Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks

Kadiravan, G.; Sujatha, P.; Asvany, T.; Punithavathi, R.; Elhoseny, Mohamed; Pustokhina, Irina V.; Pustokhin, Denis A.; Shankar, K.

doi:10.32604/cmc.2021.013034

Cited by 23 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In doubles, when inputting tactical information in each match, we must reselect the basic information such as the formation and batting order of both players. e information collection system completes the comprehensive collection of tennis game tactical information and provides the basis for the exploration and analysis of the tactical data in the game [20].…”

Section: Tennis Match Information Collectionmentioning

confidence: 99%

Application of Data Mining in Offensive Tactical Research of Tennis Match

Yang

2022

Mobile Information Systems

View full text Add to dashboard Cite

With the continuous development and growth of information technology and data scale, data mining technology has gradually matured and applied in various fields. With the continuous improvement in sports information level, the sports data are also increasing. It is the general trend to use data mining technology in the field of sports data analysis. In this article, data mining technology is used in the research of offensive tactical data of tennis, which aims to provide scientific data support for tactical decision-making in tennis training and competition and improve the winning rate of tennis competition. Firstly, based on the requirement analysis, the goal and structure framework of the tennis tactical analysis system are determined. The main functions of the system include tactical data collection, tactical statistics, and tactical comprehensive analysis. Then, the data collection ability, speed, and tactical statistic function of the tennis tactical analysis system are tested. The accuracy rate of data collection for five common offensive tactics in 10 matches of the tennis tactical analysis system is 94.4%, 97.1%, 100%, 97.4%, and 96.3%, respectively. This shows that the tennis tactical analysis system developed in this article can accurately record the tactical data in the game in real time. After the test, the system is used to analyze the tactics used by a tennis player in 2015 and analyze the characteristics of various tactics of the serve round and receive round. The results show that W-C-C is the main tactic of the serve round in the singles match, with the utilization rate of 100% and the scoring rate of W-C-C-Q tactics of 100%; the most important landing area is the inner corner of the right area, with the distribution rate of 30% and the scoring rate of 73.33%. The tactics of receiving round are mainly C-C-C-C tactics, with the utilization rate of 66.67% and the scoring rates of C-C-C-C tactics and C-Q-C-C tactics are 100%; the most important landing area is the middle road, with the distribution rate of 41.67% and the scoring rate of 46.67%. To sum up, the application of data mining in the research of offensive tactics in tennis matches has high analysis efficiency and accuracy, which is worthy of promotion.

show abstract

Section: Tennis Match Information Collectionmentioning

confidence: 99%

Application of Data Mining in Offensive Tactical Research of Tennis Match

Yang

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…and μ Ãx (x) = Every secondary Membership Function (MFs) is engaged to an interval set, whereas the primary membership remains triangular or trapezoidal [24]. The output variable denotes the chance of becoming CH which can be partitioned into six stages such as very weak, weak, little weak, lower medium, medium and higher medium [25].…”

Section: Fuzzification Processmentioning

confidence: 99%

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Justus¹,

Thirunavukkarasan²,

Dhayalini³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.

show abstract

“…1. Usually, the sensor nodes in WSN are randomly positioned in the target area and the nodes get initialized to aware of the neighboring nodes [19][20][21][22]. Followed by, the CPSOC based clustering technique is applied to cluster the nodes and elect an optimal set of CHs.…”

Section: The Proposed Cspoc-gflr Techniquementioning

confidence: 99%