Survey of wireless big data

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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“…General BDA [4] [5] [6] Wireless Sensor Networks (WSN) [7] Mobile Communication Networks [8] [9] [10] [11] Vehicular networks…”

Section: Research Issuesmentioning

confidence: 99%

“…Internet of Things (IoT) [11] 2) Variety. The type and nature of the data (structured, semistructured, unstructured, text and multimedia); 3) Velocity.…”

Section: Mobile Social Networkmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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“…Some attempts have been made to apply big data analytics to the area of wireless communications. Authors in [20] separated existing researches in wireless big data into data, transmission, network, and application layers. Wireless channel modeling, which is the foundation of wireless communications, is related to transmission layer.…”

Section: Introductionmentioning

confidence: 99%

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Huang

Wang

Bai

et al. 2020

IEEE Trans. Big Data

100

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The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

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“…To achieve this goal, Sezer et al [8] present open issues and provide an insight on IoT for big data. Furthermore, a wireless big data system that deals with massive data in wireless networks is described in [9]. Instead of describing the entire system, the authors focused on wireless infrastructure such as data-aided transmission, data-driven network optimization, and novel applications under layered architecture (as shown in application, network, transmission, and data layers in Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks for Big Data Systems

Kim

Shah

et al. 2019

Sensors

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Before discovering meaningful knowledge from big data systems, it is first necessary to build a data-gathering infrastructure. Among many feasible data sources, wireless sensor networks (WSNs) are rich big data sources: a large amount of data is generated by various sensor nodes in large-scale networks. However, unlike typical wireless networks, WSNs have serious deficiencies in terms of data reliability and communication owing to the limited capabilities of the nodes. Moreover, a considerable amount of sensed data are of no interest, meaningless, and redundant when a large number of sensor nodes is densely deployed. Many studies address the existing problems and propose methods to overcome the limitations when constructing big data systems with WSN. However, a published paper that provides deep insight into this research area remains lacking. To address this gap in the literature, we present a comprehensive survey that investigates state-of-the-art research work on introducing WSN in big data systems. Potential applications and technical challenges of networks and infrastructure are presented and explained in accordance with the research areas and objectives. Finally, open issues are presented to discuss promising directions for further research.

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Survey of wireless big data

Cited by 41 publications

References 61 publications

Big Data Analytics for Large-scale Wireless Networks

Big Data Analytics for Large-scale Wireless Networks

A Big Data Enabled Channel Model for 5G Wireless Communication Systems

Wireless Sensor Networks for Big Data Systems

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