An improved Markov method for prediction of user mobility

Cheng, Yihang; Qiao, Yuanyuan; Yang, Jie

doi:10.1109/cnsm.2016.7818454

Cited by 20 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is not suitable for scenarios with long-term dependencies [132]. Examples of IoT use-cases that have employed HMM include anomaly detection [133], physical activity recognition [134], traffic control management [135], health monitoring [136], prediction of user mobility [137], detection of sitting posture activities [138], etc.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

View full text Add to dashboard Cite

The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

show abstract

Section: Time Series Forecastingmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

View full text Add to dashboard Cite

show abstract

“…K-means is one of existing cluster partition algorithms. In this algorithm, the partition that has data considered as cluster k. Other clustering algorithms are also proposed to handle document grouping tasks for automatic grouping and enhanced partition of K-means algorithm, such as a method for initializing centroid [6], [7], oncology-based K-means algorithm, domain ontological grouping [8], and dataset based analysis to increase the efficiency of the K-means algorithm in case that the false document is given as input [9].…”

Section: A Theoretical Backgroundmentioning

confidence: 99%

Document Similarity Detection using K-Means and Cosine Distance

Usino¹,

Prabuwono²,

Allehaibi³

et al. 2019

ijacsa

View full text Add to dashboard Cite

A two-year study by the Ministry of Research, Technology and Education in Indonesia presented the evaluation of most universities in Indonesia. The findings of the evaluation are the peculiarities of various dissertation softcopies of doctoral students which are similar to any texts available on internet. The suspected plagiarism behavior has a negative effect on both students and faculty members. The main reason behind this behavior is the lack of standardized awareness among faculty members with regard to plagiarism. Therefore, this study proposes a computerized system that is able to detect plagiarism information by using K-means and cosine distance algorithm. The process starts from preprocessing process that includes a novel step of checking Indonesian big dictionary, vector space model design, and the combined calculation of K-means and cosine distance from 17 documents as test data. The result of this study generally shows that the documents have detection accuracy of 93.33%.

show abstract

“…In addition, there is a need for location-based services to provide high-quality services to users. In order to address the issues, user mobility prediction has been studied [ 13 , 14 , 15 ]. In [ 13 ], an improved Markov algorithm is proposed to predict the user’s next location from the trajectory.…”

Section: Related Studiesmentioning

confidence: 99%

“…In order to address the issues, user mobility prediction has been studied [ 13 , 14 , 15 ]. In [ 13 ], an improved Markov algorithm is proposed to predict the user’s next location from the trajectory. It predicts the user’s next location using the history of the user’s trajectory consisting of time-interval-representative location pairs.…”

Section: Related Studiesmentioning

confidence: 99%

Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

Park

Lim

2018

Sensors

View full text Add to dashboard Cite

In 5G networks, heterogeneous cloud radio access network (H-CRAN) is considered a promising future architecture to minimize energy consumption and efficiently allocate resources. However, with the increase in the number of users, studies are performed to overcome the energy consumption problems. In this study, we propose a power control algorithm with mobility prediction to provide a high-energy efficiency for 5G H-CRAN. In particular, the proposed algorithm predicts UE mobility in vehicular mobility scenarios and performs remote radio head (RRH) switching operations based on % prediction results. We formulate an optimization problem to maximize the energy efficiency while satisfying the outage probability requirement. We then propose an RRH switching operation based on Markov mobility prediction and optimize the transmission power based on a gradient method. Simulation results demonstrate the improved energy efficiency compared with those of existing RRH switching-operation algorithms.

show abstract

An improved Markov method for prediction of user mobility

Cited by 20 publications

References 18 publications

Data Analytics in the Internet of Things: A Survey

Data Analytics in the Internet of Things: A Survey

Document Similarity Detection using K-Means and Cosine Distance

Energy-Effective Power Control Algorithm with Mobility Prediction for 5G Heterogeneous Cloud Radio Access Network

Contact Info

Product

Resources

About