A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Xie, Jiyang; Song, Zeyu; Li, Yupeng; Zhang, Yanting; Yu, Hong; Zhan, Jinnan; Ma, Zhanyu; Qiao, Yuanyuan; Zhang, Jianhua; Guo, Jun

doi:10.1155/2018/8738613

Cited by 40 publications

(18 citation statements)

References 151 publications

(183 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These effects are well-known in the literature as over- and under-fitting of the dataset, and are the two biggest causes for poor performance of machine-learning algorithms [ 44 ]. The task of a good machine-learning model is to generalize well from the training data to any data from the problem domain.…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

Sousa

Thomä

2018

Sensors

View full text Add to dashboard Cite

A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

Sousa

Thomä

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, 5G can provide interaction between IoT devices and the smart environment through smart sensors connected. [64]. The evolution of IoT automatically leads to the evolution of IoV [119].…”

Section: G Applicationsmentioning

confidence: 99%

“…-IoV challenges: Ensuring a good method of communication between the drivers and the IoV system [64,120] • Healthcare: with the evolution of 5G and the improvement in detection and security technologies, will be lightly monitored by opening up healthcare solutions.…”

Section: G Applicationsmentioning

confidence: 99%

A survey of 5G network systems: challenges and machine learning approaches

Mâaloul

Fourati

2020

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless networks then we give a deep insight into a set of 5G challenges and research opportunities for machine learning (ML) techniques to manage these challenges. The first part of the paper is devoted to overview the fifth-generation of cellular networks, explaining its requirements as well as its key technologies, their challenges and its forthcoming architecture. The second part is devoted to present a basic overview of ML techniques that are nowadays applied to cellular networks. The last part discusses the most important related works which propose ML solutions in order to overcome 5G challenges.

show abstract

“…The execution in distributed exascale computing system the variety of Big data causes a problem. (Xie, J., Song, Z., Li, Y., et al (2018)…”

Section: Big Data Anatomy and Dynamic And Interactive Naturementioning

confidence: 99%

Challenges of Using Big Data in Distributed Exascale Systems

Tahmazli-Khaligova¹,

Oil²

2020

AzJHPC

View full text Add to dashboard Cite

In a traditional High Performance Computing system, it is possible to process a huge data volume. The nature of events in classic High Performance computing is static. However, distributed exascale system has a different nature. The processing big data in a distributed exascale system evokes a new challenge. The dynamic and interactive character of a distributed exascale system changes process’s status and system elements. This paper discusses the challenge of the big data attributes: volume, velocity, variety; how they influence distributed exascale system dynamic and interactive nature. While investigating the effect of the dynamic and interactive nature of exascale systems in computing big data, this research suggests the Markov chains model. This model constructs the transition matrix, which identifies system status and memory sharing. It lets us analyze convergence of the two systems. As a result both systems are explored by the influence of each other.

show abstract

A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications

Cited by 40 publications

References 151 publications

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

A survey of 5G network systems: challenges and machine learning approaches

Challenges of Using Big Data in Distributed Exascale Systems

Contact Info

Product

Resources

About