Papers by Mostafa Mirzaie
2020 28th Iranian Conference on Electrical Engineering (ICEE), 2020
In this study, an adaptive fuzzy logic based algorithm for clustering heterogeneous sensors is pr... more In this study, an adaptive fuzzy logic based algorithm for clustering heterogeneous sensors is proposed (AFLCH) which considers the environmental conditions of each sensor to select the best candidates as cluster centers. The proposed method uses three different clustering algorithms, different clustering parameters and adaptive threshold in order to control the number of total messages inside the network to increase the life of the sensors as well as the life of the network. AFLCH is compared to other methods using criteria such as the first node dies (FND), total residual energy (TRE), half node die (HND), the total number of the dead sensors and the last sensor dies (LND). The results indicate that AFLCH is able to control more energy and also increase network lifetime compared to other methods by decreasing the number of received and sent messages.
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arXiv: Databases, 2019
One of the most significant problems of Big Data is to extract knowledge through the huge amount ... more One of the most significant problems of Big Data is to extract knowledge through the huge amount of data. The usefulness of the extracted information depends strongly on data quality. In addition to the importance, data quality has recently been taken into consideration by the big data community and there is not any comprehensive review conducted in this area. Therefore, the purpose of this study is to review and present the state of the art on the quality of big data research through a hierarchical framework. The dimensions of the proposed framework cover various aspects in the quality assessment of Big Data including 1) the processing types of big data, i.e. stream, batch, and hybrid, 2) the main task, and 3) the method used to conduct the task. We compare and critically review all of the studies reported during the last ten years through our proposed framework to identify which of the available data quality assessment methods have been successfully adopted by the big data communi...
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ArXiv, 2019
One of the most significant problems of Big Data is to extract knowledge through the huge amount ... more One of the most significant problems of Big Data is to extract knowledge through the huge amount of data. The usefulness of the extracted information depends strongly on data quality. In addition to the importance, data quality has recently been taken into consideration by the big data community and there is not any comprehensive review conducted in this area. Therefore, the purpose of this study is to review and present the state of the art on the quality of big data research through a hierarchical framework. The dimensions of the proposed framework cover various aspects in the quality assessment of Big Data including 1) the processing types of big data, i.e. stream, batch, and hybrid, 2) the main task, and 3) the method used to conduct the task. We compare and critically review all of the studies reported during the last ten years through our proposed framework to identify which of the available data quality assessment methods have been successfully adopted by the big data communi...
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With the advent of big data applications and the increasing amount of data being produced in thes... more With the advent of big data applications and the increasing amount of data being produced in these applications, the importance of efficient methods for big data analysis has become highly evident. However, the success of any such method will be hindered should the data lacks the required quality. Big data quality assessment is therefore a major requirement for any organization or business that use big data analytics for its decision making. On the other hand, using contextual information is advantageous in many analysis tasks in various domains, e.g. user behavior analysis in the social networks. However, the big data quality assessment has benefited less from this potential. There is a vast variety of data sources in the big data domain that can be utilized to improve the quality evaluation of big data. Including contextual information provided by these sources into the big data quality assessment process is an emerging trend towards more advanced techniques aimed at enhancing the...
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Wireless Networks
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Alexandria Engineering Journal
Abstract Fuzzy Multi Cluster-Based Routing with a Constant Threshold (FMCR-CT) is presented as a ... more Abstract Fuzzy Multi Cluster-Based Routing with a Constant Threshold (FMCR-CT) is presented as a solution to help save more energy in wireless sensor networks. Most of the algorithms introduced so far are based on clustering in each round and single-hop transmission of data to the base station. Clustering in each round increases the number of control messages and the possibility of collision. FMCR-CT is innovative in several areas which includes avoid performing clustering in each round, introducing fixed threshold, using different algorithms to do the clustering and utilizing multi-hop routing by considering a suitable middle node to send data from each cluster to the base station. The main aim of the study is to propose an approach in which improves the wireless sensor network lifetime by lessening cluster head selections and transmitted messages in each round. Remaining energy, number of nodes and distance of each node are considered as fuzzy criteria to select the cluster head. FMCR-CT has been compared to other algorithms in terms of parameters such as network lifetime, dead nodes in each round, the first node die, half node die and the last node die. The results from simulation reveal that FMCR-CT could outperform other methods.
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Wireless Personal Communications
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Wireless Networks, 2017
In this study, a multi-clustering algorithm based on fuzzy logic (MCFL) with an entirely differen... more In this study, a multi-clustering algorithm based on fuzzy logic (MCFL) with an entirely different approach is presented to carry out node clustering in wsn. This approach minimizes energy dissipation and, consequently, prolongs network lifetime. In the past, numerous algorithms were tasked with clustering nodes in wireless sensors networks. The common denominator of all these approaches is the constancy of the algorithm in all the rounds of network lifetime that causes the selection of cluster heads in each round. Selecting cluster heads in each round indicates that throughout the process the most eligible nodes are not selected. By comparing the chance of each node to be selected as a cluster head using a random number, the majority of these clustering approaches, both fuzzy and non-fuzzy, destroy the chance of selecting the most eligible node as cluster head. As a result, all these approaches require the selection of cluster heads in each round. Performing selections in each round increases the rate of sent and received messages. By increasing the number of messages, the total number of sent messages in the network increases too. Therefore, in a network with a high number of nodes, any increase in the number of packets will augment network traffic and increase the collision probability. On the other hand, since nodes lose a certain amount of energy for each sent message, by increasing the number of messages, nodes’ energy will correspondingly decrease which results in their premature death. However, by selecting the most eligible nodes as cluster heads and trusting them for at least a few rounds, the amount of sent and received messages is reduced. In this article, In addition to clustering nodes in different rounds using different clustering algorithms, MCFL avoids selecting new cluster heads by trusting previous cluster heads leading to a reduction in the number of messages and saving energy. MCFL is compared with other approaches in three different scenarios using indices such as total remaining energy, the number of dead nodes, first node dies, half of nodes die, and last node dies. Results reveal that MCFL has as advantage over other approaches.
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Computer Communications
Abstract In the past, numerous algorithms were tasked with clustering nodes in wireless sensors n... more Abstract In the past, numerous algorithms were tasked with clustering nodes in wireless sensors networks. Each of these algorithms has its own advantages and disadvantages. The common denominator of all these approaches is the constancy of the algorithm in all the rounds of network lifetime that causes the selection of cluster heads in each round. Failing to select the best nodes as cluster heads leads to holding elections in each round. By comparing the chance of each node to be selected as a cluster head using a random number, the majority of these clustering approaches, both fuzzy and non-fuzzy, destroy the chance of selecting the most eligible node as cluster head. As a result, all these approaches require the selection of cluster heads in each round. Selecting cluster heads in each round increases the amount of received and sent messages such that in networks with large number of nodes, it causes some problems such as energy reduction, collision increase, and network traffic. However, by selecting the most eligible nodes as cluster heads and trusting them for at least a few rounds, the amount of sent and received messages is reduced. In this article, an adaptive multiclustering algorithm using fuzzy logic in wireless sensor network (Adaptive MCFL) is presented. In addition to clustering nodes in different rounds using different clustering algorithms, the proposed algorithm avoids selecting new cluster heads by trusting previous cluster heads leading to a reduction in the number of messages and saving energy. The proposed approach is compared with other approaches in three different scenarios using indices such as remaining energy, the number of dead nodes, first node dies (FND), half of nodes die (HND), and last node dies (LND). Results reveal that Adaptive MCFL has as advantage over other approaches.
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Cumhuriyet Dental Journal
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Papers by Mostafa Mirzaie