Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest

Lv, Qiujian; Qiao, Yuanyuan; Ansari, Nirwan; Jun, Liu; Yang, Jie

doi:10.1109/tvt.2016.2611654

Cited by 118 publications

(57 citation statements)

References 40 publications

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“…Although no existing work target proactive CoMP clustering leveraging Big Data explicitly yet, there exist certain works, wherein dynamic CoMP clustering is performed targeted at hotspots, assuming hotspot location are already somehow known by the network. The Big Data processing framework presented above cannot only identify the future hotspots but it can also predict future load, e.g using data of mobility traces and past CDR records [107], [116], [117]. Once a hotspot is characterized, the appropriate CoMP algorithm can be leveraged to cope with high capacity demands for hotspots.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

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Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

158

109

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Abstract-Mobile data traffic grew by 74% in 2015 and it's expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

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Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

“…Similarly, in [116], [117], [123]- [125], Big Data technologies and analytical algorithms have been used for predicting hotspot formation or forecasting pedestrian destinations with satisfactory accuracy.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

158

109

View full text Add to dashboard Cite

show abstract

“…For example, [13] uses a real dataset consisting of 4.9 million trajectories (790 million GPS points) as a population, but only small subsets having a maximum 30, 000 trajectories are used in their experiments. To the best of our knowledge, the largest real dataset used was in [4], consisting of 37 million GPS points. They utilized [4] the MapReduce model in their implementation to handle large datasets.…”

Section: Clustering Based Approachesmentioning

confidence: 99%

“…Therefore, most TP methods demonstrated in the literature use synthetic or small to medium size real trajectory datasets. To the best of our knowledge, the largest real dataset was used in [4], consisting of 370 million GPS points. These authors utilized a parallel processing model, MapReduce, in their implementation to handle large datasets.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Framework for Trajectory Prediction

Rathore

Kumar

Rajasegarar

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing this data employing spatio-temporal techniques for trajectory prediction in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network. Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviours in input trajectory data.In our experiments, we compare our proposed approach with a mixed Markov model (MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for both short-and long-term trajectory predictions. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15, 061 taxis in Singapore over a period of one month. Experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short-and long-term prediction performances, based on prediction accuracy and distance error (in km).

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“…Moreover, through defining a measure of entropy, Song et al [150] believe that 93% of individual movements are potentially predictable. Thus, various models have been applied to describe the human offline mobility behavior [151]. Passively collecting human mobile traffic data while users are accessing the mobile Internet has many advantages like low energy consumption.…”

Section: Analyses Of Human Online and Offline Behavior Based On Mobilmentioning

confidence: 99%

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

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest

Cited by 118 publications

References 40 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

A Scalable Framework for Trajectory Prediction

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

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