Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

Khorramnejad, Kaveh; Ferdouse, Lilatul; Guan, Ling; Anpalagan, Alagan

doi:10.1186/s13677-018-0115-6

Cited by 12 publications

(12 citation statements)

References 13 publications

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“…During fault location, firstly traverse the cluster center node of each cluster. When the information is obtained from the cluster center node, there is abnormal node state in the cluster; then, traverse all nodes in the cluster, and then identify the location of the fault node and isolate the fault area, and synchronously transfer the fault information to other cluster center nodes [29][30][31][32]. When the clustering center satisfies the convergence condition of the semi-supervised learning, the detection statistic of the characteristic information of the large-data value of the communication satisfies the clustering convergence condition, and the implementation process of the large-data fuzzy weighted clustering algorithm designed in this paper is obtained, as shown in Figure 6.…”

Section: Big Data Fuzzy Weighted Clustering Optimizationmentioning

confidence: 99%

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

2020

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It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Cloud computing and adaptive quantitative recurrent classifiers were used for data classification, and block template matching and multi-sensor information fusion were combined to search the clustering center automatically to improve the convergence of clustering. The simulation results show that, after the application of this method, the information fusion performance of the clustering process was better, the automatic searching ability of the data clustering center was stronger, the frequency domain equalization control effect was good, the bit error rate was low, the energy consumption was small, and the ability of fuzzy weighted clustering retrieval of numerical attributes of communication big data was effectively improved.

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Section: Big Data Fuzzy Weighted Clustering Optimizationmentioning

confidence: 99%

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

2020

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“…There are some proposals that apply these techniques to multimedia processing tasks to improve their flexibility and performance [58], [59]. Some of the most common applications include solutions that broadcast both image [60], [61] and video data [62], [63] among acquisition devices and cloud servers.…”

Section: B Multimedia Architecturesmentioning

confidence: 99%

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

et al. 2019

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New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city's multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments. INDEX TERMS Mobile cloud computing, data processing, distributed architectures, smart city, urban computing.

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“…If the match is not successful, the rule can get the abnormal accurately, and the chromosome will get a reward. To evaluate the accuracy of the new rule, we need to recognize the vehicle load data in the actual wireless sensor network environment [12][13][14][15][16][17][18][19].…”

Section: Accurate Recognition Of Abnormal Wireless Sensor Network Vehmentioning

confidence: 99%

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

Niknamian¹

2019

AJTTE

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Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.

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Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

Cited by 12 publications

References 13 publications

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

Abnormal Vehicle Load Identification Method Based on Genetic Algorithm and Wireless Sensor Network

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