that there is no meta-heuristic algorithm applying for all optimization problems [1,2]. In other words, an algorithm shows very promising results on a set of issues, but it doesn't perform well on another set of issues. So it needs putting forward a new algorithm to get high performance in certain specific areas.Over the past decades, a large number of meta-heuristics are inspired by natural behaviors [3-5], such as, Genetic Algorithm (GA) [6][7][8], Differential Evolution (DE) Algorithm [9-12], Grey Wolf Optimizer (GWO) Algorithm [13-17], Particle Swarm Optimization (PSO) Algorithm [18-21], Artificial Bee Colony (ABC) Algorithm [22-24], Cat Swarm Optimization (CSO) [25-27], Artificial Fish Swarm Algorithm (AFSA) [28,29], Ant Colony Optimization (ACO) Algorithm [30-34], Shuffled Frog Leaping Algorithm (SFLA) [35-40], Biogeography Based Optimization (BBO) Algorithm [41-43], QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm [44][45][46][47] and so on. Because they all have some defects, many researchers also introduce hybrid algorithms to improve the defects [48][49][50][51][52][53][54][55].The rest of the paper is organized as follows: some related research works are described in the Secti...

show abstract

Section: Prediction Resultsmentioning

confidence: 99%

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Pan

Chu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The rotation of CHs helps in uniform distribution of the traffic across the network. This approach enhances the lifetime of WSNs up to some extent, however, at the same time it generates energy holes, particularly, in WSNs with a single sink module …”

Section: Literature Reviewmentioning

confidence: 99%

“…This approach enhances the lifetime of WSNs up to some extent, however, at the same time it generates energy holes, particularly, in WSNs with a single sink module. 26 To solve this issue, different methods have been proposed in the literature such as assistance approaches, traffic compression, and aggregation. 27,28 The assistance approaches solve this issue by the deployment of nodes having higher power and transmission capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

View full text Add to dashboard Cite

Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

show abstract

“…Wireless sensor network (WSN) refers to a set of spatially dispersed and dedicated sensors for recording and monitoring the physical conditions and organizing the data collected at a central location [1,2]. WSNs are a low-cost network that is widely used in numerous applications [3][4][5]. The needed environmental information like temperature, light, sound, humidity, wind, air, and water pollution levels could be captured and measured by sensor nodes of WSN [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

Chu

Dao

Pan

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

Wireless sensor network (WSN) has been paid more attention by scholars due to the practical communication of a system of devices to transfer information gathered from a monitored field through wireless links. Precise and accurate data of aggregating messages from sensor nodes is a vital demand for a success WSN application. This paper proposes a new scheme of identifying the correctness data scheme for aggregating data in cluster heads in hierarchical WSN based on naive Bayes classification. The collecting environmental information includes temperature, humidity, sound, and pollution levels, from sensor nodes to cluster heads that classify data fault and aggregate and transfer them to the base station. The collecting data is classified based on the classifier to aggregate in the cluster head of WSN. Compared with some existing methods, the proposed method offers an effective way of forwarding the correct data in WSN applications.

show abstract

An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Cited by 164 publications

References 14 publications

New Hybrid Algorithms for Prediction of Daily Load of Power Network

New Hybrid Algorithms for Prediction of Daily Load of Power Network

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification

Contact Info

Product

Resources

About