A probabilistic approach to user mobility prediction for wireless services

Stynes, David; Brown, Kenneth N.; Sreenan, Cormac J.

doi:10.1109/iwcmc.2016.7577044

Cited by 16 publications

(4 citation statements)

References 10 publications

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“…It is noteworthy that physical device changes between the offline and online phases can affect positioning accuracy, as well as the choice of algorithms and their parameters in the online phase. In the positioning literature, machine learning algorithms have wide use in estimating these positions [29][30][31][32][33]. There are several algorithms suggested in this application, and it is a non-trivial task to find the one that best behaves in a given scenario.…”

Section: Characterization Of User Mobilitymentioning

confidence: 99%

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

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Section: Characterization Of User Mobilitymentioning

confidence: 99%

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Medeiros

Neto

Lopez

et al. 2020

J Internet Serv Appl

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“…Most of work on predicting the movement as well as direction in vehicles are done using the methods Markov Chain [15] or probability distributions [16]. Here we present a simple linear model which gives high accuracy in predicting their locations.…”

Section: Model For Connected Carmentioning

confidence: 99%

Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

Naik

2020

SCPE

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Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than some common algorithms such as active monitoring, weighted round robin and throttled load balancer.

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“…Mobility prediction-based approaches show promising solutions mainly because human mobility behaviour is far from random and is highly influenced by their historical behaviour. Although mobility prediction can determine the future location of users, improve the handover process, and manage the resources efficiently [4], the predictions only focus on the homogeneous network. In addition, the proposed algorithm has not been implemented to prove the concept of mobility prediction in helping the handover process, especially for both homogeneous networks and heterogeneous networks.…”

Section: Introductionmentioning

confidence: 99%

Enhanced handover mechanism using mobility prediction in wireless networks

2020

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The rapid increase in the usage of the mobile internet has led to a great expansion of cellular data networks in order to provide better quality of service. However, the cost to expand the cellular network is high. One of the solutions to provide affordable wireless connectivity is the deployment of a WiFi access point to offload users' data usage. Nevertheless, the frequent and inefficient handover process between the WiFi AP and cellular network, especially when the mobile device is on the go, may degrade the network performance. Mobile devices do not have the intelligence to select the optimal network to enhance the quality of service (QoS). This paper presents an enhanced handover mechanism using mobility prediction (eHMP) to assist mobile devices in the handover process so that users can experience seamless connectivity. eHMP is tested in two wireless architectures, homogeneous and heterogeneous networks. The network performance significantly improved when eHMP is used in a homogeneous network, where the network throughput increases by 106% and the rate of retransmission decreases by 85%. When eHMP is used in a heterogeneous network, the network throughput increases by 55% and the retransmission rate decreases by 75%. The findings presented in this paper reveal that mobility prediction coupled with the multipath protocol can improve the QoS for mobile devices. These results will contribute to a better understanding of how the network service provider can offload traffic to the WiFi network without experiencing performance degradation. OPEN ACCESSCitation: Yap K-L, Chong Y-W, Liu W (2020) Enhanced handover mechanism using mobility prediction in wireless networks. PLoS ONE 15(1): e0227982. https://doi.org/10.

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A probabilistic approach to user mobility prediction for wireless services

Cited by 16 publications

References 10 publications

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

Enhanced handover mechanism using mobility prediction in wireless networks

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