Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

Kibria, Mirza Golam; Nguyen, Kien; Villardi, Gabriel Porto; Zhao, Ou; Ishizu, Koji; Kojima, Fumihide

doi:10.1109/access.2018.2837692

Cited by 403 publications

(218 citation statements)

References 13 publications

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“…Our measurement scale for AIC had four measures: (1) we have the capability to simulate human intelligence behavior in making predictions of customer decisions (developed based on conceptualization in [21,34]); (2) we have the capability to develop human-inspired algorithms to Sustainability 2020, 12, 949 9 of 23 predict customer behavior (adapted from [21]); (3) we have the capability to develop devices to replicate human intelligence and other cognitive functions (adapted from [40]); (4) we have the capability to develop artificial intelligence for learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction (new scale developed based on filed research). For responses on this scale, 0 = no capability and 10 = very high level of capability.…”

Section: Measurement Scale Developmentmentioning

confidence: 99%

“…Fourth, while past research on artificial intelligence (AI) has focused on computational tools, algorithms, and technical aspects of AI [21,30,40], we transformed these technical functions to capability perspective, developed, and validated the AIC scale with data from China and the United States. In addition, Hao et al [1] called for future research to include AIC.…”

Section: Theoretical Contributionsmentioning

confidence: 99%

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Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability

Zhang

Song

2020

Sustainability

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There has been increased interest in studying how big data analytics capability (BDAC) and artificial intelligence capability (AIC) lead to sustainable innovation and performance. Yet, few studies have investigated how these two emerging capabilities affect the success of sustainability development projects through the mediating effects of the sustainability design and commercialization processes. Based on Day and Wensley’s theoretical framework for diagnosing competitive superiority, we propose a research model to investigate how sustainability design and commercialization mediate the relationships between two emerging capabilities and sustainable growth and performance. To test the proposed research model, we collected empirical data from 905 sustainability development projects from China and the United States. This study makes theoretical and managerial contributions to sustainable development theory. The study findings reveal several interesting results. First, BDAC and AIC not only increase the proficiency of sustainability design and commercialization but also directly enhance sustainable growth and performance. Second, sustainability design and commercialization mediate the positive effects of BDAC and AIC on sustainable growth and performance. Finally, the empirical analyses uncovered several cross-national differences. For sustainability design, BDAC is more important than AIC in the United States, while AIC is more important than BDAC in China.

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Section: Measurement Scale Developmentmentioning

confidence: 99%

Section: Theoretical Contributionsmentioning

confidence: 99%

Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability

Zhang

Song

2020

Sustainability

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“…The optimization problem usually concerns maximizing or minimizing a certain outcomes while satisfying given constraints. The typical optimal outcomes in wireless networks include throughput, delay, outage, energy consumption, operation cost and QoS [10]. • Decision making.…”

Section: B Common Data Analytics Methodsmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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“…In this way, the information gathered by these processes is accelerated to achieve intelligence and efficiency in managing urban resources and settings [27][28][29]. Collectively, ICT-based predictive analytics can demonstrate the best implements for gaining insight into data for future decisions [30,31], and enhance the outcomes for other stakeholders in the smart city area [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of a Smart City’s Trends in Urban Planning before and after 2016 through Keyword Network Analysis

2019

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The aim of this study was to explore the keywords related to smart city concepts, and to understand their flow. This research used a keyword network analysis by collecting keywords from papers published on the web from Scopus, which is an international scholarly papers engine. The data were collected from before and after 2016, and since the amount of data has been growing rapidly after global agreements such as the United Nations' Sustainable Development Goals (SDGs) in 2015, we attempted to focus on adjacent years of publication. In order to understand the flow of research, we conducted a central analysis, which is widely used in quantitative research relating to social network analysis, and performed cluster analysis to identify relationships with related research. The results of the analysis are represented in the form of network maps, and the role of each keyword was clarified based on these network maps. In addition, the overall flow explained the change of flow through discarded and emerging keywords, and the relationships with related fields were explained through cluster analysis. The findings could serve as a basis for policymakers, urban managers, and researchers seeking a comprehensive understanding of the smart city concept in urban planning areas.Sustainability 2019, 11, 3155 2 of 25 adaption to environmental issues such as climate change and global warming, and efficiently utilizing and managing energy. In addition, Information and Communication Technologies (hereafter ICT) will extensively and effectively help cities achieve a comparative edge [4,8,9], and be used as the tools and means to develop Intelligent Transport Systems (hereafter ITS) with mobility information and the Internet of Things (hereafter IoT) [10][11][12], as well as to achieve urban policy making based on governance and open data [13][14][15]. Accordingly, in improving the urban quality of future urban areas, the term smart city is considered as an umbrella concept that includes various sub-concepts such as sustainable smart environments, smart technology, smart energy, smart transportation, smart mobility, and smart government [16][17][18][19][20][21].

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Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks

Cited by 403 publications

References 13 publications

Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability

Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability

Big Data Analytics for Large-scale Wireless Networks

A Comparison of a Smart City’s Trends in Urban Planning before and after 2016 through Keyword Network Analysis

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