A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

Khedr, Ahmed M.; Osamy, Walid; Salim, Ahmed; Abbas, Sohail

doi:10.1109/access.2020.3017488

Cited by 12 publications

(3 citation statements)

References 48 publications

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“…The user's interest will be reduced due to the inconsistency between the rules and expectations, more noninteresting attributes in the rules, or more redundant rules. For this problem, attribute classification analysis should be adopted to clarify the front and rear items of association rules of data mining [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

Shi

Zhu

2022

Computational Intelligence and Neuroscience

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In recent years, the amount of educational data in colleges and universities has increased rapidly. Each university has set up multiple courses to recruit talents. Students cannot choose courses. The emergence of data mining technology and its application in college teaching and curriculum has been preferred particularly to streamline these activities. When analyzing the correlation of courses based on data mining technology, we usually use the correlation between the scores of various subjects to analyze the correlation between courses. Correlation among various courses that are offered at colleges or universities is reflected through many different aspects such as factors or metrics, which are affecting course interest, course content, course arrangement, etc. In this article, we have thoroughly analyzed various factors that are affecting students’ interest in literature courses with the help of association rules of data mining technology. Through the collected original data, this article uses Apriori algorithm to screen the association rules affecting students’ interest in literature courses and combines them with the current teaching situation to complete the rule analysis. The results of rule analysis show that the most relevant factors affecting students’ interest in literature curriculum mainly include the space-time dimension of textbook selection and compilation, the processing method of selected reading, and the evaluation method. The reading content is effectively processed by using the counterpoint reading method, and the literature curriculum textbooks are compiled from the perspective of cross-cultural communication, to enhance students’ interest in the literature curriculum.

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Section: Introductionmentioning

confidence: 99%

Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

Shi

Zhu

2022

Computational Intelligence and Neuroscience

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“…Honeycomb and Hexagonal networks [1] have been widely studied in various research domains, such as wireless sensor networks, wireless networks [2][3][4][5][6] and cellular networks, in order to study and analyze various issues like routing, [7][8][9] location management and target tracking [10][11][12], energy conservation [13][14][15], and interference estimation [16]. These networks and application domains are applicable to the IoT as well [17][18][19][20][21][22][23][24]. The concept of metric basis and metric dimension for the purpose of robot navigation within a graph-structured framework was initially studied in [25].…”

Section: Introductionmentioning

confidence: 99%

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Abbas¹,

Raza²,

Siddiqui³

et al. 2022

Computers, Materials &Amp; Continua

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Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G, which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC(n), and the hexagonal network HX (n) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN1(n) are also proposed.

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“…The Internet of Things (IoT) can be seen as the primary bridge that connects physical and digital world. IoT has become one of the significant and attractive field of research through which researchers monitor everyday usages via the Internet (Khedr et al, 2020). Integrating computational abilities in various kinds of things and living organisms can create big leap in many sectors such as health, military, home, entertainment etc (Palopoli, Passerone & Rizano, 2011).…”

Section: Introductionmentioning

confidence: 99%

Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

Osamy

Aziz

Khedr

2021

PeerJ Computer Science

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Data acquisition problem in large-scale distributed Wireless Sensor Networks (WSNs) is one of the main issues that hinder the evolution of Internet of Things (IoT) technology. Recently, combination of Compressive Sensing (CS) and routing protocols has attracted much attention. An open question in this approach is how to integrate these techniques effectively for specific tasks. In this paper, we introduce an effective deterministic clustering based CS scheme (DCCS) for fog-supported heterogeneous WSNs to handle the data acquisition problem. DCCS employs the concept of fog computing, reduces total overhead and computational cost needed to self-organize sensor network by using a simple approach, and then uses CS at each sensor node to minimize the overall energy expenditure and prolong the IoT network lifetime. Additionally, the proposed scheme includes an effective algorithm for CS reconstruction called Random Selection Matching Pursuit (RSMP) to enhance the recovery process at the base station (BS) side with a complete scenario using CS. RSMP adds random selection process during the forward step to give opportunity for more columns to be selected as an estimated solution in each iteration. The results of simulation prove that the proposed technique succeeds to minimize the overall network power expenditure, prolong the network lifetime and provide better performance in CS data reconstruction.

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A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

Cited by 12 publications

References 48 publications

Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

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